diff --git a/.github/workflows/mypy.yml b/.github/workflows/mypy.yml index c09ca44c..8f6f5409 100644 --- a/.github/workflows/mypy.yml +++ b/.github/workflows/mypy.yml @@ -20,4 +20,4 @@ jobs: - name: Type checking with MyPy run: | pip install mypy - mypy malsim tests --install-types --non-interactive + mypy python/malsim tests --install-types --non-interactive diff --git a/.github/workflows/publish-to-pypi-and-test-pypi.yml b/.github/workflows/publish-to-pypi-and-test-pypi.yml index b30b9e54..076b9b88 100644 --- a/.github/workflows/publish-to-pypi-and-test-pypi.yml +++ b/.github/workflows/publish-to-pypi-and-test-pypi.yml @@ -3,72 +3,212 @@ name: Publish Python distributions to PyPI and TestPyPI on: push jobs: - build: - name: Build Python distribution - runs-on: ubuntu-latest + linux: + name: wheels (linux ${{ matrix.platform.target }}) + runs-on: ${{ matrix.platform.runner }} + strategy: + fail-fast: false + matrix: + platform: + - runner: ubuntu-22.04 + target: x86_64 + - runner: ubuntu-22.04 + target: aarch64 + steps: + - uses: actions/checkout@v6 + - uses: actions/setup-python@v6 + with: + python-version: "3.10" + - name: Build wheels + uses: PyO3/maturin-action@v1 + with: + target: ${{ matrix.platform.target }} + args: --release --out dist --find-interpreter + sccache: ${{ !startsWith(github.ref, 'refs/tags/') }} + manylinux: auto + - name: Test wheel (native target only) + if: matrix.platform.target == 'x86_64' + env: + # The maturin-action build step above leaves RUSTC_WRAPPER=sccache + # in the job environment, but that sccache binary only exists + # inside its manylinux container. This step runs on the host and + # rebuilds mal-toolbox (a git dependency) from source via cargo, + # so the leaked wrapper must be cleared or the build fails. + RUSTC_WRAPPER: "" + SCCACHE_GHA_ENABLED: "" + run: | + pip install mal-simulator --find-links dist --force-reinstall + python -c "import malsim" + - name: Upload wheels + uses: actions/upload-artifact@v6 + with: + name: wheels-linux-${{ matrix.platform.target }} + path: dist + + musllinux: + name: wheels (musllinux ${{ matrix.platform.target }}) + runs-on: ${{ matrix.platform.runner }} + strategy: + fail-fast: false + matrix: + platform: + - runner: ubuntu-22.04 + target: x86_64 + - runner: ubuntu-22.04 + target: aarch64 + steps: + - uses: actions/checkout@v6 + - uses: actions/setup-python@v6 + with: + python-version: "3.10" + - name: Build wheels + uses: PyO3/maturin-action@v1 + with: + target: ${{ matrix.platform.target }} + args: --release --out dist --find-interpreter + sccache: ${{ !startsWith(github.ref, 'refs/tags/') }} + manylinux: musllinux_1_2 + - name: Upload wheels + uses: actions/upload-artifact@v6 + with: + name: wheels-musllinux-${{ matrix.platform.target }} + path: dist - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: "3.x" + windows: + name: wheels (windows ${{ matrix.platform.target }}) + runs-on: windows-latest + strategy: + fail-fast: false + matrix: + platform: + - target: x64 + python_arch: x64 + steps: + - uses: actions/checkout@v6 + - uses: actions/setup-python@v6 + with: + python-version: "3.10" + architecture: ${{ matrix.platform.python_arch }} + - name: Build wheels + uses: PyO3/maturin-action@v1 + with: + target: ${{ matrix.platform.target }} + args: --release --out dist --find-interpreter + sccache: ${{ !startsWith(github.ref, 'refs/tags/') }} + - name: Test wheel + run: | + pip install mal-simulator --find-links dist --force-reinstall + python -c "import malsim" + - name: Upload wheels + uses: actions/upload-artifact@v6 + with: + name: wheels-windows-${{ matrix.platform.target }} + path: dist - - name: Install pypa/build - run: >- - python3 -m - pip install - build - --user + macos: + name: wheels (macos ${{ matrix.platform.target }}) + runs-on: ${{ matrix.platform.runner }} + strategy: + fail-fast: false + matrix: + platform: + - runner: macos-15-intel + target: x86_64 + - runner: macos-latest + target: aarch64 + steps: + - uses: actions/checkout@v6 + - uses: actions/setup-python@v6 + with: + python-version: "3.10" + - name: Build wheels + uses: PyO3/maturin-action@v1 + with: + target: ${{ matrix.platform.target }} + args: --release --out dist --find-interpreter + sccache: ${{ !startsWith(github.ref, 'refs/tags/') }} + - name: Test wheel + run: | + pip install mal-simulator --find-links dist --force-reinstall + python -c "import malsim" + - name: Upload wheels + uses: actions/upload-artifact@v6 + with: + name: wheels-macos-${{ matrix.platform.target }} + path: dist - - name: Build a binary wheel and a source tarball - run: python3 -m build - - name: Store the distribution packages - uses: actions/upload-artifact@v4 - with: - name: python-package-distributions - path: dist/ + sdist: + name: sdist + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v6 + - name: Build sdist + uses: PyO3/maturin-action@v1 + with: + command: sdist + args: --out dist + - name: Upload sdist + uses: actions/upload-artifact@v6 + with: + name: wheels-sdist + path: dist - publish-to-pypi: - name: Publish Python distribution to PyPI - if: startsWith(github.ref, 'refs/tags/') # only publish to PyPI on tag pushes - needs: - - build - runs-on: ubuntu-latest - environment: - name: pypi - url: https://pypi.org/p/mal-simulator - permissions: - id-token: write # IMPORTANT: mandatory for trusted publishing + publish-to-testpypi: + name: Publish Python distribution to TestPyPI + if: startsWith(github.ref, 'refs/tags/') # only publish to PyPI on tag pushes + needs: + - linux + - musllinux + - windows + - macos + - sdist + runs-on: ubuntu-latest + environment: + name: testpypi + url: https://test.pypi.org/p/mal-simulator + permissions: + id-token: write # IMPORTANT: mandatory for trusted publishing - steps: + steps: - name: Download all the dists - uses: actions/download-artifact@v4 + uses: actions/download-artifact@v7 with: - name: python-package-distributions + pattern: wheels-* path: dist/ - - name: Publish distribution to PyPI + merge-multiple: true + - name: Publish distribution to TestPyPI uses: pypa/gh-action-pypi-publish@release/v1 + with: + repository-url: https://test.pypi.org/legacy/ - publish-to-testpypi: - name: Publish Python distribution to TestPyPI - if: startsWith(github.ref, 'refs/tags/') # only publish to PyPI on tag pushes - needs: - - build - runs-on: ubuntu-latest - environment: - name: testpypi - url: https://test.pypi.org/p/mal-simulator - permissions: - id-token: write # IMPORTANT: mandatory for trusted publishing + publish-to-pypi: + name: Publish Python distribution to PyPI + if: startsWith(github.ref, 'refs/tags/') # only publish to PyPI on tag pushes + needs: + - linux + - musllinux + - windows + - macos + - sdist + - publish-to-testpypi # TestPyPI is a gate: must succeed before the real publish + runs-on: ubuntu-latest + environment: + name: pypi + url: https://pypi.org/p/mal-simulator + permissions: + id-token: write # IMPORTANT: mandatory for trusted publishing + attestations: write # for the GitHub-native build provenance attestation below - steps: + steps: - name: Download all the dists - uses: actions/download-artifact@v4 + uses: actions/download-artifact@v7 with: - name: python-package-distributions + pattern: wheels-* path: dist/ - - name: Publish distribution to TestPyPI - uses: pypa/gh-action-pypi-publish@release/v1 + merge-multiple: true + - name: Generate artifact attestation + uses: actions/attest@v4 with: - repository-url: https://test.pypi.org/legacy/ + subject-path: "dist/*" + - name: Publish 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+version = "0.1.0" +edition = "2021" +license = "Apache-2.0" +repository = "https://github.com/mal-lang/mal-simulator" +publish = false + +[workspace.dependencies] +malsim-core = { path = "core/malsim-core" } + +maltoolbox-attackgraph = { git = "https://github.com/mal-lang/mal-toolbox", branch = "rust-rewrite" } +maltoolbox-language = { git = "https://github.com/mal-lang/mal-toolbox", branch = "rust-rewrite" } +maltoolbox-model = { git = "https://github.com/mal-lang/mal-toolbox", branch = "rust-rewrite" } diff --git a/PORTING_NOTES.md b/PORTING_NOTES.md new file mode 100644 index 00000000..469bcf44 --- /dev/null +++ b/PORTING_NOTES.md @@ -0,0 +1,1212 @@ +# Porting notes: moving malsim's simulator core to Rust + +Reference for the port of `MalSimulator`'s and `DynaMalSimulator`'s +hot-path logic from Python into the `core/malsim-core` / +`py-bindings/malsim-pyo3` Rust crates. The port is complete; this file +records the design, the standing conventions, and the places where the +Rust code deliberately differs from (or deliberately copies a quirk of) +the Python original. Section numbers are referenced from code comments +(`PORTING_NOTES.md §10` etc.), so keep them stable. + +## 0. Status + +All phases are done (last change 2026-10-12). One line per step, with +where it landed, is in §5 (Phase A, `MalSimulator`), §6 (Phase B, +`DynaMalSimulator`), §7 (Phase C, Rust-only library API + scenario +loading) and §13 (Phase D, Rust-only policies, rewards, `run_simulation` +and CLI). + +- [x] Phase 0 - Rust workspace scaffolding (`core/`, `py-bindings/`; two + separate Cargo workspaces). +- [x] Phase A - `MalSimulator` runs on native (A1-A11). +- [x] Phase B - `DynaMalSimulator` runs on native (B1-B7). The two B5 + bug classes code comments refer to as "§0 B5" (stale node ids + after a model effect, and the missing-TTC-value gap) are described + in §10 "Dyna path". +- [x] Phase C - `malsim-core` usable without Python, including scenario + loading (C1-C6). +- [x] Static and dynamic simulators split into `Simulator` / + `DynaSimulator` (§11). +- [x] Phase D - the Rust-only path has built-in policies, rewards, + `run_simulation` and a `malsim` binary (D1-D4, §13). The Python + path is untouched by it. + +Gates for any further change (all must stay clean): + +```bash +cargo test && cargo clippy -- -D warnings && cargo fmt --check # in the repo root +cargo test && cargo clippy -- -D warnings && cargo fmt --check # in py-bindings/ +uv run pytest tests examples/* # includes `integration` +uv run mypy python/malsim tests +uv run ruff check && uv run ruff format --check +``` + +If `tests/testdata/scenarios/**` or the scenario schema changes, +regenerate the parity golden file with `python -m tests.scenario_parity` +(§8). + +## 1. Goals and non-goals + +**Goals** (from the porting request): +- `MalSimulator` and `DynaMalSimulator`'s `reset()`/`step()` (and the + other public query methods on them) run on Rust code under the hood. +- `AttackerState`, `DefenderState`, `MalSimulatorState`, `Scenario`, + `AttackerSettings`/`DefenderSettings`/`MalSimulatorSettings`/ + `NodePropertyRule` stay real, hand-written Python classes - "mirrored + in Python" - not replaced by native pyclasses. Python users keep + today's types, `isinstance` checks, pickling, equality, etc. +- Non-breaking: the existing test suite (`tests/test_mal_simulator.py`, + `tests/test_dyna_mal_simulator.py`, `tests/test_scenario.py`, + `tests/test_ttc_utils.py`) is the compatibility contract and must stay + green. +- A Rust-only path exists: an embedding Rust program can build a + simulator and call `reset()`/`step()` without any Python involved. +- That Rust-only path also implements the same functionality as the + Python `Scenario` class: loading a scenario YAML file (language + + model + agent settings + sim settings, including `extends` merging) + into a ready-to-run graph/settings pair, not just stepping an + already-built graph. See §2.6 and §7. +- Every ported module gets Rust-native tests equivalent to the relevant + Python tests, where relevant (not just reliance on the Python suite + staying green) - see §8. +- `python/malsim/envs/`, `python/malsim/policies/`, and + `python/malsim/visualization/` are explicitly out of scope for porting + - left exactly as-is - and double as integration tests: if they still + work unmodified against the new native-backed `MalSimulator`, that's + strong evidence the public surface didn't shift under them. + +**Non-goals** (scoped out, confirmed via §2): +- Bit-identical reproducibility of seeded runs across the Python-RNG → + Rust-RNG transition (see §2.1). +- Touching the Python `Scenario` class itself (`scenario/scenario.py`) + - it stays exactly as-is for the Python-bound path, unchanged, per §4. + (Scope note, not a non-goal: a *separate*, additive Rust implementation + of the same scenario-loading functionality is in scope for Phase C - + see §2.6/§7 - since a Rust-only caller has no Python `Scenario` to call + into. This is new Rust code, not a port/replacement of + `scenario.py`.) +- Moving the Python path's reward computation (`rewards.py`) or + decision agents (`policies/`) onto native - see §2.4. Both stay pure + Python, unchanged, for Python callers. (Scope note, as for `Scenario` + above: Phase D added *separate* Rust implementations of both for the + Rust-only path - §13. The Python path never calls them.) + +## 2. Architectural decisions + +### 2.1 RNG / TTC reproducibility: statistically-equivalent only + +TTC sampling (`ttc_utils.py`), false-positive/negative generation +(`false_alerts.py`), detector true/false-positive rolls +(`event_logger.py`), and pre-enabled-defense Bernoulli draws all pull from +numpy's `default_rng` (PCG64), repeatedly per step. Reproducing numpy/ +scipy's *exact* output for a given seed in Rust is impractical in general: +Gamma/LogNormal sampling in scipy uses rejection algorithms that consume a +variable, data-dependent number of underlying draws per sample, not a +simple inverse-CDF - matching that bit-for-bit would mean reimplementing +scipy's internal C sampling algorithms exactly. + +**Decision:** after the port, the same seed may produce a +*different-but-statistically-equivalent* sequence of outcomes. This is a +one-time, documented compatibility break for anyone who recorded "golden" +seeded trajectories before the port - not an ongoing concern. It unblocks +porting all RNG-consuming sampling into Rust (via the `rand`/`statrs` +crates) for real speedups. + +**Consequence for tests:** a test that asserts an *exact* sampled value +or exact sequence of actions for a fixed seed is either relaxed (assert +statistical/structural properties instead) or pinned against the Rust +RNG's output (§8). + +### 2.2 Shared live graph and model with mal-toolbox's PyO3 layer + +Python's `maltoolbox.AttackGraph`/`Model` objects and `malsim-core` +operate on the *same* `Rc>`/`Rc>`: +zero-copy and always in sync, which `DynaMalSimulator` needs because the +graph and model mutate at runtime. + +**Mechanism: a `PyCapsule` handoff, not a pyclass downcast.** A PyO3 +pyclass from a shared `rlib` crate gets a separate, unrelated type object +in every `cdylib` that statically links it, so `malsim._native` can never +downcast an object created by `maltoolbox._native` (`TypeError: +'AttackGraph' object is not an instance of 'AttackGraph'`). Instead +mal-toolbox's `PyAttackGraph` and `PyModel` each expose +`__inner_capsule__()`, which returns a named `PyCapsule` +(`"maltoolbox._native.AttackGraph.inner"` / +`"maltoolbox._native.Model.inner"`) holding one `Rc::into_raw` clone that +the capsule's destructor drops. `malsim-pyo3`'s `extract_shared_graph`/ +`extract_shared_model` (`lib.rs`) check the name and rebuild the `Rc`. +The capsule name is the only cross-module contract, so `malsim-pyo3` +depends only on the pure `maltoolbox-attackgraph`/`maltoolbox-model` +crates, not on mal-toolbox's `-py` crates. + +**Any capsule consumer must call `Rc::increment_strong_count(ptr)` before +`Rc::from_raw(ptr)`.** The capsule owns exactly one strong reference; +`from_raw` alone steals it and it is dropped twice (a double free that a +single-call smoke test does not show, only a create/use/delete/ +`gc.collect()` loop does). + +**Consequence:** `malsim-core` (pure Rust, no PyO3) depends only on the +plain mal-toolbox crates. Its simulators always take +`Rc>` (and `Rc>`) as handle types, +also on the Rust-only path, which just owns the only reference - so there +is one code path, not two. + +### 2.3 Phasing + +`MalSimulator` (Phase A) was ported and stabilized before +`DynaMalSimulator` (Phase B), which reuses Phase A's FFI/mirroring +pattern. Both are done; code comments saying `DynaMalSimulator` "still +uses the pure-Python path" predate Phase B. + +### 2.4 NodePropertyRule DSL stays resolved in Python; rewards stay pure Python + +`NodePropertyRule` (`config/node_property_rule.py`) is a small per-node +DSL (match by asset name/type) used for `rewards`, `actionable_steps`, +`observable_steps`, `false_positive_rates`, `false_negative_rates`, and +`ttc_dists` overrides on `AttackerSettings`/`DefenderSettings`. It already +has `.per_node(attack_graph) -> dict[full_name, value]`, which flattens +any rule against a concrete graph. + +**Decision:** +- `NodePropertyRule` itself is **not** ported to Rust. Its DSL evaluation + stays exactly as today, in Python, unchanged. +- Everywhere a resolved rule is needed inside the Rust hot loop + (actionability + observability + FP/FN rates + TTC overrides, which + *are* read per-node, per-step inside attack-surface/defense-surface/ + false-alert computation), Python resolves it to a flat `dict[node_id, + value]` (or `set[node_id]` for booleans) **once**, at `reset()` time, + and hands that plain data across the FFI boundary. Rust never parses or + evaluates the DSL. +- **Rewards are not ported to Rust at all.** `rewards.py`'s reward + closures are a separate, pull-based API (`sim.agent_reward(state)`), + not part of `step()`'s return value - confirmed by reading + `simulator.py`: `step()` returns only `self._agent_states`, and reward + closures are built once in `__init__` and called on demand. They + operate on cheap set arithmetic (`step_performed_nodes`, etc.) over the + already-mirrored Python dataclasses. There's no performance reason to + move this, and leaving it alone removes an entire category of + porting/non-breaking risk for zero cost. `node_reward()`/ + `NodePropertyRule[float]` for rewards stay untouched, pure Python. +- `TTCDist` (`ttc_utils.py`) is the one exception that's genuinely + two-sided: it must stay a real, user-constructible Python class (users + author `ttc_dists` overrides as `TTCDist(...)` in scenario config), but + its *sampling* needs a Rust-side equivalent for the hot loop. Bridge via + the existing `TTCDist.to_dict()`/`from_dict()` methods: Python resolves + `ttc_dists: NodePropertyRule[TTCDist]` to `dict[node_id, ttc_dict]` + (via `.per_node()` + `.to_dict()` per value) and hands that plain dict + across; Rust parses the same shape into its own `TtcDist` enum. The + default (non-overridden) per-node TTC distribution is derived from + `node.ttc`/`node.type` already available on the (shared) graph, so most + nodes need no override entry at all. + +### 2.5 Internal freedoms that don't affect the public contract + +Not decisions requiring sign-off, but worth recording so nobody +"fixes" them later thinking they're bugs: + +- `AttackerState.previous_state`/`DefenderState.previous_state` currently + form an unbounded linked list back through every prior step (each new + state point to the literal previous state object). Confirmed via + `grep -rn previous_state` that nothing outside the `*_state_factories.py` + one-step-back `step_*` properties ever reads more than one level deep. + The Rust-backed implementation is free to *not* reconstruct this chain + beyond one level (i.e. only ever set `previous_state` to the immediately + prior Python state object, never deeper) - this is an internal memory + characteristic, not observable behavior, and the existing behavior + already doesn't rely on deep traversal. +- `MalSimulatorSettings.uncompromise_untraversable_steps` is defined but + not read anywhere in the current implementation (verified by grep) - + carry it through unchanged (mirror the field), don't implement new + behavior for it as part of this port. + +### 2.6 Rust-only path needs its own Scenario + NodePropertyRule equivalent + +§2.4 decided that `NodePropertyRule` stays Python-only, with Python +flattening it to plain data before crossing into `malsim-pyo3`. That +decision assumed Python is always present to do the flattening. The +Rust-only path (Phase C, §7) breaks that assumption: an embedding Rust +program with no Python runtime has no `Scenario`/`NodePropertyRule` to +call into, so if it's going to load a scenario YAML file at all (now a +goal - see §1), it needs its own way to parse the same YAML shapes and +evaluate the same by-asset-type/by-asset-name matching. + +**Decision:** port `NodePropertyRule`'s matching semantics +(`by_asset_type`/`by_asset_name` dict lookup, precedence: asset_name > +asset_type > default) to Rust as a **second, independent** +implementation used only by the Rust-only scenario loader - it does +*not* replace or get shared with the Python-side `NodePropertyRule` used +by the PyO3 path (§2.4 still stands for that path: Python flattens, +Rust never parses the DSL there). Two implementations of a small, +well-specified, RNG-free dict-lookup rule is a reasonable, low-risk +trade for keeping §2.4's simplicity on the PyO3 path while still letting +the Rust-only path be genuinely self-sufficient. `NodePropertyRule.to_dict +()`/`from_dict()`'s existing JSON-ish shape is the de facto schema both +implementations target, which keeps them honest against each other. + +**Scope boundary - the loader does not instantiate `policy`.** Scenario +YAML's `agents..policy` names a decision-agent class +(`agent_settings_factories.py`'s `policy_name_to_class`, e.g. +`BreadthFirstAttacker`). The Rust scenario loader parses and carries the +`policy` field as an opaque string and never validates or instantiates +it. Since Phase D (§13) the built-in policies exist in Rust: +`policies::policy_from_name` instantiates one from that string, and +`run_simulation::decision_agents(&scenario)` does so for every agent +(Python's lazy `AgentRuntimeMixin.agent`). An embedding Rust caller that +steps the `Simulator` itself remains free to ignore the field. + +**What's reused, not reimplemented:** the language/model/attack-graph +construction itself (`create_attack_graph`-equivalent) is *not* +duplicated - it's already available as pure Rust via mal-toolbox's +`maltoolbox-language`/`maltoolbox-model`/`maltoolbox-attackgraph` crates +(the same ones `malsim-core` already depends on / will depend on +transitively). Only malsim's *own* scenario-file schema (the YAML shape +in `tests/testdata/scenarios/*.yml`: `agents`, `sim_settings`, `extends`, +field validation/deprecation) and the `NodePropertyRule` matching above +are new Rust code. + +### 2.7 Rust code style, structure, and terminology + +The sections above (and §4's table) describe *what* logic moves to Rust +and *why* - they are deliberately silent on *where* it lives inside +`core/malsim-core`/`py-bindings/malsim-pyo3`, because that's a separate +decision with a separate answer: + +- **Crate/module layout does not have to mirror `python/malsim`'s file + layout.** There is no requirement that `ttc_utils.py` becomes `ttc.rs`, + that `attack_surface.py` becomes `attack_surface.rs`, or that the + Python package tree (`mal_simulator/`, `config/`, `scenario/`) shows up + as a matching Rust module tree. Organize the Rust code the way Rust + code should be organized - by ownership, by what borrows what, by + trait boundaries - and let that structure diverge from Python's module + boundaries wherever Rust's idioms want it to. §4's table is a map of + *content* (which Python behavior ends up native vs. stays Python), not + a map of *location*. +- **Stay close to a line-for-line port of the logic - written in + idiomatic Rust, not a syntactic transliteration.** These are not in + tension and neither one wins at the other's expense. Follow the + Python implementation's steps, order of operations, and decision + points closely enough that someone who knows `attack_surface.py` can + read the Rust function and follow along almost line by line - that + faithfulness is a real goal of this port, not just a description of + expected difficulty. At the same time, express each step the way + idiomatic Rust would: `Result`/`Option` instead of Python's exceptions/ + `None`-checks, iterators instead of manually-indexed loops, real enums + instead of Python's loosely-typed dict/string dispatch, ownership/ + borrowing used properly. What to avoid is "horrible" Rust that fights + the language just to look more like the Python source syntactically + (e.g. a hand-rolled index-based `while` loop where Python used `for`, + just because that's what the Python line looked like) - that's a + transliteration, not a port, and it's worse for the original author + too, since it obscures the logic behind unidiomatic noise instead of + making it easy to follow. +- **But keep the terminology recognizable.** The whole point of this + plan is that the original Python author can read the Rust code and + know what they're looking at. Reuse malsim's own vocabulary for the + same concepts - `attack_surface`, `defense_surface`, `necessity`, + `ttc_values`, `action_surface`, `performed_nodes`, `enabled_defenses`, + `impossible_steps`, and so on - adapted only for Rust naming + conventions (`snake_case` functions/fields, `PascalCase` types, e.g. + `TtcDist` not `TTCDist`). Don't rename a concept to different + vocabulary just because it landed in a different module than its + Python namesake; a reader should be able to grep malsim's Python + source for a name and find its Rust counterpart close by in spirit, + even if not in the same relative path. +- **Divergences get written down here, not as code comments.** When + porting a module turns up a place where the Rust implementation's + structure, behavior, or API shape genuinely differs from the Python + original in a way the original author needs to know - not just "this + landed in a different file" - record it in this document (§10), the + same way mal-toolbox's own `PORTING_NOTES.md` keeps a running + "Architecture: deliberate design differences" / "Confirmed upstream + bugs fixed, not reproduced" log. A Rust doc comment is for someone + reading that one file; this document is what the Python author + actually reads to understand the port, so that's where the callout + belongs - in addition to, not instead of, normal doc comments that + explain the Rust code on its own terms. + +## 3. Target architecture + +``` + ┌────────────────────────────────┐ + Rust-only caller ──▶ │ core/malsim-core (pure Rust) │──▶ depends on + │ scenario::load_from_file(yaml) │ maltoolbox- + │ - own NodePropertyRule (§2.6) │ language / + │ - field validation / extends │ -model / + │ Simulator>> │ -attackgraph + └───────────────┬──────────────────┘ (pure Rust) + │ path dependency + ┌───────────────┴──────────────┐ + Python caller ──▶ │ py-bindings/malsim-pyo3 │ + (via malsim._native)│ shared graph/model handles │──▶ pure + │ via PyCapsule (§2.2) │ maltoolbox + │ Simulator / DynaSimulator: │ crates only + │ reset_native()/step_native()│ (git dep) + │ → plain dict/set/float │ + └───────────────┬──────────────┘ + │ plain Python primitives only + ┌───────────────┴──────────────┐ + │ python/malsim/mal_simulator/ │ + │ MalSimulator.reset()/.step() │ + │ rebuilds AttackerState/ │ + │ DefenderState dataclasses │ + │ from native output │ + └────────────────────────────────┘ +``` + +Note: the Rust-only `scenario::load_from_file` path and the Python-bound +`py-bindings/malsim-pyo3` path are independent consumers of the same +`Simulator` core - the Python path never calls the Rust scenario loader +(Python keeps using its own `Scenario` class, unchanged, per §2.6), and +the Rust-only path never touches `malsim-pyo3`/PyO3 at all. + +(The module paths in the diagram - `scenario::`, `Simulator<...>` - are +illustrative of the dependency/data-flow shape, not a mandated namespace. +Per §2.7, the actual module layout inside each crate is free to be +whatever's idiomatic.) + +Key rule for the FFI boundary (both directions): **only plain data +crosses it** - ids (`AttackGraphNode.id: i64`, see §10), strings, +floats, bools, and flat dicts/sets/lists thereof. No Python object +(beyond the shared `AttackGraph`/`Model` handles) and no Rust struct is +ever handed across directly. This is what makes the +Python dataclasses genuinely "mirrored" rather than wrapped: `malsim-pyo3` +hands the Python shim raw ids and primitives; the shim resolves ids to +real `AttackGraphNode` objects via `attack_graph.nodes[id]` (already O(1) +in mal-toolbox) and constructs the unchanged dataclasses itself. + +`malsim-core`'s `Simulator` owns: +- `graph: Rc>` +- the `MalSimulatorSettings` passed at `reset` (§11); rewards are not + computed in Rust (§2.4) +- per-episode `GraphState` equivalent: ttc values by node id, pre-enabled + defenses, impossible attack steps, necessity-by-id (all computed once + at `reset()`) +- runtime `enabled_defenses: NodeSet` (grows during `step()`) +- per-agent runtime structs (`AttackerRuntime`/`DefenderRuntime`): the + Rust-side equivalent of the mutable parts of `AttackerState`/ + `DefenderState` (performed/attempted/observed node id sets, action + surface, num_attempts, iteration, performed_nodes_order, logs, and a + Rust-only `performed_history` that is not exposed to Python) plus the + flattened per-agent settings from §2.4 (actionable ids, observable ids, + FP/FN rate maps, ttc override map) - **not** rewards. + +`DynaSimulator` wraps a `Simulator` and adds the shared `Model` handle +and its pristine snapshot (§11). + +## 4. File/module inventory + +Where each piece of behavior lives now. Rust module layout is not required +to mirror Python's (§2.7); this is a map, not a rule. + +| Python (`python/malsim/`) | What it holds now | Rust counterpart (`core/malsim-core/src/`) | +|---|---|---| +| `mal_simulator/simulator.py` | `MalSimulator`; module-level `reset()`/`step()` delegating to `_native.Simulator`; `_graph_state_from_native`, `_ordered_new_nodes`, `MALSimulatorStaticData`. Public query methods unchanged. | `simulator.rs` | +| `mal_simulator/native_settings.py` | `flatten_sim_settings`/`flatten_attacker_settings`/`flatten_defender_settings` (§2.4). | `scenario/flatten.rs` (Rust-only path) | +| `mal_simulator/attacker_state_factories.py`, `defender_state_factories.py` | Only `create_*_state_from_native`, `get_entry_points`, `_log_entry_from_native`. | - | +| `mal_simulator/attacker_state.py`, `defender_state.py`, `agent_state.py`, `agent_states.py`, `simulator_state.py` | The mirrored dataclasses. **Do not change their shape.** | `AttackerRuntime`/`DefenderRuntime`/`SimState` in `simulator.rs` | +| `mal_simulator/graph_utils.py`, `state_query.py`, `node_getters.py` | Live: back `MalSimulator`'s public query methods (`node_is_blocked`, `node_is_traversable`, `node_ttc_value`, ...). Not dead code (§9). | `graph_utils.rs` (used inside `step`) | +| `mal_simulator/ttc_utils.py` | `TTCDist` and helpers, the user-facing config type. | `ttc.rs` | +| `mal_simulator/graph_state.py` | Only `GraphState`. | `graph_state.rs`, `necessity.rs`, `viability.rs` | +| `mal_simulator/event_logger.py` | Only `LogEntry`. | `event_logger.rs` | +| `mal_simulator/attacker_step.py`, `defender_step.py` | Only `attacker_is_terminated`/`defender_is_terminated`. | `attacker_step.rs`, `defender_step.rs` | +| `mal_simulator/false_alerts.py`, `observability.py` | Only the per-node rule getters. | `false_alerts.rs`, `observability.rs` | +| (deleted) `attack_surface.py`, `defense_surface.py`, `graph_processing.py`, `reset_agent.py`, `simulator_static_data.py` | - | `attack_surface.rs`, `defense_surface.rs`, `necessity.rs`, `viability.rs` | +| `mal_simulator/rewards.py`, `run_simulation.py` | Unchanged, pure Python (§2.4). | Independent Rust implementations (§13): `rewards.rs`, `run_simulation.rs` | +| `mal_simulator/agent_state.py`, `attacker_state.py`, `defender_state.py` (as seen by policies/rewards) | The mirrored dataclasses (see above). | `agent_state.rs`: borrowed `AttackerState`/`DefenderState`/`AgentState` views (§11) | +| `__main__.py` | Unchanged Python CLI. | `core/malsim-cli/src/main.rs` (`malsim` binary, §13) | +| `dyna_mal_simulator/simulator.py`, `simulator_state.py` | `DynaMalSimulator`, `dyna_reset`/`dyna_step` delegating to `_native.DynaSimulator`; `DynaMalSimulatorState`, `AssetOp`/`AssocOp`, `DetachedAsset`, `modification_record_from_native`. | `dyna_simulator.rs`, `dyna_attacker_step.rs`, `dyna_defender_step.rs`, `dyna_graph_state.rs`, `model_effects.rs`, `assoc_traversal.rs`, `model_state.rs` | +| `config/*`, `scenario/scenario.py` | Unchanged; used by the Python path only. | Independent Rust loader (§2.6): `settings.rs`, `scenario/{mod,loading,node_property_rule,agent_settings,flatten}.rs` | +| `policies/` | Untouched by design; doubles as an integration test. | Independent Rust implementations (§13): `policies/{mod,passive_agent,random_agent,keyboard_input,searchers,heuristic_agent,ttc_soft_min,shortest_path,path_finding}.rs` | +| `envs/`, `visualization/` | Untouched by design; they double as integration tests. | - | + +`py-bindings/malsim-pyo3/src/`: `lib.rs` (capsule extraction, plus the +test-support functions `node_count`, `set_detector_rates`, +`model_asset_count`, `model_add_asset_native` - none are public API), +`simulator.rs` (`Simulator` pyclass and the dict conversions both +pyclasses share), `dyna_simulator.rs` (`DynaSimulator` pyclass). It only +parses dicts, maps errors and builds output (§11). Stubs: +`python/malsim/_native.pyi`. + +Rust test fixtures: `core/malsim-core/src/test_fixtures.rs` compiles +`tests/testdata/langs/dummy_lang.mal` (the file `tests/conftest.py:: +dummy_lang_graph` uses) and wiperLang via `maltoolbox_language:: +from_mal_spec`. A real `AttackGraphNode` with a specific step type cannot +be hand-built (`AttackStepId` is a slotmap key only a `LanguageGraph` +mints), which is why graph-reading functions in `graph_state.rs` are +split into a thin node-reading wrapper plus a `*_from_parts`/`*_for_dist` +helper over plain data. + +## 5. Phase A: `MalSimulator` port (done) + +- **A1** - shared graph handle via `PyCapsule` (§2.2). +- **A2** - `ttc.rs`: `DistFunction`, `Operation`, `TtcDist`, + `named_ttc_dist`, on `statrs` + `rand`. +- **A3** - `graph_state.rs` (`TtcMode`, TTC values, pre-enabled defenses, + impossible steps, `compute_initial_graph_state`), `necessity.rs`. +- **A4** - `graph_utils.rs`: `node_is_live`, `node_is_necessary`, + `node_is_blocked`, `node_is_traversable`. +- **A5** - `attack_surface.rs` (`get_attack_surface`, + `get_effects_of_attack_step`), `defense_surface.rs`. +- **A6** - `false_alerts.rs`, `observability.rs`, `event_logger.rs`. +- **A7** - `attacker_step.rs`, `defender_step.rs`. +- **A8** - `_native.Simulator` pyclass with `reset_native`/`step_native` + returning plain dicts/lists. +- **A9** - `MalSimulator.reset()`/`.step()` delegate to native; + `native_settings.py`; `create_*_state_from_native`. Followed by the + delta-only `step_native` wire format (§10). +- **A10** - audit of the other public `MalSimulator` methods: no code + change. They read the mirrored Python state; `node_is_blocked`/ + `node_is_traversable` still evaluate in Python (§9). +- **A11** - dead Python hot-path code deleted (§4, §11). + +## 6. Phase B: `DynaMalSimulator` port (done) + +- **B1** - `assoc_traversal.rs`, `model_effects.rs`. +- **B2** - `model_state.rs` (snapshot/reconcile/reset), + `dyna_graph_state.rs`, `dyna_attacker_step.rs`, + `dyna_defender_step.rs`. +- **B3** - shared `Model` handle via `PyModel::__inner_capsule__` + (upstream mal-toolbox change, §2.2). +- **B4** - native dyna reset/step entry points, now the + `_native.DynaSimulator` pyclass (§11); `step_modification_record` + output. +- **B5** - `DynaMalSimulator.reset()`/`.step()` delegate to native. + Fixed two bug classes on the way: stale node ids after a model effect, + and Python's `GraphState` missing nodes created mid-episode (the + "TTC-gap fix"). Both in §10 "Dyna path". +- **B6** - audit of inherited query methods against a graph mutated + mid-episode: no code change; locked in by + `test_inherited_query_methods_follow_graph_mutated_by_model_effects`. +- **B7** - dead dyna Python deleted; viability/pruning ported to + `viability.rs`. + +## 7. Phase C: Rust-only library API + scenario loading (done) + +Phase C itself was library API only: the embedding program selects +actions itself. Built-in policies and a CLI were added later, in Phase D +(§13). + +- **C1** - `scenario/node_property_rule.rs`: `NodePropertyRule`. +- **C2** - `scenario/loading.rs`: YAML loading, `extends` merge + (`recursive_update`), path resolution, validation. Uses `serde_yaml`. +- **C3** - `settings.rs` (`MalSimulatorSettings`, `Flat*Settings`), + `scenario/agent_settings.rs`, `scenario/flatten.rs`. +- **C4** - `scenario/mod.rs`: `Scenario` (`load_from_file`, `from_dict`, + `flatten_agents(rng)`), `ModelSource`. +- **C5** - orchestration moved from `malsim-pyo3` into public + `malsim_core::simulator`; smoke test + `core/malsim-core/tests/scenario_simulator.rs` uses only the public + API (no pyo3 in `cargo tree -p malsim-core`). +- **C6** - Rust/Python scenario parity via committed golden JSON (§8). + +## 8. Testing rules + +- **Ported code gets Rust-native tests covering the same cases as the + Python tests, not just a green Python suite.** Python-only concerns + (pickling, `deepcopy`, object identity) are excluded; say so explicitly + when skipping one. +- **Never assert an exact sampled value or exact RNG-dependent sequence + against Python's output** (§2.1). Assert the structural property, or + pin to this port's own deterministic-per-seed output. Before touching + RNG-consuming code, `grep -rn seed= tests/`: several tests are pinned + to the current native RNG consumption order and will shift if it + changes. +- **Do not assert on `HashMap`/`HashSet`/`frozenset` iteration order.** + Node-id collections (`NodeSet`/`NodeMap`) use a non-randomized hasher, + but their order follows insertion history, not `AttackGraphNode.id`. + Everything else (i64/`String`-keyed collections, mal-toolbox's + `children`/`parents`, `potential_context`, model associated assets) + uses the std hasher, seeded per process, so an order-dependent + test passes or fails per process invocation, not per run within one + process. Run a suspect test in several separate `cargo test`/`pytest` + processes. +- The Python suite is the compatibility contract for the Python path, + and `envs/`, `policies/`, `visualization/` must keep working + unmodified. +- **Scenario parity (C6):** `python -m tests.scenario_parity` writes + `tests/testdata/scenario_parity.json` (each fixture's resolved, + non-random shape; a fixture Python refuses records only its error + type). `tests/test_scenario_parity.py` asserts the file is current; + `core/malsim-core/tests/scenario_parity.rs` asserts the Rust loader + matches it. Known divergences are an explicit list in the Rust test + (currently only `wrong_agent_classes_scenario.yml`, §12 C3). + +## 9. Open risks + +- **Two independent scenario/`NodePropertyRule` implementations** (§2.6) + can drift. Any change to `scenario.py`'s YAML schema or + `node_property_rule.py`'s precedence needs the matching Rust change + and a regenerated parity file (§8). +- **Two implementations of the blocked/traversable predicates:** + `graph_utils.py` (behind `MalSimulator.node_is_blocked`/ + `.node_is_traversable`) and `malsim-core::graph_utils` (inside + `step`). No dedicated cross-check exists; change both together. +- **mal-toolbox's `rust-rewrite` branch is a moving target.** Both + `Cargo.toml`s and `pyproject.toml` track the branch. The capsule names + and `__inner_capsule__` (§2.2) are the contract to re-check after an + upstream change. +- **`uv`/`maturin` can install a stale `mal-toolbox` build** (seen + repeatedly on this machine, root cause unknown): the installed + `maltoolbox/_native*.so` lacks a symbol that exists in the resolved + commit, even after `uv cache clean mal-toolbox`. Before blaming Rust + code for a Python-level failure after an upstream change, check + `strings | grep `. Workaround: fresh `git + clone` of mal-toolbox, `maturin build --release --manifest-path + py-bindings/maltoolbox-pyo3/Cargo.toml -o `, `uv pip install + --reinstall-package mal-toolbox `, then use `uv run --no-sync` + for the rest of the session (plain `uv run` re-syncs and reinstalls + the stale build). +- **Native memory grows across repeated `DynaMalSimulator` runs that + apply model effects** (~110-125 KB/run on `wiper_scenario.yml`). + Upstream and pre-existing: valgrind attributes it to + `maltoolbox/_native.so`, none to malsim's crates, and the old + pure-Python dyna path grew faster. The gc-based leak tests cannot see + it. Repro: build `DynaMalSimulator.from_scenario`, `run_simulation` + with `RandomAgent`, repeat 300x, read `VmRSS` after `gc.collect()` + + `malloc_trim(0)`. Not yet reported upstream. +- **Entry points or goals whose asset is removed mid-episode** are not + handled (a removed goal can never be performed). Pre-existing; + reset-time staleness is handled (§11). + +## 10. Differences log + +Per §2.7: where the Rust implementation's structure, behavior or API +shape differs from what reading `python/malsim` would lead you to expect, +and Python quirks that were ported on purpose. Add an entry whenever a +change introduces a new divergence; a comment in the `.rs` file is not +enough. + +### FFI boundary and Python-side reconstruction + +- **Node ids crossing the boundary are `AttackGraphNode.id: i64`**, the + stable user-facing id, not `AttackGraphNodeId` (an opaque slotmap key). + `to_node_id`/`stable_ids` in `malsim-pyo3/src/simulator.rs` translate. +- **`stable_ids` sorts its output**, and agents are processed in name + order (§12 C5). Every RNG-consuming or order-producing iteration over + a set in `malsim-core` likewise goes in `AttackGraphNode.id` order + (`graph_utils::sort_by_node_id`; asset-id sets in asset-id order). + Otherwise std `HashSet`/`HashMap` order, random per process, would + make a fixed seed irreproducible across processes. Test: + `malsim-core/tests/reproducibility.rs`. +- **`reset_native` returns full state; `step_native` returns deltas.** + Monotonic fields come back as `step_*` keys (`step_enabled_defenses`, + `step_performed_nodes`, `step_attempted_nodes`, + `step_compromised_nodes`, `step_observed_nodes`, `step_logs`) and + `create_*_state_from_native` merges them into `previous_state` + (`previous_state is None` means "this is reset output, full shape"). + `num_attempts` is not sent; Python increments it from + `step_attempted_nodes`. `action_surface`, `iteration`, `terminated` + are always full values. Resending accumulated state made the port + slower than pure Python and log handling O(episode²); regression + test: `test_logger_incremental_log_merge_matches_full_rerun`. +- **Graph-state fields (`ttc_values`, `necessity_per_node`, + `impossible_attack_steps`, `pre_enabled_defenses`) are sent at reset + only**, except on a dyna step that ran a model effect - see "Dyna + path". +- **Two RNG streams.** `reset()` draws one `native_seed` from the Python + `rng` and native seeds its own `StdRng` from it for all TTC/Bernoulli/ + detector sampling. The Python `rng` is used only for `get_entry_points` + and the `SAMPLE_TTC` reward mode. A fixed seed is reproducible, but + the seed-to-outcome mapping differs from pre-port (§2.1). +- **Multiple entry-point sets are sampled in Python** + (`get_entry_points(attack_graph, ...)`), permanently; only the chosen + set crosses the boundary. +- **`num_attempts` is sparse in Rust, dense in Python.** + `create_attacker_state_from_native` rebuilds + `dict.fromkeys(attack_graph.attack_steps, 0)` plus the counts. +- **`iteration` is 0-indexed in native, 1-indexed on + `AttackerState`/`DefenderState`.** The factories add 1 and use native's + un-incremented value as the `performed_nodes_order` key. Getting this + wrong corrupts those keys silently. +- **Per-attacker `ttc_dists` are override-only.** With overrides + configured, `AttackerState.ttc_values`/`.impossible_steps` hold only + the overridden nodes, not the graph-wide map. The resolved rule values + are treated as predefined-distribution *names* (`TTCDist.from_name`), + despite the field's declared `NodePropertyRule[TTCDist]` type. +- **`recording` order is reconstructed** by `_ordered_new_nodes`: + requested actions first in request order, then effect-chain-only nodes + in implementation-defined order. With several successful actions in one + step the old per-action interleaving is not reproduced. +- **An unknown agent name in `actions` raises `ValueError`** from native + (Python's `_pre_step_check` raises `KeyError` first on the normal + path). +- **Pickling:** `_native_sim` / `_dyna_native_sim` are dropped by + `__getstate__` and there is no `__setstate__`, so `reset()`/`step()` + on an unpickled simulator raise `AttributeError` (same as the reward + closures always did). Restoring would need Rust-side RNG state + serialization. +- **`reset()` reads the settings captured in `__init__`** + (`MALSimulatorStaticData`) for both simulators; reassigning + `sim.sim_settings` afterwards has no effect. + +### mal-toolbox binding behavior to know about + +- **`AttackGraphNode.children`/`.parents` return a cached Python set.** + `node.children.add(x)` never reaches the real graph, which native + reads; only assignment does. Tests use + `tests/conftest.py::connect_nodes`. +- **`AttackGraphNode.detectors` is likewise a Python-side cache, with no + setter.** To change detector rates on the real graph use the + test-support `_native.set_detector_rates(graph, node_id, label, + tprate, fprate)`. Native reads each node's own `detectors` map and + never the graph-level `attack_graph.detectors` list. +- **`AttackGraphNode.__hash__` includes the owning graph's pointer**, so + a `frozenset` of nodes iterates in a different order in every process. + Anything tie-breaking on that order (`ShortestPathAttacker`, searchers + with `ActionOrdering.NOTHING`) is not reproducible across processes; + tests must not pin it. `TTCSoftMinAttacker` needs an explicit `seed` + in its config to be deterministic. +- **Natively removed assets leave no tombstone.** mal-toolbox records + tombstones only in `PyModel.remove_asset`; native calls the core + `Model::remove_asset`, so `model.get_asset_by_id` returns `None` + afterwards. Hence `AssetOp.asset`/`AssocOp.assoc` are `ModelAsset | + DetachedAsset`: the live handle when it still resolves, otherwise a + `DetachedAsset(id, type, name)` built from the native record. + +### Python quirks ported as-is (do not "fix" without asking) + +- `TtcDist::success_probability` ignores `combine_with`. +- Pre-enabled defenses test `success_probability(0)`, i.e. `cdf(0) = 1 - + p` for a Bernoulli: the named `"Enabled"` dist takes the "never + succeeds" branch (pre-enabled), `"Disabled"` the "always succeeds" one. + Python has a `# TODO: is this correct?` there. +- `attempt_attacker_step` resolves the TTC dist before the `Disabled` + check, so a malformed `ttc` dict errors even with TTCs disabled. +- The `EXPECTED_VALUE`/`PRE_SAMPLE` comparison is effectively + `num_attempts_before + 2 >= ttc_value`. +- Entry points bypass both the action-surface and the traversability + check in `attacker_step` (Python: `# TODO: should this actually be the + case?`). +- `node_is_blocked`: an `and` node is blocked if *any* parent blocks it, + an `or` node only if *all* do. So a parentless `or` node is "blocked" + and a parentless `and` node is not. +- Detector rates use Python truthiness: `None` and `0.0` are falsy, a + negative rate is truthy but never fires. `collect_logs` calls + `get_context` before the true-positive roll (so its errors surface + even for a false negative); `collect_false_positives` calls + `get_random_context` only for a detector that fires. The RNG is drawn + only when the rate is truthy. +- `sample_size`'s `quantity is None` branch does not clip to `max_size` + (unreachable with a finite `max_size` today). +- More in §12 (C3 `_validate_agent_dict`, C4 `agents` vs + `agent_settings`). + +### Deliberate deviations + +- **`statrs` parameter order differs from scipy:** `Binomial::new(p, n)` + (Python args are `[n, p]`) and `Gamma::new(shape, rate)` with `rate = + 1 / scale`. `ttc.rs` swaps/inverts on purpose. +- **`NodePropertyRule` arguments become flattened sets/maps** (§2.4): + `Option<&NodeSet>` for actionability/observability (`None` = no rule + configured), `Option<&NodeMap>` for rates (`NodeSet`/`NodeMap`: + `FxHashSet`/`FxHashMap` over `AttackGraphNodeId`, in `types.rs`; + `necessity_per_node` is a `NodeDenseMap`, a slotmap + `SecondaryMap`). + `attack_surface.rs`, `defense_surface.rs` and `observability.rs` each + keep a private `node_is_*_flat` helper rather than sharing one in + `graph_utils.rs`, where it would look like the ported + `node_is_actionable`. +- **`get_attack_surface` has `#[allow(clippy::too_many_arguments)]`** + instead of a bundling struct with a single caller. +- **`get_effects_of_attack_step` grows one `visited` set** instead of + recomputing `performed | set(effects)` per iteration; same result. +- **`LogEntry` is id-based in Rust:** `trigger: AttackGraphNodeId` and + `DetectorId = (AttackGraphNodeId, String)` (node plus its label key), + since mal-toolbox's `Detector` has no id. Python re-wraps into the + real `LogEntry`. +- **`collect_logs` requires `model_asset` only for nodes that have + detectors**, matching where Python's assert sits. +- **Python's `assert node == attack_graph.nodes[node.id]` becomes a + liveness check** (`node_is_live`) returning `NodeNotInGraph`. +- **`state_query.py::node_ttc_value`'s precedence lives in + `attacker_step.rs::resolve_ttc_value`** (override, else graph-level + value, else `AttackerStepError::MissingTtcValue`). +- **`viability.rs` vs `graph_processing.py`:** + `prune_unviable_and_unnecessary_nodes` validates every necessity entry + before removing anything (Python fails half-pruned) and removes in + deterministic graph order; `make_node_unviable` returns only the set + of nodes made unviable; a missing child entry in + `propagate_viability_from_node` is `MissingViability`, whereas + `necessity.rs` treats it as "changed" (pre-existing inconsistency); + `ViabilityError` is `Debug` only because it wraps upstream + `GraphError`; Python's logging calls are dropped (no logging + dependency in the crate). + +### Dyna path + +- **Node ids from accumulated state can go stale mid-episode.** A model + effect can remove assets, including the backing asset of the step that + triggered it, and nothing recreates those nodes. Any id taken from + historical or cross-step state (`performed_nodes`, `enabled_defenses`, + `GraphState` maps, per-agent TTC overrides, a sibling in the same + effect chain, log context) must be looked up with `graph.nodes.get(id)` + / `node_is_live` and skipped when gone; never index `graph.nodes[id]` + on it (that panics the whole Python process with `invalid SlotMap key + used`). Ids read from the live graph in the same call are safe. + `stable_ids`/`id_value_map` drop dead ids centrally. When debugging + this class, print at the mutation site in `execute_model_effects` + (`new_assets`/`removed_assets`/`new_nodes`) before theorizing: two + backtrace-based diagnoses of it were wrong. +- **Graph-state fields are resent in full on a dyna step whose + `step_modification_record` is non-empty** (`insert_graph_state_fields` + in `build_step_output`). Python's `dyna_step` detects this by + `'ttc_values' in native_out['sim_state']` and replaces its + `GraphState` wholesale. A full resend rather than a delta because + `necessity_per_node` is recomputed for the whole graph on every fold + that runs. + Test: `test_native_dyna_simulator_step_resends_graph_state_only_on_model_effect`. +- **`execute_model_effects` does not fold new nodes into `GraphState`** + (Python's does). It returns `(modification_record, new_node_ids)`; + `dyna_attacker_step`/`dyna_defender_step` call + `dyna_graph_state.rs::fold_new_nodes_into_graph_state` right after, + but skip it when the effects returned no ops and no new nodes + (equivalent to Python's unconditional call: no RNG draws, same + necessity). Python's `add_new_nodes_to_graph_state` has no Rust + port; the fold extends the graph state in place. +- **`model_effects::regenerate_graph` renumbers the nodes created by + `partially_regenerate_graph`** canonically (by model asset id, then + creation order), contiguously from the prior `next_node_id`, because + mal-toolbox creates them in std-`HashSet` order. Temporary until + mal-toolbox creates nodes deterministically. +- **There is no `dyna_attempt_attacker_step`.** Python's differs from + the static one only by `num_attempts.get(node, 0)`, which the Rust + `attempt_attacker_step` already does. +- **No `DynaMalSimulatorState` struct in Rust;** dyna step functions take + `&mut GraphState` and `&mut enabled_defenses` separately. +- **`ModelSnapshot` is a typed struct, and `reconcile_model_to_snapshot` + computes a real minimal diff.** Python's version compares int ids + against string dict keys, so it always falls back to removing and + re-adding every association. Same end state; this is the one place + correctness was chosen over bug-for-bug fidelity. +- **The pristine model snapshot is captured natively** in + `DynaSimulator`'s constructor; Python no longer calls + `model.to_dict()` or stores a snapshot. +- **Modification records carry self-contained `AssetRef { id, + asset_type, name }`**, captured when each op is recorded, because a + later op in the same record can invalidate an earlier id. Wire keys: + `kind` (`asset`/`assoc`), `type` (`ADDITIVE`/`SUBTRACTIVE`), + `asset_{id,type,name}` or `left_asset_*`/`right_asset_*` + + `field_name`. Only `DynaSimulator.step_native` sends + `step_modification_record` (§12). +- **Bidirectional associations:** `Model::add_associated_assets` updates + both sides, so which side's tuple lands in a reconcile/modification + record depends on `HashMap` order. Tests assert on the resulting model + state, or accept either side. + +## 11. Conventions + +Standing decisions made by the user that later work follows without +asking again. Each entry names the phase it was decided in. + +- **Adding a crate dependency is always a question for the user**, also + when it only promotes a transitive dependency to a direct one. The + same goes for design choices with several defensible options: present + them, don't pick silently. + +- **Python code shadowed by native is deleted, not kept as an oracle** + (decided at A11/B7). Once a Python function no longer has a live caller + (one reachable from `MalSimulator`/`DynaMalSimulator`'s public API, + `native_settings.py`, `rewards.py`, `run_simulation.py`, `config/`, + `scenario/`, `envs/`, `policies/`, `visualization/` or `examples/`), it + is deleted outright: no feature flag, no test-only reference copy, no + deprecation period. Python tests of the deleted code are deleted with + it only if Rust tests cover the same behavior; where they don't, the + missing Rust tests are written first. +- **Live code left behind in an otherwise-dead module stays in that + module** (decided at A11/B7). The file shrinks to just the live items + rather than having them moved somewhere tidier, so import paths don't + change. Example: `event_logger.py` now holds only `LogEntry`, and + `attacker_step.py` only `attacker_is_terminated`. +- **Dead Python functionality with no Rust port gets ported, not dropped, + when deleting it would otherwise lose test coverage** (decided at B7 + for `graph_processing.py`'s viability/pruning half). Applies even when + nothing calls the code: coverage is kept by porting the code and its + tests to `malsim-core`, not by keeping the Python copy alive. + +- **Orchestration lives in `malsim-core`** (decided at C, 2026-10-10; + the "one `Simulator`" part was superseded on 2026-10-11, see the next + bullet). All reset/step orchestration (per-agent runtime state, + `SimState`, static and dyna reset/step) lives in public `malsim-core` + types that take plain-Rust settings structs and return a core error + type. `malsim-pyo3` is only a thin + wrapper: it parses Python dicts into those core structs and builds + Python output from core results. New orchestration logic never goes + into `malsim-pyo3`. +- **Static and dynamic simulators are separate types** (decided + 2026-10-11, replacing C's single `Simulator` with `new_dyna`). The user + asked for "a simulator object for running the simulator with static + instance model/attack graph" and a "DynaSimulator" for the dynamic one. + - `malsim_core::simulator::Simulator` runs a static graph. + `malsim_core::dyna_simulator::DynaSimulator` *contains* a `Simulator`, + plus the shared `Model` handle and its snapshot. It reuses the + `Simulator`'s reset and per-agent bookkeeping and swaps in only the + dyna step functions. This is composition, the Rust analogue of + `DynaMalSimulator(MalSimulator)`: no step-mode trait or generic, and + no `Deref` from `DynaSimulator` to `Simulator`. + - Python sees two pyclasses with the same method names: + `_native.Simulator(graph)` and `_native.DynaSimulator(graph, model)`, + both with `reset_native(settings, agents, seed)`/`step_native(actions)`. + The `Model` is captured and snapshotted in `DynaSimulator`'s + constructor (mirroring `DynaMalSimulator.__init__`), not passed per + call. No `dyna_`-prefixed method names. + - Both types return the one `SimulatorError` enum (the error convention + below). +- **The Rust scenario types keep their Python names** (decided at C). + They live in `malsim_core::scenario` (with submodules) and are named + `NodePropertyRule`, `AttackerSettings`, `DefenderSettings`, + `AgentSettings` (an enum over the two), `Scenario` and + `Scenario::load_from_file`. The exception is `MalSimulatorSettings`, + `AttackSurfaceSettings` and `RewardMode`, which live in a top-level + `malsim_core::settings` module (see the next bullets). Like Python, settings keep their + *unresolved* `NodePropertyRule`s. A separate resolve step turns them + into the id-keyed maps/sets the `Simulator` consumes. This applies + §2.7's terminology rule to type names as well. +- **Settings shared by the scenario loader and the `Simulator` live in + a top-level `malsim_core::settings` module** (decided at C). This + covers `MalSimulatorSettings`, `AttackSurfaceSettings`, `RewardMode` + and the flat inputs below; `TtcMode` stays in `graph_state.rs` and is + re-exported there. `scenario` and `simulator` both depend on + `settings`; `simulator` never depends on `scenario`. +- **The flat, id-resolved per-agent `Simulator` inputs are + `FlatAttackerSettings`, `FlatDefenderSettings` and the + `FlatAgentSettings` enum** (decided at C). They are named after + `native_settings.py`'s `flatten_*_settings`, which produce the same + shape on the Python side. +- **`Simulator` API: settings are passed at reset, mirroring + `reset_native`** (decided at C). `Simulator::new(graph)`, + `DynaSimulator::new(graph, model)`, `reset(&MalSimulatorSettings, + agents, seed) -> Result<&SimState, _>`, `step(&actions) -> + Result` (per-step deltas, like `step_native`; + `DynaSimulator::step` returns `DynaStepOutcome`, which adds the + modification record) and + `state() -> Option<&SimState>` for the full accumulated state. Multiple + entry-point sets are sampled before reset by the caller: Python's + `get_entry_points`, or a Rust helper on `Scenario`. +- **`DynaSimulator::restore_model()` restores a dyna model before agents + are flattened** (decided after C, 2026-10-11; it moved from `Simulator` + to `DynaSimulator` in the split above). It is a no-op for an + already-pristine model, and `reset` still restores too (a no-op diff + by then). Every dyna reset caller calls it *before* resolving per-node + settings into `FlatAgentSettings`: Python's `dyna_reset` through pyo3's + `DynaSimulator.restore_model_native()`, and Rust callers before + `Scenario::flatten_agents`. +- **Attacker entry points/goals are re-resolved by full name on every + reset** (decided 2026-10-11). The Rust `Scenario` keeps a private + full-name copy of its attacker settings, and `Scenario::flatten_agents` + resolves it against the current graph on each call. It now returns + `Result<_, AgentSettingsError>`; `UnknownNode` only happens if the + caller skipped `restore_model`. The public `agent_settings` keeps the + load-time ids. On the Python side, `DynaMalSimulator` captures the same + full-name copy at construction and `dyna_reset` re-resolves it after + the restore. +- **`NodePropertyRule` is generic** (decided at C). The `RuleValue` trait + covers three things: parsing from a JSON value, what a list-form entry + means (`true` for bool, `1.0` for f64, an error for `TtcDist`), and + Python truthiness. Truthiness matters because precedence is + `by_asset_name or by_asset_type or default`, so a falsy value falls + through, exactly as in Python. +- **Rust/Python parity checks use committed golden JSON** (decided at C). A Python + generator dumps the resolved, non-random shape of each fixture to + committed JSON. A pytest asserts that the JSON is current, and a Rust + `#[test]` asserts that the Rust implementation matches it. No test-only + FFI hooks, and `cargo test` never shells out to Python. +- **The dynamic scenario-dict representation is `serde_json::Value`** (decided at C). + YAML is parsed with `serde_yaml` (as §7 C2 specified; it is already + in the lockfile via mal-toolbox) straight into `serde_json::Value`. + That is the type mal-toolbox's `maltoolbox_model::from_dict` and node + `ttc` already use. Typed structs are built from it by hand-written + conversion code, not by `serde` derive. +- **Policies, rewards, `run_simulation` and a CLI exist in Rust** + (decided at D, 2026-10-12, reversing C's "library API only"). All nine + entries of `policy_name_to_class` are ported, including + `KeyboardAgent`. They are for the Rust-only path; the Python path keeps + its own, unchanged. +- **One `DecisionAgent` trait over an `AgentState` enum** (decided at D). + `trait DecisionAgent { fn get_next_action(&mut self, &AgentState) -> + Result, PolicyError> }`, with `AgentState` + an enum of borrowed `AttackerState`/`DefenderState` views + (`agent_state.rs`). This mirrors Python's single `DecisionAgent`: one + policy map, and a policy handed the wrong kind of agent fails at + runtime (`PolicyError::WrongAgentType`) rather than at compile time. + Policies that only read the action surface (`RandomAgent`, the + searchers, ...) work for both kinds, as `bfs_vs_bfs_scenario.yml` + relies on. +- **The CLI is its own crate, `core/malsim-cli`, and uses `clap`** + (decided at D). It builds the `malsim` binary and holds nothing but + argument parsing; `run_simulation`, policies and rewards are library + modules of `malsim-core`, which stays free of CLI dependencies. +- **Errors (existing convention, restated at C):** each module gets one + hand-written error enum with `Display` + `std::error::Error` and + `From` impls for wrapped errors. No `thiserror`. + +## 12. Decisions + +Ambiguities resolved during implementation without asking (per the +standing instructions: the most reasonable choice, recorded here). Each +entry names its phase. + +- **B7: the deleted-module reachability was decided against `examples/` + and everything in `python/malsim` outside the hot-path modules as live + roots.** Downstream code importing the deleted internals (e.g. + `malsim.mal_simulator.attack_surface`) breaks. That is accepted by the + §11 "delete outright" convention; none of those modules was exported + from a package `__init__`. +- **mal-toolbox `main` is the original pure-Python toolbox.** This repo + builds against mal-toolbox's `rust-rewrite` branch; `main` is only a + behavioral reference. +- **C1: `NodePropertyRule.default` (the dataclass field) is not ported.** + `value()` never reads it (it uses its `default` *argument*), and + `from_dict()` never sets it, so it's dead state. Rust's `value()` + returns `Option` instead; `None` is where Python returns its + `default` argument, so `rule.value(..).unwrap_or(d)` is the exact call. +- **C1: `per_node()` is keyed by node id, not full name** (§7 C1's + `HashMap`). Full names are one `full_name_of` away and ids are + what the `Simulator` consumes. +- **C1: TTC override values are parsed (and validated) when the rule is + loaded.** Python keeps the raw YAML value and only calls + `TTCDist.from_name` at reset time (`_flatten_ttc_dists`). The Rust rule + holds parsed `TtcDist`s, so an unknown name fails at scenario load + instead of at the first reset. Only the name form is accepted, as on + the Python path. A dict-form TTC is rejected there too. +- **C1: value parsing is typed but Python-lenient where Python visibly + is.** `f64` rules accept booleans (`True == 1` in Python). `bool` rules + accept only booleans; Python would accept any truthy value, but none of + the fixtures rely on that. An explicit `null` step value is treated as + absent, which is what Python's `or` chain does with it. +- **C2: a scenario file without `lang_file` is reported by validation, + not by path resolution.** Python's `load_scenario_dict` indexes + `scenario['lang_file']` unconditionally and raises a bare `KeyError`. + Rust skips path resolution for a missing key and lets + `validate_scenario_dict` report `MissingField("lang_file")`. Both reject + the file; only the error differs. +- **C2: YAML 1.2 vs PyYAML's YAML 1.1.** `serde_yaml` follows YAML 1.2, + so `yes`/`no`/`on`/`off` are strings, not booleans as in + `yaml.safe_load`. No fixture uses those spellings (checked by grep). + `serde_yaml` is marked deprecated upstream; it's used anyway because §7 + C2 specified it and mal-toolbox already depends on the same version. + Swapping it out later only touches `read_yaml_mapping`. +- **C2: `recursive_update` key order is deterministic** (old keys first, + then new-only keys). Python iterates a `set` of keys, so its order is + arbitrary, and nothing depends on it. +- **C3: unknown `policy` names are accepted** (carried opaquely, per + §2.6). Python's `agent_settings_from_dict` raises `LookupError` for a + name outside `policy_name_to_class`. The Rust loader has no policy + registry, so `wrong_agent_classes_scenario.yml` loads in Rust and fails + in Python. C6's parity test records this case as an expected + divergence. +- **C3: `_validate_agent_dict`'s missing-key check is ported as + written, i.e. it doesn't check.** Python tests `illegal_keys` twice + instead of `missing_keys`, so a missing `policy` is accepted. Rust + accepts it too. A missing `type` is still an error in both (Python + raises `KeyError` when reading it; Rust returns `MissingType`). +- **C3: agent fields are type-checked where Python is duck-typed.** + `goals` must be a list of strings (Python's `frozenset(d['goals'])` + would turn a bare string into a set of characters), and `config` must + be a mapping or `null` (`null` is treated as `{}`). The `sim_settings` + booleans must be booleans, and `seed` a non-negative integer. No + fixture is affected. +- **C3: `get_entry_points` with an empty list of alternatives returns no + entry points.** Python's `rng.choice([])` raises instead. The case + can't come from YAML (`entry_points: []` parses as an empty *single* + set, as in Python), only from programmatic construction. +- **C3: `MalSimulatorSettings` is `Copy` and carries `seed: + Option`.** Python allows any int. A negative seed would be + rejected by numpy's `default_rng` anyway. +- **C4: `lang_file` git URLs are not supported by the Rust loader.** + Python's `LanguageGraph.load_from_file` clones `.git` URLs (the + `integration`-marked `simple_scenario_git_url.yml` test). Supporting + that in Rust would need a git dependency or shelling out to `git`, for + a path that's already network-only on the Python side. Rust returns + `ScenarioError::Language` with an explanatory message instead. +- **C4: `Scenario` has no `to_dict`/`save_to_file`.** §7 asks only for + loading. A writer can be added later on top of + `NodePropertyRule::to_value`. +- **C4: `agents` must be present even though validation accepts + `agent_settings`.** `_validate_scenario_dict` treats `('agents', + 'agent_settings')` as a one-of pair, but `from_dict` then reads + `scenario_dict['agents']` and raises `KeyError`. Rust mirrors that by + returning `InvalidAgents` rather than silently aliasing + `agent_settings`. +- **C4: `sim_settings: null` means default settings.** Python would call + `MalSimulatorSettings(**None)` and raise a `TypeError`. Treating it like + an absent key is the natural reading, and no fixture has it. +- **C4: `Scenario::new` takes a `ModelSource` (file path or inline + dict), not an already-built `Model`.** The model must share the + scenario's freshly loaded `Rc`, which a caller-built + `Model` can't guarantee. Python's `Model`-instance branch has no safe + Rust equivalent. +- **C5: agents are processed in name order (`BTreeMap`), not insertion + order.** The pre-C5 pyo3 code used `HashMap`s, whose per-process random + iteration order made RNG consumption with more than one attacker or + defender nondeterministic across processes for a fixed seed. Sorted + order is deterministic. It differs from Python's dict order, but seeded + runs were never bit-compatible with Python anyway (§2.1). Native output + dicts are also keyed in name order now. +- **Dyna reset order: restore the model, then resolve per-node + settings.** `dyna_reset` calls `restore_model_native()` before + flattening, and re-resolves attacker entry points/goals by full name + (§11). Flattening against the previous episode's graph either raised + `ValueError: node id N is not part of this simulator's attack graph` + or left restored nodes out of the new episode's maps; a stale Python + `AttackGraphNode` cannot even be read (`.full_name` raises + `LookupError`). Ids of nodes that no longer exist are accepted by + native and stay inert (slotmap keys are versioned, so they never alias + a new node). Regression tests: + `test_reset_restored_nodes_keep_rule_settings` and + `test_reset_re_resolves_entry_points_and_goals_on_restored_nodes` in + `test_dyna_mal_simulator.py`, with Rust twins of the same names + (prefixed `dyna_reset_`) in `tests/scenario_simulator.rs`. +- **C5: `SimulatorError::ModelState` boxes its payload**, following the + existing `DynaAttackerStepError::ModelEffects(Box<..>)` precedent + (clippy `result_large_err`). +- **C5: model asset ids keep counting across dyna resets** (`Wiper-7`, + then `Wiper-8`, ... for the asset wiperLang's effect adds). This was + checked against Python's `DynaMalSimulator`, which does the same: the + model's id counter isn't part of the restored snapshot. The Rust smoke + test looks new nodes up by pattern instead of pinning a name. Left as + is (matches Python). +- **C6: YAML `!!set` is read from `serde_yaml`'s untagged form.** + PyYAML loads `entry_points: !!set {A: null}` as a Python `set`, which + `_load_entry_points` accepts. `serde_yaml` drops the tag and yields the + mapping `{A: null}`. The Rust loader therefore treats a mapping whose + values are all `null` as a set, for `entry_points` and `goals`. This is + slightly more permissive than Python: an *untagged* all-`null` mapping + is a `ValueError` there. Found by the parity test on + `detector_lang_scenario.yml`. +- **C6: the golden file records rate maps as `{len, nonzero}`.** Full + per-node rate maps would make the file several times larger for no + extra coverage (every non-listed node is `0.0`, which `len` already + pins). +- **Dyna reset: `DynaMalSimulator.agent_settings` gets fresh + `AttackerSettings` objects on every reset.** `dyna_reset` now returns the + re-resolved settings as a fourth tuple element, and both callers + (`__init__`, `reset`) store them. Code that held on to an old + `sim.agent_settings[...]` object across a reset keeps the previous + episode's node objects. Rules and every other field are the same objects + (shallow copy), so the reward closures built in `__init__`, which only + read rules, are unaffected. `dyna_reset` isn't exported from + `malsim.dyna_mal_simulator`, so changing its signature breaks no public + API. Entry points or goals whose asset is removed *mid-episode* are + still not handled (e.g. a removed goal can never be performed); that + case predates the port and is a different question from the + reset-time staleness fixed here. +- **D: agent states are borrowed views, not owned structs.** + `AttackerState`/`DefenderState` in `agent_state.rs` borrow the graph, + the model, `SimState`, the agent's runtime struct, its scenario + settings and the last `StepOutcome`. Accumulated fields are read + through `state.runtime` (`state.runtime.action_surface`, ...), the + per-step ones are fields of the view. `StepOutcome::from_reset` gives + the "everything is new" deltas Python's `step_*` properties return on + a reset state. There is no `previous_state`. +- **D: rewards are plain functions of the agent state** + (`rewards::attacker_step_reward`/`defender_step_reward`/ + `agent_reward`), not closures built at construction, since the view + already carries the settings Python's closures capture. `node_reward` + lives in `rewards.rs`, not `graph_utils.rs`, so the step code keeps not + depending on `scenario`. A defender with a reward mode other than + `CUMULATIVE`/`ONE_OFF` is `RewardError::UnsupportedDefenderRewardMode` + when its reward is computed; Python raises `KeyError` when the + simulator is constructed. +- **D: `run_simulation(sim, scenario, max_steps)` takes the scenario.** + The Rust `Simulator` holds no agent settings, policies or model, so the + `Scenario` the simulator was built from is passed alongside (a + simulator on another graph is `RunSimulationError::GraphMismatch`). It + resets the simulator itself, like Python. One `StdRng`, seeded from + `sim_settings.seed` (OS entropy without one), samples entry points, + yields the simulator seed and draws `SAMPLE_TTC` rewards. + `run_simulation_with_agents` takes caller-supplied `DecisionAgent`s. + Static `Simulator` only, as the Python CLI; progress goes to stdout + with `println!` (Python mixes `print` and `logging`). +- **D: policies iterate action surfaces in node-id order.** Wherever + Python iterates a `set` of nodes (`TTCSoftMinAttacker`, + `KeyboardAgent`, `ShortestPathAttacker`'s goals and performed nodes, + reward sums), Rust sorts by `AttackGraphNode.id` first, so a seeded run + is reproducible across processes. For the same reason the searchers' + `action_ordering: nothing` sorts by id and `list_random` behaves like + `random` (sort, then shuffle): Python's variants rely on set order, + which a `HashSet` randomizes per process. +- **D: policy `seed` must be an integer or `null`** for every policy + (Python only checks that in the searchers). Without a seed the policy + draws from OS entropy, as `random.Random(None)` does. Unknown `config` + keys are ignored, as in Python. +- **D: unknown policy names still load** (C3 stands, so the parity file + is unchanged); they fail with `PolicyError::UnknownPolicy` when + `run_simulation`/`decision_agents` instantiates them. +- **D: `path_finding.rs` runs one backwards Dijkstra per goal** instead + of building a networkx graph and searching once per compromised node. + Same cheapest path; ties are broken by node id, where networkx's + bidirectional search breaks them by traversal order. Python's + `_validate_path` AND-step branch (and `_add_paths_to_total_path`) is + unreachable - it tests `node not in path` while iterating `path` - so + it is not ported: like Python in practice, the path does not account + for an AND step's other parents. A missing TTC value is an error + (`AgentStateError::MissingTtcValue`) where Python raises `KeyError`. +- **D: `ShortestPathAttacker` picks its goal with its own seeded rng and + waits when there is no path.** Python calls the global + `random.choice` (ignoring its `seed`) and raises `StopIteration` on an + empty path. +- **D: `KeyboardAgent` rejects an index equal to the number of actions.** + Python's `0 <= node <= len(action_surface)` accepts it and then raises + `KeyError`. End of input is `PolicyError::Io` (Python: `EOFError`). It + is generic over its reader/writer so tests can drive it; + `KeyboardAgent::new()` uses stdin/stdout. +- **D: the two heuristic defenders share one loop** (`cheapest_defense` + with a per-policy predicate); the Python classes are copies of each + other. Their unused `seed`/`randomize` config is accepted and ignored. +- **D: the CLI has no `-g/--send-to-gui`.** The GUI client is not ported. + `-s` takes a non-negative seed and `-t` is range-checked by `clap` + (Python raises `ValueError` from `TTCMode(n)`). `-o` goes through + mal-toolbox's `AttackGraph::save_to_file`, which picks JSON or YAML by + extension. As in Python, the CLI replaces the scenario file's + `sim_settings` wholesale. +- **Simulator split (2026-10-11): the static `Simulator.step_native` + output no longer has `step_modification_record`.** The static path + never runs model effects, so it was always `[]`, and the core + `StepOutcome` no longer carries the field. Only + `DynaSimulator.step_native` sends it. + `test_native_simulator_step_output_is_delta_only(_attacker_only)` now + assert the exact key set `{'step_enabled_defenses'}`, so they are as + strict as before. No Python code read the key on the static path. +- **Simulator split: `DynaMalSimulator` keeps its native handle in + `_dyna_native_sim`, not the inherited `_native_sim`.** `MalSimulator` + types `_native_sim` as `_native.Simulator`, and `DynaSimulator` is + deliberately not a subtype of it (§11). `DynaMalSimulator` overrides + every method that reads the handle (`reset`/`step`), so a dyna instance + never needs the base attribute. `DynaMalSimulator.__getstate__` drops + `_dyna_native_sim` from pickles, the same way the base drops + `_native_sim`. +- **Simulator split: the pyo3 `NotReset` message names the pyclass** + (`"Simulator.step_native() called before reset_native()"` / + `"DynaSimulator.step_native() ..."`). Both are still `ValueError`s. The + old dyna-only "no Model is attached" error is gone, because a + `DynaSimulator` always has its model. + +## 13. Phase D: Rust-only policies, rewards, `run_simulation`, CLI (done) + +Everything here is for the Rust-only path. The Python package is not +changed and does not call any of it; `malsim-pyo3` exposes none of it. + +- **D1** - `agent_state.rs` (`AgentState` views, `node_ttc_value`, + `sorted_by_id`), `rewards.rs`; `SimState::done`, + `StepOutcome::from_reset`, `TtcMode::from_value`/`value`. +- **D2** - `policies/`: the `DecisionAgent` trait, `PolicyError`, + `policy_from_name` and the nine built-in policies. Rust ports of + `tests/agents/*.py` in `src/policies/tests.rs` (hand-built graphs) and + `tests/run_simulation.rs` (scenario files). +- **D3** - `run_simulation.rs` (`run_simulation`, + `run_simulation_with_agents`, `decision_agents`). +- **D4** - `core/malsim-cli`: the `malsim` binary + (`cargo run -p malsim-cli -- scenario.yml [-s SEED] [-t TTC_MODE] + [-o FILE]`). + +The Python tests assert some things the Rust ports do not, and the +reverse: + +- `test_defend_compromised_defender` sets rewards on the simulator's + settings after the state was built, so in Python both defenses cost 0 + and node 1 wins twice. The Rust test checks that the cheaper defense + actually wins. +- `ttc_avoider` pins simulator seed 2 and policy seed 0. As in Python the + assertion ("only easy steps") is not true for every seed: a hard step + whose pre-sampled TTC came out low is drawn now and then. diff --git a/README.md b/README.md index 995c5ebb..f22d763c 100644 --- a/README.md +++ b/README.md @@ -22,7 +22,7 @@ The simulator also has a DOI at the KTH Open and FAIR Data repository for easier - The CI pipeline runs `pytest`, `mypy` and `ruff format && ruff check` for linting and type checking. PRs will only be merged if pipeline succeeds. ## Making a release -- Make a release PR (called `release-x.y.z`) with your changes. Update version number in `pyproject.toml` and `malsim/__init__.py`. +- Make a release PR (called `release-x.y.z`) with your changes. Update version number in `pyproject.toml` and `python/malsim/__init__.py`. - Merge the PR after the pipeline suceeds - Tag the latest commit with the new version number - Push the tag diff --git a/core/malsim-cli/Cargo.toml b/core/malsim-cli/Cargo.toml new file mode 100644 index 00000000..3beba382 --- /dev/null +++ b/core/malsim-cli/Cargo.toml @@ -0,0 +1,15 @@ +[package] +name = "malsim-cli" +version.workspace = true +edition.workspace = true +license.workspace = true +repository.workspace = true +publish.workspace = true + +[[bin]] +name = "malsim" +path = "src/main.rs" + +[dependencies] +clap = { version = "4.6.7", features = ["derive"] } +malsim-core = { workspace = true } diff --git a/core/malsim-cli/src/main.rs b/core/malsim-cli/src/main.rs new file mode 100644 index 00000000..86a465a7 --- /dev/null +++ b/core/malsim-cli/src/main.rs @@ -0,0 +1,86 @@ +//! CLI to run simulations in MAL Simulator using scenario files - the Rust +//! counterpart of `python/malsim/__main__.py`. + +use std::error::Error; +use std::path::PathBuf; +use std::process::ExitCode; + +use clap::Parser; +use malsim_core::graph_state::TtcMode; +use malsim_core::run_simulation::run_simulation; +use malsim_core::scenario::Scenario; +use malsim_core::settings::{AttackSurfaceSettings, MalSimulatorSettings}; +use malsim_core::simulator::Simulator; + +const TTC_MODE_HELP: &str = "\ +0: EFFORT_BASED_PER_STEP_SAMPLE\t +1: PER_STEP_SAMPLE\t +2: PRE_SAMPLE\t +3: EXPECTED_VALUE\t +4: DISABLED"; + +#[derive(Parser, Debug)] +#[command(name = "malsim", version)] +struct Args { + /// Can be found in https://github.com/mal-lang/malsim-scenarios/ + scenario_file: PathBuf, + + /// If set to a path, attack graph will be dumped there + #[arg(short, long)] + output_attack_graph: Option, + + /// If set to a seed, simulator will use it as setting + #[arg(short, long)] + seed: Option, + + #[arg( + short, + long, + default_value_t = TtcMode::Disabled.value(), + value_parser = clap::value_parser!(u8).range(0..=4), + help = TTC_MODE_HELP, + )] + ttc_mode: u8, + + /// Maximum number of simulation steps to run + #[arg(long)] + max_steps: Option, +} + +/// Entrypoint function of the MAL Simulator CLI +fn run(args: &Args) -> Result<(), Box> { + let mut scenario = Scenario::load_from_file(&args.scenario_file)?; + // As in Python, the CLI's settings replace the scenario file's + // `sim_settings` wholesale. + scenario.sim_settings = MalSimulatorSettings { + seed: args.seed, + ttc_mode: TtcMode::from_value(args.ttc_mode).ok_or("invalid TTC mode")?, + attack_surface: AttackSurfaceSettings { + skip_unnecessary: false, + ..AttackSurfaceSettings::default() + }, + ..MalSimulatorSettings::default() + }; + let mut sim = Simulator::new(scenario.attack_graph.clone()); + + if let Some(path) = &args.output_attack_graph { + scenario + .attack_graph + .borrow() + .save_to_file(Some(&scenario.model.borrow()), path)?; + } + + run_simulation(&mut sim, &scenario, args.max_steps)?; + Ok(()) +} + +fn main() -> ExitCode { + let args = Args::parse(); + match run(&args) { + Ok(()) => ExitCode::SUCCESS, + Err(e) => { + eprintln!("error: {e}"); + ExitCode::FAILURE + } + } +} diff --git a/core/malsim-core/Cargo.toml b/core/malsim-core/Cargo.toml new file mode 100644 index 00000000..0e0d8cd0 --- /dev/null +++ b/core/malsim-core/Cargo.toml @@ -0,0 +1,18 @@ +[package] +name = "malsim-core" +version.workspace = true +edition.workspace = true +license.workspace = true +repository.workspace = true +publish.workspace = true + +[dependencies] +maltoolbox-attackgraph = { workspace = true } +maltoolbox-language = { workspace = true } +maltoolbox-model = { workspace = true } +rustc-hash = "2.1" +slotmap = "1.1" +rand = "0.10.3" +serde_json = { version = "1.0.151", features = ["preserve_order"] } +serde_yaml = "0.9.34" +statrs = { version = "0.19.1", default-features = false, features = ["std", "rand"] } diff --git a/core/malsim-core/src/agent_state.rs b/core/malsim-core/src/agent_state.rs new file mode 100644 index 00000000..3bc2df48 --- /dev/null +++ b/core/malsim-core/src/agent_state.rs @@ -0,0 +1,225 @@ +//! Read-only per-agent views handed to policies and reward functions - the +//! Rust counterpart of `agent_state.py`/`attacker_state.py`/ +//! `defender_state.py`. +//! +//! Python's `AttackerState`/`DefenderState` are owned dataclasses rebuilt +//! every step. Here they are borrowed views over what the `Simulator` +//! already holds (`SimState`, the agent's runtime struct and the last +//! step's `StepOutcome`) plus the agent's scenario settings, so building +//! one copies nothing but the defender's `step_compromised_nodes`. See +//! `PORTING_NOTES.md` §11. + +use crate::types::NodeSet; +use std::fmt; + +use maltoolbox_attackgraph::{AttackGraph, AttackGraphNodeId}; +use maltoolbox_model::Model; + +use crate::graph_state::TtcMode; +use crate::scenario::agent_settings::{AgentSettings, AttackerSettings, DefenderSettings}; +use crate::simulator::{AttackerRuntime, DefenderRuntime, SimState, StepOutcome}; + +#[derive(Debug, Clone, PartialEq)] +pub enum AgentStateError { + /// The agent is not registered in the simulator state (or has no entry + /// in the step outcome). + UnknownAgent(String), + /// Mirrors `node_ttc_value`'s assert on the TTC mode. + TtcValuesUnavailable(TtcMode), + /// Mirrors `node_ttc_value`'s assert that the node has a TTC value. + MissingTtcValue(String), +} + +impl fmt::Display for AgentStateError { + fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { + match self { + AgentStateError::UnknownAgent(name) => { + write!(f, "agent '{name}' is not part of the simulator state") + } + AgentStateError::TtcValuesUnavailable(mode) => write!( + f, + "TTC value only when TTCMode is PRE_SAMPLE or EXPECTED_VALUE, not {}", + mode.name() + ), + AgentStateError::MissingTtcValue(node) => { + write!(f, "Node {node} does not have a ttc value") + } + } + } +} + +impl std::error::Error for AgentStateError {} + +/// Port of `AttackerState`, as a borrowed view. +#[derive(Clone)] +pub struct AttackerState<'a> { + pub name: &'a str, + pub attack_graph: &'a AttackGraph, + pub model: &'a Model, + pub sim_state: &'a SimState, + pub settings: &'a AttackerSettings, + /// The accumulated fields: `performed_nodes`, `action_surface`, + /// `num_attempts`, `goals`, ... + pub runtime: &'a AttackerRuntime, + pub step_performed_nodes: &'a NodeSet, + pub step_attempted_nodes: &'a NodeSet, +} + +impl AttackerState<'_> { + /// The nodes this attacker has performed (`runtime.performed_nodes`) + /// in the order they were performed, each once: the nodes + /// compromised at reset first (in node-id order, since the order + /// within the pre-compromised set is not defined), then every + /// step's compromises in the order the step produced them. + pub fn performed_history(&self) -> &[AttackGraphNodeId] { + &self.runtime.performed_history + } + + /// Port of `state_query.py::node_ttc_value`: the attacker's own + /// (override) TTC value if it has one, else the graph-wide one. + pub fn node_ttc_value(&self, node: AttackGraphNodeId) -> Result { + let ttc_mode = self.sim_state.settings.ttc_mode; + if !matches!(ttc_mode, TtcMode::PreSample | TtcMode::ExpectedValue) { + return Err(AgentStateError::TtcValuesUnavailable(ttc_mode)); + } + self.runtime + .ttc_values + .get(&node) + .or_else(|| self.sim_state.graph_state.ttc_values.get(&node)) + .copied() + .ok_or_else(|| { + AgentStateError::MissingTtcValue( + self.attack_graph.full_name_of(node, Some(self.model)), + ) + }) + } +} + +/// Port of `DefenderState`, as a borrowed view. +#[derive(Clone)] +pub struct DefenderState<'a> { + pub name: &'a str, + pub attack_graph: &'a AttackGraph, + pub model: &'a Model, + pub sim_state: &'a SimState, + pub settings: &'a DefenderSettings, + /// The accumulated fields: `performed_nodes`, `compromised_nodes`, + /// `observed_nodes`, `action_surface`, ... + pub runtime: &'a DefenderRuntime, + /// Defenses enabled this step (by any defender, as on the Python side). + pub step_performed_nodes: &'a NodeSet, + /// Nodes any attacker compromised this step. + pub step_compromised_nodes: NodeSet, + pub step_observed_nodes: &'a NodeSet, +} + +/// `AttackerState | DefenderState`. +#[derive(Clone)] +pub enum AgentState<'a> { + Attacker(AttackerState<'a>), + Defender(DefenderState<'a>), +} + +impl<'a> AgentState<'a> { + /// The state of the agent `settings` describes, after the reset or + /// step that produced `outcome` (`StepOutcome::from_reset` for a + /// reset). + pub fn new( + settings: &'a AgentSettings, + attack_graph: &'a AttackGraph, + model: &'a Model, + sim_state: &'a SimState, + outcome: &'a StepOutcome, + ) -> Result, AgentStateError> { + let unknown = || AgentStateError::UnknownAgent(settings.name().to_string()); + match settings { + AgentSettings::Attacker(settings) => { + let runtime = sim_state + .attackers + .get(&settings.name) + .ok_or_else(unknown)?; + let step = outcome.attackers.get(&settings.name).ok_or_else(unknown)?; + Ok(AgentState::Attacker(AttackerState { + name: &settings.name, + attack_graph, + model, + sim_state, + settings, + runtime, + step_performed_nodes: &step.step_performed_nodes, + step_attempted_nodes: &step.step_attempted_nodes, + })) + } + AgentSettings::Defender(settings) => { + let runtime = sim_state + .defenders + .get(&settings.name) + .ok_or_else(unknown)?; + let step = outcome.defenders.get(&settings.name).ok_or_else(unknown)?; + Ok(AgentState::Defender(DefenderState { + name: &settings.name, + attack_graph, + model, + sim_state, + settings, + runtime, + step_performed_nodes: &outcome.step_enabled_defenses, + step_compromised_nodes: outcome + .attackers + .values() + .flat_map(|a| a.step_performed_nodes.iter().copied()) + .collect(), + step_observed_nodes: &step.step_observed_nodes, + })) + } + } + } + + pub fn name(&self) -> &'a str { + match self { + AgentState::Attacker(a) => a.name, + AgentState::Defender(d) => d.name, + } + } + + pub fn attack_graph(&self) -> &'a AttackGraph { + match self { + AgentState::Attacker(a) => a.attack_graph, + AgentState::Defender(d) => d.attack_graph, + } + } + + pub fn model(&self) -> &'a Model { + match self { + AgentState::Attacker(a) => a.model, + AgentState::Defender(d) => d.model, + } + } + + pub fn action_surface(&self) -> &'a NodeSet { + match self { + AgentState::Attacker(a) => &a.runtime.action_surface, + AgentState::Defender(d) => &d.runtime.action_surface, + } + } + + /// `action_surface` ordered by node id (`sorted(.., key=lambda n: + /// n.id)`), the deterministic order policies iterate it in. + pub fn sorted_action_surface(&self) -> Vec { + sorted_by_id(self.attack_graph(), self.action_surface().iter().copied()) + } +} + +/// `nodes` ordered by `AttackGraphNode.id`. Nodes no longer in the graph +/// are dropped. +pub fn sorted_by_id( + attack_graph: &AttackGraph, + nodes: impl IntoIterator, +) -> Vec { + let mut nodes: Vec<(i64, AttackGraphNodeId)> = nodes + .into_iter() + .filter_map(|key| attack_graph.nodes.get(key).map(|node| (node.id, key))) + .collect(); + nodes.sort_unstable_by_key(|&(id, _)| id); + nodes.into_iter().map(|(_, key)| key).collect() +} diff --git a/core/malsim-core/src/assoc_traversal.rs b/core/malsim-core/src/assoc_traversal.rs new file mode 100644 index 00000000..e7c37483 --- /dev/null +++ b/core/malsim-core/src/assoc_traversal.rs @@ -0,0 +1,765 @@ +//! Rust port of `python/malsim/dyna_mal_simulator/process_assoc_traversal.py` +//! - see `PORTING_NOTES.md` §6 Phase B1. +//! +//! Evaluates an already-parsed [`AssocTraversalChain`] (mal-toolbox's +//! declaration grammar for a model effect's `base`/`target` expression) +//! against a live [`Model`]'s actual asset-association links - walking +//! `associated_assets` by asset id, sampling quantity filters via `rng`. +//! This is deliberately *not* the same traversal mal-toolbox's own +//! `maltoolbox-language::graph::assoc_traversal` module implements: that +//! module walks the *type*-level `LanguageGraph` (by `AssetId`) to +//! validate a model effect's declaration at language-compile time: this +//! module walks actual model *instances* (by `i64` asset id) at +//! simulation runtime, with RNG-driven quantity filtering the type-level +//! validator never needs. The two intentionally share no code. + +use std::collections::HashSet; +use std::fmt; +use std::hash::Hash; + +use maltoolbox_language::graph::ids::AssetId; +use maltoolbox_language::graph::model_effect::{ + AssocSet, AssocTraversal, AssocTraversalElem, GlobAssocTraversal, QuantityFilter, SetOperation, +}; +use maltoolbox_model::Model; +use rand::seq::IndexedRandom; +use rand::{Rng, RngExt}; + +#[derive(Debug, Clone, PartialEq)] +pub enum AssocTraversalError { + /// Mirrors Python's `raise ValueError('Association traversal chain + /// did not terminate in an AssocTraversal.')` in + /// `_resolve_terminal_traversal`. + EmptyChain, + /// Mirrors Python's `raise NotImplementedError('Quantity filtering is + /// not supported set operations in terminal fields.')`. + QuantityFilterUnsupportedInTerminal, + /// Mirrors the bare `assert node.model_asset` guard every + /// `parse_addition`/`parse_removal` caller in `model_effects.py` relies + /// on before calling them. + NodeHasNoModelAsset, + /// Mirrors Python's `raise ValueError(f'{asset.name} does not have a + /// valid association to the node's model asset {node.model_asset.name}, + /// so cannot traverse to self.')` in `parse_addition`'s terminating + /// expression for `field_name == 'self'`. + NoSelfAssociation { asset_id: i64, node_asset_id: i64 }, + /// Mirrors Python's bare `asset.lg_asset.associations[field_name]` + /// `KeyError` in `parse_addition`'s terminating expression (no + /// `asset_filter` and no declared association for `field_name`). + UnknownAssociation { asset_id: i64, field_name: String }, +} + +impl fmt::Display for AssocTraversalError { + fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { + match self { + AssocTraversalError::EmptyChain => { + write!(f, "association traversal chain did not terminate in an AssocTraversal") + } + AssocTraversalError::QuantityFilterUnsupportedInTerminal => write!( + f, + "quantity filtering is not supported for set operations in terminal fields" + ), + AssocTraversalError::NodeHasNoModelAsset => { + write!(f, "attack graph node does not have a model asset") + } + AssocTraversalError::NoSelfAssociation { asset_id, node_asset_id } => write!( + f, + "asset {asset_id} does not have a valid association to the node's model asset {node_asset_id}, so cannot traverse to self" + ), + AssocTraversalError::UnknownAssociation { asset_id, field_name } => write!( + f, + "asset {asset_id} does not have an association for field \"{field_name}\"" + ), + } + } +} + +impl std::error::Error for AssocTraversalError {} + +/// Port of `sample_size`. `max_size: None` mirrors Python's default +/// `max_size=float('inf')`. +/// +/// **The `quantity is None` branch returns `1` unconditionally, even when +/// `max_size` is given and is `0`** - ported as-is from Python, which +/// never clips this branch against `max_size` (only the `int`/tuple +/// branches do). Every real call site only reaches this branch with +/// `max_size` left at its default (infinite) - see `model_effects.rs`'s +/// `add_asset_op` - so the mismatch is latent, not reachable today. +pub fn sample_size( + quantity: Option, + rng: &mut impl Rng, + max_size: Option, +) -> i64 { + match quantity { + None => 1, + Some(QuantityFilter::Exact(q)) => match max_size { + Some(m) => q.min(m), + None => q, + }, + Some(QuantityFilter::Range(lo, hi)) => { + let high = match max_size { + Some(m) => hi.min(m), + None => hi, + }; + let low = lo.min(high); + rng.random_range(low..=high) + } + } +} + +/// The elements of `set` in ascending order. The std sets this module +/// works on iterate in a per-instance random order, so every loop that +/// draws from the RNG or produces an ordered result goes through this. +pub fn sorted_vec(set: &HashSet) -> Vec { + let mut items: Vec = set.iter().cloned().collect(); + items.sort_unstable(); + items +} + +/// Port of `_apply_quantity_filter`. Generic over the sampled element +/// type so it serves both the asset-id sets this module traverses and +/// the `Addition`/`Removal` tuple sets `model_effects.rs` filters. +/// +/// Samples from the elements in sorted order, so the result only depends +/// on the RNG state, not on the (std `RandomState`) layout of `objects`. +pub fn apply_quantity_filter( + objects: &HashSet, + quantity: Option, + rng: &mut impl Rng, +) -> HashSet { + let items = sorted_vec(objects); + let size = sample_size(quantity, rng, Some(items.len() as i64)).max(0) as usize; + items.sample(rng, size).cloned().collect() +} + +/// Port of `_assoc_traversal`. +/// +/// **`field_name == "self"` returns `instigating_assets` unchanged**, +/// ignoring any `asset_filter`/`quantity_filter` that happen to also be +/// set on the same element - ported as-is from Python's early `return`, +/// which never reaches the rest of the function body in that case. +fn assoc_traversal( + model: &Model, + instigating_assets: &HashSet, + traversal: &AssocTraversal, + rng: &mut impl Rng, +) -> HashSet { + if traversal.field_name == "self" { + return instigating_assets.clone(); + } + let mut next_assets = HashSet::new(); + for asset_id in sorted_vec(instigating_assets) { + let Some(asset) = model.get_asset_by_id(asset_id) else { + continue; + }; + // Mirrors Python's `logger.error(...)` + skip for an asset with no + // such association field - not an error, just excluded. + let Some(candidates) = asset.associated_assets.get(&traversal.field_name) else { + continue; + }; + let mut candidate_set: HashSet = candidates.clone(); + if let Some(filter_id) = traversal.asset_filter { + candidate_set.retain(|&id| { + model + .get_asset_by_id(id) + .map(|a| a.lg_asset == filter_id) + .unwrap_or(false) + }); + } + if traversal.quantity_filter.is_some() { + candidate_set = apply_quantity_filter(&candidate_set, traversal.quantity_filter, rng); + } + next_assets.extend(candidate_set); + } + next_assets +} + +/// Port of `_glob_assoc_traversal` - a transitive-closure fixed-point +/// loop over `glob.pattern`, re-applying the pattern to the growing +/// result set until it stops growing. +fn glob_assoc_traversal( + model: &Model, + instigating_assets: &HashSet, + glob: &GlobAssocTraversal, + rng: &mut impl Rng, +) -> Result, AssocTraversalError> { + let mut next_assets = + traverse_association_chain(model, instigating_assets, &glob.pattern, rng)?; + loop { + let applied = traverse_association_chain(model, &next_assets, &glob.pattern, rng)?; + let mut union = next_assets.clone(); + union.extend(applied); + if union.len() == next_assets.len() { + break; + } + next_assets = union; + } + if glob.quantity_filter.is_some() { + next_assets = apply_quantity_filter(&next_assets, glob.quantity_filter, rng); + } + Ok(next_assets) +} + +/// Port of `_assoc_set_traversal`. `SetOperation`'s Rust enum is +/// exhaustively matched, so Python's `else: raise ValueError('Unknown set +/// operation ...')` has no Rust equivalent - unreachable by construction. +fn assoc_set_traversal( + model: &Model, + starting_assets: &HashSet, + assoc_set: &AssocSet, + rng: &mut impl Rng, +) -> Result, AssocTraversalError> { + let left = traverse_association_chain(model, starting_assets, &assoc_set.left, rng)?; + let right = traverse_association_chain(model, starting_assets, &assoc_set.right, rng)?; + let mut candidate_assets: HashSet = match assoc_set.set_op { + SetOperation::Union => left.union(&right).copied().collect(), + SetOperation::Difference => left.difference(&right).copied().collect(), + SetOperation::Intersection => left.intersection(&right).copied().collect(), + }; + if assoc_set.quantity_filter.is_some() { + candidate_assets = apply_quantity_filter(&candidate_assets, assoc_set.quantity_filter, rng); + } + Ok(candidate_assets) +} + +/// Port of `traverse_association_chain`. `AssocTraversalElem`'s Rust enum +/// is exhaustively matched, so Python's `else: raise ValueError('Unknown +/// association traversal type ...')` has no Rust equivalent. +pub fn traverse_association_chain( + model: &Model, + instigating_assets: &HashSet, + chain: &[AssocTraversalElem], + rng: &mut impl Rng, +) -> Result, AssocTraversalError> { + let mut current_assets = instigating_assets.clone(); + for elem in chain { + current_assets = match elem { + AssocTraversalElem::Traversal(t) => assoc_traversal(model, ¤t_assets, t, rng), + AssocTraversalElem::Glob(g) => glob_assoc_traversal(model, ¤t_assets, g, rng)?, + AssocTraversalElem::Set(s) => assoc_set_traversal(model, ¤t_assets, s, rng)?, + }; + } + Ok(current_assets) +} + +/// (anchor asset id, field name used to reach the terminal, right-hand +/// side resolved from [`parse_addition`]/[`parse_removal`]'s `terminate` +/// closures). +type TerminalResolve = (HashSet, Option); + +/// Port of `_resolve_terminal_traversal`. Generic over the terminal +/// element type `T` the caller's `terminate` closure produces - +/// `parse_addition`'s `Addition` or `parse_removal`'s `Removal`. +fn resolve_terminal_traversal( + model: &Model, + instigating_assets: &HashSet, + chain: &[AssocTraversalElem], + rng: &mut impl Rng, + terminate: &F, +) -> Result, AssocTraversalError> +where + T: Eq + Hash + Clone + Ord, + F: Fn(&HashSet, &AssocTraversal) -> Result, AssocTraversalError>, +{ + let Some((last, init)) = chain.split_last() else { + return Err(AssocTraversalError::EmptyChain); + }; + let current_assets = traverse_association_chain(model, instigating_assets, init, rng)?; + match last { + AssocTraversalElem::Traversal(t) => terminate(¤t_assets, t), + // `*` terminates in whatever its inner pattern terminates in. + AssocTraversalElem::Glob(g) => { + resolve_terminal_traversal(model, ¤t_assets, &g.pattern, rng, terminate) + } + AssocTraversalElem::Set(s) => { + let (left, left_quantity) = + resolve_terminal_traversal(model, ¤t_assets, &s.left, rng, terminate)?; + let (right, right_quantity) = + resolve_terminal_traversal(model, ¤t_assets, &s.right, rng, terminate)?; + if left_quantity.is_some() || right_quantity.is_some() { + return Err(AssocTraversalError::QuantityFilterUnsupportedInTerminal); + } + let terminal: HashSet = match s.set_op { + SetOperation::Union => left.union(&right).cloned().collect(), + SetOperation::Difference => left.difference(&right).cloned().collect(), + SetOperation::Intersection => left.intersection(&right).cloned().collect(), + }; + Ok((terminal, None)) + } + } +} + +/// Right-hand side of a parsed addition: either an existing model asset +/// reached by traversal, or the *type* of a not-yet-created asset (when +/// no live association exists yet) - Python's `LanguageGraphAsset | +/// ModelAsset` union, translated to ids on both sides. +#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, PartialOrd, Ord)] +pub enum AdditionTarget { + ExistingAsset(i64), + NewAssetType(AssetId), +} + +/// (left asset id, field name, right-hand side). +pub type Addition = (i64, String, AdditionTarget); +/// (left asset id, field name, right asset id). +pub type Removal = (i64, String, i64); + +/// Port of `parse_addition`'s inner `_parse_terminating_expr`. +fn parse_addition_terminal( + model: &Model, + node_asset_id: i64, + instigating_assets: &HashSet, + traversal: &AssocTraversal, +) -> Result, AssocTraversalError> { + let mut additions = HashSet::new(); + for asset_id in sorted_vec(instigating_assets) { + let Some(asset) = model.get_asset_by_id(asset_id) else { + continue; + }; + + if traversal.field_name == "self" { + let node_asset = model + .get_asset_by_id(node_asset_id) + .expect("node's own model asset must exist in the model"); + if model + .lang_graph + .associations_to(asset.lg_asset, node_asset.lg_asset) + .is_empty() + { + return Err(AssocTraversalError::NoSelfAssociation { + asset_id, + node_asset_id, + }); + } + for (field_name, associated) in &asset.associated_assets { + if associated.contains(&node_asset_id) { + additions.insert(( + asset_id, + field_name.clone(), + AdditionTarget::ExistingAsset(node_asset_id), + )); + } + } + } + + if let Some(filter_id) = traversal.asset_filter { + additions.insert(( + asset_id, + traversal.field_name.clone(), + AdditionTarget::NewAssetType(filter_id), + )); + } else { + let associations = model.lang_graph.associations(asset.lg_asset); + let assoc = associations.get(&traversal.field_name).ok_or_else(|| { + AssocTraversalError::UnknownAssociation { + asset_id, + field_name: traversal.field_name.clone(), + } + })?; + let target_type = assoc.get_field(&traversal.field_name).asset; + additions.insert(( + asset_id, + traversal.field_name.clone(), + AdditionTarget::NewAssetType(target_type), + )); + } + } + Ok((additions, traversal.quantity_filter)) +} + +/// Port of `parse_addition`. +pub fn parse_addition( + model: &Model, + node_asset_id: i64, + instigating_assets: &HashSet, + chain: &[AssocTraversalElem], + rng: &mut impl Rng, +) -> Result, AssocTraversalError> { + resolve_terminal_traversal(model, instigating_assets, chain, rng, &|assets, t| { + parse_addition_terminal(model, node_asset_id, assets, t) + }) +} + +/// Port of `parse_removal`'s inner `_parse_terminating_expr`. Unlike +/// addition's terminal, removal never errors on a missing association - +/// Python's `.get(field_name, set())` defaults to empty, same as this +/// port's `.get(...).cloned().unwrap_or_default()`. +fn parse_removal_terminal( + model: &Model, + node_asset_id: i64, + instigating_assets: &HashSet, + traversal: &AssocTraversal, +) -> Result, AssocTraversalError> { + let mut removals = HashSet::new(); + for asset_id in sorted_vec(instigating_assets) { + let Some(asset) = model.get_asset_by_id(asset_id) else { + continue; + }; + + if traversal.field_name == "self" { + for (field_name, associated) in &asset.associated_assets { + if associated.contains(&node_asset_id) { + removals.insert((asset_id, field_name.clone(), node_asset_id)); + } + } + } + + let mut candidates: HashSet = asset + .associated_assets + .get(&traversal.field_name) + .cloned() + .unwrap_or_default(); + if let Some(filter_id) = traversal.asset_filter { + candidates.retain(|&id| { + model + .get_asset_by_id(id) + .map(|a| a.lg_asset == filter_id) + .unwrap_or(false) + }); + } + for right_id in candidates { + removals.insert((asset_id, traversal.field_name.clone(), right_id)); + } + } + Ok((removals, traversal.quantity_filter)) +} + +/// Port of `parse_removal`. +pub fn parse_removal( + model: &Model, + node_asset_id: i64, + instigating_assets: &HashSet, + chain: &[AssocTraversalElem], + rng: &mut impl Rng, +) -> Result, AssocTraversalError> { + resolve_terminal_traversal(model, instigating_assets, chain, rng, &|assets, t| { + parse_removal_terminal(model, node_asset_id, assets, t) + }) +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::test_fixtures::wiper_attack_graph; + use maltoolbox_attackgraph::{AttackGraph, AttackGraphNodeId}; + use maltoolbox_language::graph::model_effect::AssocTraversalElem; + use rand::rngs::StdRng; + use rand::SeedableRng; + + fn rng() -> StdRng { + StdRng::seed_from_u64(42) + } + + // --- Direct port of `test_dyna_mal_simulator.py::test_assoc_traversal` --- + + #[test] + fn wiper_infect_step_base_is_self() { + let (graph, model) = wiper_attack_graph(); + let infect_id = graph.full_name_to_node["InfectedDevice:infect"]; + let infect = &graph.nodes[infect_id]; + let device_asset = infect.model_asset.expect("infect step has a model asset"); + let effect = &infect + .additive_model_effects + .as_ref() + .expect("infect step has model effects")[0]; + let mut r = rng(); + + let terminating = traverse_association_chain( + &model, + &HashSet::from([device_asset]), + &effect.base, + &mut r, + ) + .unwrap(); + assert_eq!( + terminating, + HashSet::from([device_asset]), + "infect step has self as base" + ); + + let resolved = traverse_association_chain( + &model, + &terminating, + &effect.targets[0].assoc_traversal, + &mut r, + ) + .unwrap(); + assert!( + resolved.is_empty(), + "infect step should not have any targets yet" + ); + } + + #[test] + fn wiper_infect_step_target_resolves_after_adding_wiper() { + let (graph, mut model) = wiper_attack_graph(); + let infect_id = graph.full_name_to_node["InfectedDevice:infect"]; + let infect = &graph.nodes[infect_id]; + let device_asset = infect.model_asset.unwrap(); + let effect = infect.additive_model_effects.as_ref().unwrap()[0].clone(); + let mut r = rng(); + + let next_id = model.next_id; + let wiper_id = model + .add_asset( + "Wiper", + Some("Wiper".to_string()), + Some(next_id), + None, + None, + false, + ) + .unwrap(); + let infected_device = model.get_asset_by_name("InfectedDevice").unwrap().id; + model + .add_associated_assets(wiper_id, "victim", HashSet::from([infected_device])) + .unwrap(); + + let resolved = traverse_association_chain( + &model, + &HashSet::from([device_asset]), + &effect.targets[0].assoc_traversal, + &mut r, + ) + .unwrap(); + assert_eq!( + resolved, + HashSet::from([wiper_id]), + "infect step should have Wiper as target" + ); + } + + /// Builds the same `Wiper:exfiltrate` fixture `test_assoc_traversal` + /// does: the `Wiper:infect` model effect has already fired by hand + /// (an explicitly-named `Wiper` asset, linked via `malware`), and the + /// graph has been regenerated so `Wiper:exfiltrate` exists. + fn wiper_attack_graph_with_wiper_asset() -> (AttackGraph, Model, AttackGraphNodeId) { + let (mut graph, mut model) = wiper_attack_graph(); + let next_id = model.next_id; + let wiper_id = model + .add_asset( + "Wiper", + Some("Wiper".to_string()), + Some(next_id), + None, + None, + false, + ) + .unwrap(); + let infected_device = model.get_asset_by_name("InfectedDevice").unwrap().id; + model + .add_associated_assets(wiper_id, "victim", HashSet::from([infected_device])) + .unwrap(); + graph.regenerate_graph(&model).unwrap(); + let exfiltrate_id = graph.full_name_to_node["Wiper:exfiltrate"]; + (graph, model, exfiltrate_id) + } + + #[test] + fn wiper_exfiltrate_step_base_is_infected_data() { + let (graph, model, exfiltrate_id) = wiper_attack_graph_with_wiper_asset(); + let exfiltrate = &graph.nodes[exfiltrate_id]; + let wiper_asset = exfiltrate + .model_asset + .expect("exfiltrate step has a model asset"); + let effect = &exfiltrate.additive_model_effects.as_ref().unwrap()[0]; + let mut r = rng(); + + let terminating = + traverse_association_chain(&model, &HashSet::from([wiper_asset]), &effect.base, &mut r) + .unwrap(); + let infected_data = model.get_asset_by_name("InfectedData").unwrap().id; + assert_eq!( + terminating, + HashSet::from([infected_data]), + "exfiltrate step has InfectedData as base" + ); + + // InfectedData isn't associated to C2Server yet, so the target + // doesn't resolve to anything. + let resolved = traverse_association_chain( + &model, + &terminating, + &effect.targets[0].assoc_traversal, + &mut r, + ) + .unwrap(); + assert!( + resolved.is_empty(), + "exfiltrate target shouldn't resolve before the C2Server link exists" + ); + } + + #[test] + fn wiper_exfiltrate_step_target_resolves_after_c2_link_from_wiper_asset() { + let (graph, mut model, exfiltrate_id) = wiper_attack_graph_with_wiper_asset(); + let wiper_asset = graph.nodes[exfiltrate_id].model_asset.unwrap(); + let effect = graph.nodes[exfiltrate_id] + .additive_model_effects + .as_ref() + .unwrap()[0] + .clone(); + let mut r = rng(); + + let infected_data = model.get_asset_by_name("InfectedData").unwrap().id; + let c2_server = model.get_asset_by_name("C2Server").unwrap().id; + model + .add_associated_assets(infected_data, "node", HashSet::from([c2_server])) + .unwrap(); + + // This target is an additive assoc op, so the instigating asset is + // the asset where the step is defined (the Wiper), not the base. + let resolved = traverse_association_chain( + &model, + &HashSet::from([wiper_asset]), + &effect.targets[0].assoc_traversal, + &mut r, + ) + .unwrap(); + assert_eq!(resolved, HashSet::from([infected_data])); + } + + // --- sample_size / apply_quantity_filter --- + + #[test] + fn sample_size_none_quantity_is_always_one() { + let mut r = rng(); + assert_eq!(sample_size(None, &mut r, Some(0)), 1); + assert_eq!(sample_size(None, &mut r, None), 1); + } + + #[test] + fn sample_size_exact_clamped_to_max_size() { + let mut r = rng(); + assert_eq!( + sample_size(Some(QuantityFilter::Exact(5)), &mut r, Some(2)), + 2 + ); + assert_eq!( + sample_size(Some(QuantityFilter::Exact(1)), &mut r, Some(2)), + 1 + ); + } + + #[test] + fn sample_size_range_is_within_clamped_bounds() { + let mut r = rng(); + for _ in 0..50 { + let size = sample_size(Some(QuantityFilter::Range(1, 10)), &mut r, Some(3)); + assert!((1..=3).contains(&size), "size {size} out of clamped range"); + } + } + + #[test] + fn apply_quantity_filter_exact_picks_requested_count() { + let objects: HashSet = (0..10).collect(); + let mut r = rng(); + let chosen = apply_quantity_filter(&objects, Some(QuantityFilter::Exact(3)), &mut r); + assert_eq!(chosen.len(), 3); + assert!(chosen.is_subset(&objects)); + } + + #[test] + fn apply_quantity_filter_clamps_to_available_size() { + let objects: HashSet = (0..2).collect(); + let mut r = rng(); + let chosen = apply_quantity_filter(&objects, Some(QuantityFilter::Exact(10)), &mut r); + assert_eq!(chosen, objects); + } + + // --- glob (transitive) and set-operation traversal, hand-built --- + + #[test] + fn glob_traversal_reaches_transitive_neighbors() { + // Device.receiveFrom/sendTo is symmetric, so a glob over "sendTo" + // starting from one device should reach every device transitively + // reachable via sendTo links, not just its direct neighbor. + let (_graph, mut model) = wiper_attack_graph(); + let a = model.get_asset_by_name("InfectedDevice").unwrap().id; + let b = model.get_asset_by_name("VulnerableDevice").unwrap().id; + // wiper_model.yml already links InfectedDevice <-> VulnerableDevice + // via sendTo/receiveFrom; add a third device only reachable through b. + let c = model + .add_asset( + "Device", + Some("ThirdDevice".to_string()), + None, + None, + None, + false, + ) + .unwrap(); + model + .add_associated_assets(b, "sendTo", HashSet::from([c])) + .unwrap(); + + let glob = AssocTraversalElem::Glob(GlobAssocTraversal { + pattern: wiper_sendto_chain(), + quantity_filter: None, + }); + let mut r = rng(); + let result = + traverse_association_chain(&model, &HashSet::from([a]), &[glob], &mut r).unwrap(); + assert!(result.contains(&b)); + assert!( + result.contains(&c), + "glob should reach c transitively through b" + ); + } + + fn wiper_sendto_chain() -> Vec { + vec![AssocTraversalElem::Traversal(AssocTraversal { + field_name: "sendTo".to_string(), + asset_filter: None, + quantity_filter: None, + })] + } + + #[test] + fn set_union_combines_both_sides() { + let (_graph, model) = wiper_attack_graph(); + let a = model.get_asset_by_name("InfectedDevice").unwrap().id; + let b = model.get_asset_by_name("C2Server").unwrap().id; + + // left: InfectedDevice.sendTo, right: InfectedDevice.receiveFrom - + // wiper_model.yml makes these disjoint (sendTo={C2Server, + // VulnerableDevice}, receiveFrom={VulnerableDevice}), so union + // should just be their combined set. + let set = AssocSet { + set_op: SetOperation::Union, + left: wiper_sendto_chain(), + right: vec![AssocTraversalElem::Traversal(AssocTraversal { + field_name: "receiveFrom".to_string(), + asset_filter: None, + quantity_filter: None, + })], + quantity_filter: None, + }; + let mut r = rng(); + let result = traverse_association_chain( + &model, + &HashSet::from([a]), + &[AssocTraversalElem::Set(set)], + &mut r, + ) + .unwrap(); + assert!(result.contains(&b)); + } + + #[test] + fn empty_chain_is_an_error_at_the_terminal_resolver() { + let (graph, model) = wiper_attack_graph(); + let infect_id = graph.full_name_to_node["InfectedDevice:infect"]; + let device_asset = graph.nodes[infect_id].model_asset.unwrap(); + let mut r = rng(); + + let result = parse_addition( + &model, + device_asset, + &HashSet::from([device_asset]), + &[], + &mut r, + ); + assert!(matches!(result, Err(AssocTraversalError::EmptyChain))); + } +} diff --git a/core/malsim-core/src/attack_surface.rs b/core/malsim-core/src/attack_surface.rs new file mode 100644 index 00000000..b42ceab5 --- /dev/null +++ b/core/malsim-core/src/attack_surface.rs @@ -0,0 +1,1025 @@ +//! Rust port of `python/malsim/mal_simulator/attack_surface.py` - see +//! `PORTING_NOTES.md` §5 Phase A5. +//! +//! `node_is_actionable` (`graph_utils.py`) stays Python per §2.4 - it +//! operates on a `NodePropertyRule` directly. Per A5's own description, +//! this module takes the *already-flattened* actionability id-set +//! instead: `actionable_steps: None` means "no rule configured" (every +//! node actionable, mirroring `node_is_actionable`'s `if agent_actionability` +//! being falsy), `Some(set)` means exactly the ids in `set` are actionable +//! (mirroring a flattened `NodePropertyRule.per_node()` result, which +//! already baked in its own per-node default). This module never +//! constructs or reads a `NodePropertyRule`. + +use crate::types::{NodeDenseMap, NodeSet}; +use std::collections::VecDeque; + +use maltoolbox_attackgraph::{AttackGraph, AttackGraphNodeId}; + +use crate::graph_utils::{ + node_is_live, node_is_necessary, node_is_traversable, sort_by_node_id, GraphUtilsError, +}; + +/// Port of `node_is_actionable`, operating on the already-flattened id-set +/// instead of a `NodePropertyRule` - see module docs. +fn node_is_actionable_flat(actionable_steps: Option<&NodeSet>, node_id: AttackGraphNodeId) -> bool { + match actionable_steps { + Some(steps) => steps.contains(&node_id), + None => true, + } +} + +fn causal_mode_is_effect(graph: &AttackGraph, node_id: AttackGraphNodeId) -> bool { + graph.nodes[node_id] + .causal_mode + .is_some_and(|m| m.as_str() == "effect") +} + +/// The children of `node_id` that are effects, in node-id order (`children` +/// is a std set with a per-instance random order, and the traversal below +/// is order-sensitive). +fn effect_children(graph: &AttackGraph, node_id: AttackGraphNodeId) -> Vec { + let mut children: Vec = graph.nodes[node_id] + .children + .iter() + .copied() + .filter(|&child_id| causal_mode_is_effect(graph, child_id)) + .collect(); + sort_by_node_id(graph, &mut children); + children +} + +/// Port of `get_effects_of_attack_step`. +/// +/// Python recomputes `has_visited = performed | set(effects)` fresh every +/// loop iteration; here `visited` is instead grown incrementally (starting +/// from `performed_nodes` + `attack_step_id`, gaining each newly-found +/// effect immediately) - `effects` only ever grows and nothing is ever +/// removed from `performed_nodes`, so at every check point `visited` holds +/// exactly the same members the Python union would have recomputed. Purely +/// an idiomatic/efficiency difference (no repeated set-union allocation per +/// iteration), not a behavior change - see `PORTING_NOTES.md` §2.7. +pub fn get_effects_of_attack_step( + graph: &AttackGraph, + attack_step_id: AttackGraphNodeId, + performed_nodes: &NodeSet, + impossible_attack_steps: &NodeSet, + enabled_defenses: &NodeSet, + necessity_per_node: &NodeDenseMap, +) -> Result { + let mut effects: NodeSet = NodeSet::default(); + if !node_is_live(graph, attack_step_id) { + // Only consider nodes that still exist in the attack graph. + return Ok(effects); + } + + let mut visited: NodeSet = performed_nodes.clone(); + visited.insert(attack_step_id); + + let mut potential_effects: VecDeque = + effect_children(graph, attack_step_id).into(); + + while let Some(effect_id) = potential_effects.pop_front() { + if visited.contains(&effect_id) { + continue; + } + if node_is_traversable( + graph, + effect_id, + &visited, + impossible_attack_steps, + enabled_defenses, + necessity_per_node, + )? { + visited.insert(effect_id); + effects.insert(effect_id); + potential_effects.extend(effect_children(graph, effect_id)); + } + } + + Ok(effects) +} + +/// Port of `get_attack_surface`. Takes `skip_compromised`/`skip_unnecessary` +/// directly (the two fields of `AttackSurfaceSettings` this function reads) +/// rather than a ported settings struct - full settings porting is A9's +/// job, same pattern as `graph_state::compute_initial_graph_state`. The +/// resulting flat argument list (mirroring the independent pieces of +/// `MalSimulatorState`/`AttackSurfaceSettings` the Python function reads +/// off `sim_state`/`settings`) trips clippy's default arg-count lint; +/// bundling three of them into a one-off struct for this function alone +/// would be an abstraction with no other caller, so the lint is silenced +/// here instead. +#[allow(clippy::too_many_arguments)] +pub fn get_attack_surface( + graph: &AttackGraph, + skip_compromised: bool, + skip_unnecessary: bool, + actionable_steps: Option<&NodeSet>, + performed_nodes: &NodeSet, + from_nodes: Option<&NodeSet>, + impossible_attack_steps: &NodeSet, + enabled_defenses: &NodeSet, + necessity_per_node: &NodeDenseMap, +) -> Result { + let from_nodes = from_nodes.unwrap_or(performed_nodes); + + // A performed node that deleted its own backing asset (a dyna-MAL + // model effect targeting `self`) is no longer live: the core always + // keeps a *live* node's children/parents correctly unlinked from + // anything removed, but a dead node's own `.children` is only the + // frozen pre-removal snapshot mal-toolbox keeps so `performed_nodes` + // can still hold a readable reference to it - never a source of new + // live actions. + let mut surface = NodeSet::default(); + // Children already evaluated (accepted or rejected): the filters are + // pure functions of the child, so each child is evaluated at most once + // even when several live parents share it. + let mut seen: NodeSet = + NodeSet::with_capacity_and_hasher(from_nodes.len() * 4, Default::default()); + for &parent_id in from_nodes { + if !node_is_live(graph, parent_id) { + continue; + } + for &node_id in &graph.nodes[parent_id].children { + if !seen.insert(node_id) { + continue; + } + // Nodes marked as effects are not actions/attacks. + if causal_mode_is_effect(graph, node_id) { + continue; + } + if skip_compromised && performed_nodes.contains(&node_id) { + continue; + } + if skip_unnecessary && !node_is_necessary(necessity_per_node, node_id)? { + continue; + } + if !node_is_actionable_flat(actionable_steps, node_id) { + continue; + } + if !node_is_traversable( + graph, + node_id, + performed_nodes, + impossible_attack_steps, + enabled_defenses, + necessity_per_node, + )? { + continue; + } + surface.insert(node_id); + } + } + + Ok(surface) +} + +/// Brings `surface` - the result of `get_attack_surface` for the previous +/// `performed_nodes`/`enabled_defenses` - up to date after +/// `new_performed_nodes` were performed and `new_enabled_defenses` enabled, +/// without rebuilding it. Returns the `(added, removed)` nodes. +/// +/// `performed_nodes`/`enabled_defenses` are the *updated* sets (already +/// holding the new nodes); the other arguments are as for +/// `get_attack_surface` and must be unchanged since `surface` was computed. +/// The result is then exactly what `get_attack_surface` would return. +/// +/// Why only these nodes need re-checking: a node's membership depends on +/// fixed inputs (graph, settings, impossible steps, necessity) plus +/// - whether it is performed itself (only read with `skip_compromised`), +/// - whether its parents are performed (`node_is_traversable`), +/// - whether its parents are enabled defenses (`node_is_blocked`). +/// +/// Performed nodes and enabled defenses only ever grow, so membership can +/// only change for the new performed nodes themselves (`skip_compromised` +/// only), their children, and the children of the new defenses. Nothing +/// reads whether a *parent* is in the surface, so no change cascades +/// further. Relies on `parents`/`children` being mirror images for live +/// nodes, which mal-toolbox maintains. +/// +/// Not valid across a dyna-MAL model effect (it can change the graph, +/// impossible steps and necessity): rebuild with `get_attack_surface` +/// then. +#[allow(clippy::too_many_arguments)] +pub fn update_attack_surface( + graph: &AttackGraph, + surface: &mut NodeSet, + skip_compromised: bool, + skip_unnecessary: bool, + actionable_steps: Option<&NodeSet>, + performed_nodes: &NodeSet, + new_performed_nodes: impl IntoIterator, + impossible_attack_steps: &NodeSet, + enabled_defenses: &NodeSet, + new_enabled_defenses: impl IntoIterator, + necessity_per_node: &NodeDenseMap, +) -> Result<(NodeSet, NodeSet), GraphUtilsError> { + let mut candidates = NodeSet::default(); + for node_id in new_performed_nodes { + if skip_compromised { + candidates.insert(node_id); + } + if node_is_live(graph, node_id) { + candidates.extend(graph.nodes[node_id].children.iter().copied()); + } + } + for node_id in new_enabled_defenses { + if node_is_live(graph, node_id) { + candidates.extend(graph.nodes[node_id].children.iter().copied()); + } + } + + let mut added = NodeSet::default(); + let mut removed = NodeSet::default(); + for node_id in candidates { + // Same checks, in the same order, as `get_attack_surface`'s loop + // body. Being a child of a live performed node is covered by + // `node_is_live` plus `node_is_traversable`'s "a parent is + // performed" check. + let in_surface = node_is_live(graph, node_id) + && !causal_mode_is_effect(graph, node_id) + && !(skip_compromised && performed_nodes.contains(&node_id)) + && !(skip_unnecessary && !node_is_necessary(necessity_per_node, node_id)?) + && node_is_actionable_flat(actionable_steps, node_id) + && node_is_traversable( + graph, + node_id, + performed_nodes, + impossible_attack_steps, + enabled_defenses, + necessity_per_node, + )?; + if in_surface { + if surface.insert(node_id) { + added.insert(node_id); + } + } else if surface.remove(&node_id) { + removed.insert(node_id); + } + } + Ok((added, removed)) +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::test_fixtures::{add_dummy_node, dummy_graph}; + use maltoolbox_language::graph::attack_step::CausalMode; + + fn mark_effect(graph: &mut AttackGraph, node_id: AttackGraphNodeId) { + graph.nodes[node_id].causal_mode = Some(CausalMode::Effect); + } + + // --- get_effects_of_attack_step --- + + #[test] + fn effects_dead_attack_step_returns_empty() { + let mut graph = dummy_graph(); + let step = add_dummy_node(&mut graph, "DummyOrAttackStep"); + graph.remove_node(step).unwrap(); + + let empty = NodeSet::default(); + let empty_map = NodeDenseMap::default(); + let effects = + get_effects_of_attack_step(&graph, step, &empty, &empty, &empty, &empty_map).unwrap(); + assert!(effects.is_empty()); + } + + #[test] + fn effects_follow_chain_of_effect_children_only() { + let mut graph = dummy_graph(); + let step = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let effect1 = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let effect2 = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let non_effect_child = add_dummy_node(&mut graph, "DummyOrAttackStep"); + + mark_effect(&mut graph, effect1); + mark_effect(&mut graph, effect2); + // non_effect_child keeps the default (action) causal mode. + + graph.nodes[step].children.insert(effect1); + graph.nodes[step].children.insert(non_effect_child); + graph.nodes[effect1].parents.insert(step); + graph.nodes[non_effect_child].parents.insert(step); + + graph.nodes[effect1].children.insert(effect2); + graph.nodes[effect2].parents.insert(effect1); + + let empty = NodeSet::default(); + let empty_map = NodeDenseMap::default(); + let effects = + get_effects_of_attack_step(&graph, step, &empty, &empty, &empty, &empty_map).unwrap(); + + // Only the effect-tagged descendants are collected, transitively - + // the action-mode child is never followed even though it's a + // child of `step`. + assert_eq!(effects, [effect1, effect2].into_iter().collect()); + } + + #[test] + fn effects_stop_at_already_visited_node() { + let mut graph = dummy_graph(); + let step = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let effect = add_dummy_node(&mut graph, "DummyOrAttackStep"); + mark_effect(&mut graph, effect); + + graph.nodes[step].children.insert(effect); + graph.nodes[effect].parents.insert(step); + + let performed: NodeSet = [effect].into_iter().collect(); + let empty_map = NodeDenseMap::default(); + let empty = NodeSet::default(); + + // `effect` is already performed, so it must not be re-added even + // though it's reachable as an effect child of `step`. + let effects = + get_effects_of_attack_step(&graph, step, &performed, &empty, &empty, &empty_map) + .unwrap(); + assert!(effects.is_empty()); + } + + #[test] + fn effects_do_not_cross_blocked_and_step() { + let mut graph = dummy_graph(); + let step = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let other_parent = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let effect = add_dummy_node(&mut graph, "DummyAndAttackStep"); + mark_effect(&mut graph, effect); + + graph.nodes[step].children.insert(effect); + graph.nodes[effect].parents.insert(step); + // `effect` is an `and` step with a second, unperformed necessary + // parent - not traversable yet, so it must not be collected. + graph.nodes[effect].parents.insert(other_parent); + + let necessity: NodeDenseMap<_> = [(step, true), (other_parent, true)].into_iter().collect(); + let empty = NodeSet::default(); + + let effects = + get_effects_of_attack_step(&graph, step, &empty, &empty, &empty, &necessity).unwrap(); + assert!(effects.is_empty()); + } + + // --- get_attack_surface --- + + fn surface( + graph: &AttackGraph, + skip_compromised: bool, + skip_unnecessary: bool, + actionable_steps: Option<&NodeSet>, + performed_nodes: &NodeSet, + necessity: &NodeDenseMap, + ) -> NodeSet { + let empty = NodeSet::default(); + get_attack_surface( + graph, + skip_compromised, + skip_unnecessary, + actionable_steps, + performed_nodes, + None, + &empty, + &empty, + necessity, + ) + .unwrap() + } + + #[test] + fn attack_surface_includes_traversable_action_children_of_performed_nodes() { + let mut graph = dummy_graph(); + let performed_node = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let child = add_dummy_node(&mut graph, "DummyOrAttackStep"); + graph.nodes[performed_node].children.insert(child); + graph.nodes[child].parents.insert(performed_node); + + let performed: NodeSet = [performed_node].into_iter().collect(); + let necessity: NodeDenseMap<_> = [(child, true)].into_iter().collect(); + + let result = surface(&graph, true, false, None, &performed, &necessity); + assert_eq!(result, [child].into_iter().collect()); + } + + #[test] + fn attack_surface_excludes_effect_children() { + let mut graph = dummy_graph(); + let performed_node = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let effect_child = add_dummy_node(&mut graph, "DummyOrAttackStep"); + mark_effect(&mut graph, effect_child); + graph.nodes[performed_node].children.insert(effect_child); + graph.nodes[effect_child].parents.insert(performed_node); + + let performed: NodeSet = [performed_node].into_iter().collect(); + let necessity: NodeDenseMap<_> = [(effect_child, true)].into_iter().collect(); + + let result = surface(&graph, true, false, None, &performed, &necessity); + assert!(result.is_empty()); + } + + #[test] + fn attack_surface_skip_compromised_excludes_already_performed_nodes() { + let mut graph = dummy_graph(); + let parent = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let child = add_dummy_node(&mut graph, "DummyOrAttackStep"); + graph.nodes[parent].children.insert(child); + graph.nodes[child].parents.insert(parent); + + // Both nodes already performed - with skip_compromised the already- + // performed child is excluded even though it's otherwise traversable. + let performed: NodeSet = [parent, child].into_iter().collect(); + let necessity: NodeDenseMap<_> = [(child, true)].into_iter().collect(); + + let with_skip = surface(&graph, true, false, None, &performed, &necessity); + assert!(with_skip.is_empty()); + + let without_skip = surface(&graph, false, false, None, &performed, &necessity); + assert_eq!(without_skip, [child].into_iter().collect()); + } + + #[test] + fn attack_surface_skip_unnecessary_excludes_unnecessary_nodes() { + let mut graph = dummy_graph(); + let parent = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let child = add_dummy_node(&mut graph, "DummyOrAttackStep"); + graph.nodes[parent].children.insert(child); + graph.nodes[child].parents.insert(parent); + + let performed: NodeSet = [parent].into_iter().collect(); + let necessity: NodeDenseMap<_> = [(child, false)].into_iter().collect(); + + let with_skip = surface(&graph, true, true, None, &performed, &necessity); + assert!(with_skip.is_empty()); + + let without_skip = surface(&graph, true, false, None, &performed, &necessity); + assert_eq!(without_skip, [child].into_iter().collect()); + } + + #[test] + fn attack_surface_respects_flattened_actionable_steps() { + let mut graph = dummy_graph(); + let parent = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let actionable_child = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let non_actionable_child = add_dummy_node(&mut graph, "DummyOrAttackStep"); + graph.nodes[parent].children.insert(actionable_child); + graph.nodes[parent].children.insert(non_actionable_child); + graph.nodes[actionable_child].parents.insert(parent); + graph.nodes[non_actionable_child].parents.insert(parent); + + let performed: NodeSet = [parent].into_iter().collect(); + let necessity: NodeDenseMap<_> = [(actionable_child, true), (non_actionable_child, true)] + .into_iter() + .collect(); + + // No rule at all (`None`) -> every node actionable. + let all_actionable = surface(&graph, true, false, None, &performed, &necessity); + assert_eq!( + all_actionable, + [actionable_child, non_actionable_child] + .into_iter() + .collect() + ); + + // A rule present -> only the ids explicitly in the set are actionable. + let actionable_set: NodeSet = [actionable_child].into_iter().collect(); + let restricted = surface( + &graph, + true, + false, + Some(&actionable_set), + &performed, + &necessity, + ); + assert_eq!(restricted, [actionable_child].into_iter().collect()); + } + + #[test] + fn attack_surface_from_nodes_overrides_performed_nodes_as_starting_point() { + let mut graph = dummy_graph(); + let old_performed = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let old_child = add_dummy_node(&mut graph, "DummyOrAttackStep"); + graph.nodes[old_performed].children.insert(old_child); + graph.nodes[old_child].parents.insert(old_performed); + + let new_node = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let new_child = add_dummy_node(&mut graph, "DummyOrAttackStep"); + graph.nodes[new_node].children.insert(new_child); + graph.nodes[new_child].parents.insert(new_node); + + let performed: NodeSet = [old_performed, new_node].into_iter().collect(); + let from_nodes: NodeSet = [new_node].into_iter().collect(); + let necessity: NodeDenseMap<_> = + [(old_child, true), (new_child, true)].into_iter().collect(); + let empty = NodeSet::default(); + + let result = get_attack_surface( + &graph, + true, + false, + None, + &performed, + Some(&from_nodes), + &empty, + &empty, + &necessity, + ) + .unwrap(); + + // Only `new_child` (child of `from_nodes`), not `old_child`. + assert_eq!(result, [new_child].into_iter().collect()); + } + + #[test] + fn attack_surface_excludes_children_of_non_live_from_nodes() { + let mut graph = dummy_graph(); + let dead_parent = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let child = add_dummy_node(&mut graph, "DummyOrAttackStep"); + graph.nodes[dead_parent].children.insert(child); + graph.nodes[child].parents.insert(dead_parent); + + let performed: NodeSet = [dead_parent].into_iter().collect(); + let necessity: NodeDenseMap<_> = [(child, true)].into_iter().collect(); + + graph.remove_node(dead_parent).unwrap(); + + // `dead_parent` no longer exists in the graph, so its (frozen, + // pre-removal) `.children` snapshot must not be a source of new + // attack-surface nodes. + let result = surface(&graph, true, false, None, &performed, &necessity); + assert!(result.is_empty()); + } + + /// Port of the property `tests/test_attacker.py::test_attack_surface_traininglang` + /// asserted, on a hand-built reduction of the trainingLang scenario + /// (`traininglang_scenario.yml`: entry points `User:3:phishing` and + /// `Host:0:connect`) instead of the scenario fixture itself (needs + /// Phase C's not-yet-ported scenario-YAML loader to build in Rust). + /// Enabling defenses must shrink the attack surface, with necessity + /// recomputed per phase, mirroring the simulator's recompute after a + /// defender step. Settings mirror `AttackSurfaceSettings` defaults + /// (`skip_compromised=True`, `skip_unnecessary=False`). + #[test] + fn attack_surface_shrinks_as_defenses_are_enabled() { + let mut graph = dummy_graph(); + // User:3:phishing / Host:0:connect - the scenario's entry points. + let phishing = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let connect = add_dummy_node(&mut graph, "DummyAndAttackStep"); + // Host:0:authenticate - an `or` step the attacker hasn't reached. + let auth = add_dummy_node(&mut graph, "DummyOrAttackStep"); + // User:3:notPresent / Host:0:notPresent. + let user_np = add_dummy_node(&mut graph, "DummyDefenseAttackStep"); + let host_np = add_dummy_node(&mut graph, "DummyDefenseAttackStep"); + // User:3:compromise (`and`: phishing + user notPresent). + let compromise = add_dummy_node(&mut graph, "DummyAndAttackStep"); + // Host:0:access (`and`: connect + host notPresent + authenticate). + let access = add_dummy_node(&mut graph, "DummyAndAttackStep"); + + let link = |graph: &mut AttackGraph, parent, child| { + graph.nodes[parent].children.insert(child); + graph.nodes[child].parents.insert(parent); + }; + link(&mut graph, phishing, compromise); + link(&mut graph, user_np, compromise); + link(&mut graph, connect, access); + link(&mut graph, host_np, access); + link(&mut graph, auth, access); + + let performed: NodeSet = [phishing, connect].into_iter().collect(); + let no_impossible = NodeSet::default(); + + let surface_with = |impossible: &NodeSet, enabled: &NodeSet| { + let necessity = crate::necessity::calculate_necessity(&graph, enabled).unwrap(); + get_attack_surface( + &graph, true, false, None, &performed, None, impossible, enabled, &necessity, + ) + .unwrap() + }; + + // No defenses enabled: `compromise` is reachable (its disabled + // defense parent is unnecessary, so not required), `access` is not + // (its necessary `auth` parent is unperformed). + let none_enabled = NodeSet::default(); + assert_eq!( + surface_with(&no_impossible, &none_enabled), + [compromise].into_iter().collect() + ); + + // "This wont help, already compromised" - enabling Host:0:notPresent + // only blocks `access`, which wasn't on the surface anyway. + let host_enabled: NodeSet = [host_np].into_iter().collect(); + assert_eq!( + surface_with(&no_impossible, &host_enabled), + [compromise].into_iter().collect() + ); + + // "This should block the attack from further propagating" - + // enabling User:3:notPresent too blocks `compromise`, leaving an + // empty surface and a terminated attacker. + let both_enabled: NodeSet = [host_np, user_np].into_iter().collect(); + let blocked_surface = surface_with(&no_impossible, &both_enabled); + assert!(blocked_surface.is_empty()); + assert!(crate::attacker_step::attacker_is_terminated( + &blocked_surface, + &NodeSet::default(), + &performed + )); + + // Rust-only extra: an impossible attack step is excluded the same + // way an enabled-defense-blocked one is. + let impossible: NodeSet = [compromise].into_iter().collect(); + assert!(surface_with(&impossible, &none_enabled).is_empty()); + } + + // --- update_attack_surface --- + + fn link(graph: &mut AttackGraph, parent: AttackGraphNodeId, child: AttackGraphNodeId) { + graph.nodes[parent].children.insert(child); + graph.nodes[child].parents.insert(parent); + } + + fn set(ids: &[AttackGraphNodeId]) -> NodeSet { + ids.iter().copied().collect() + } + + /// Every node of `graph` necessary except `unnecessary`. + fn necessity_except( + graph: &AttackGraph, + unnecessary: &[AttackGraphNodeId], + ) -> NodeDenseMap { + graph + .nodes + .keys() + .map(|id| (id, !unnecessary.contains(&id))) + .collect() + } + + /// Runs `update_attack_surface` (no impossible steps) and checks the + /// result against a full `get_attack_surface` rebuild. + #[allow(clippy::too_many_arguments)] + fn update( + graph: &AttackGraph, + surface: &mut NodeSet, + skip_compromised: bool, + skip_unnecessary: bool, + actionable_steps: Option<&NodeSet>, + performed: &NodeSet, + new_performed: &[AttackGraphNodeId], + enabled: &NodeSet, + new_enabled: &[AttackGraphNodeId], + necessity: &NodeDenseMap, + ) -> (NodeSet, NodeSet) { + let empty = NodeSet::default(); + let delta = update_attack_surface( + graph, + surface, + skip_compromised, + skip_unnecessary, + actionable_steps, + performed, + new_performed.iter().copied(), + &empty, + enabled, + new_enabled.iter().copied(), + necessity, + ) + .unwrap(); + let full = get_attack_surface( + graph, + skip_compromised, + skip_unnecessary, + actionable_steps, + performed, + None, + &empty, + enabled, + necessity, + ) + .unwrap(); + assert_eq!( + *surface, full, + "incremental surface differs from full rebuild" + ); + delta + } + + #[test] + fn update_adds_or_child_of_new_performed_node() { + let mut graph = dummy_graph(); + let parent = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let child = add_dummy_node(&mut graph, "DummyOrAttackStep"); + link(&mut graph, parent, child); + let necessity = necessity_except(&graph, &[]); + let empty = NodeSet::default(); + + for skip_compromised in [false, true] { + let mut surface = NodeSet::default(); + let (added, removed) = update( + &graph, + &mut surface, + skip_compromised, + false, + None, + &set(&[parent]), + &[parent], + &empty, + &[], + &necessity, + ); + assert_eq!(surface, set(&[child])); + assert_eq!(added, set(&[child])); + assert!(removed.is_empty()); + } + } + + #[test] + fn update_adds_and_child_only_once_all_necessary_parents_are_performed() { + let mut graph = dummy_graph(); + let a = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let b = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let unnecessary = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let child = add_dummy_node(&mut graph, "DummyAndAttackStep"); + link(&mut graph, a, child); + link(&mut graph, b, child); + link(&mut graph, unnecessary, child); + let necessity = necessity_except(&graph, &[unnecessary]); + let empty = NodeSet::default(); + + let mut surface = NodeSet::default(); + let (added, _) = update( + &graph, + &mut surface, + true, + false, + None, + &set(&[a]), + &[a], + &empty, + &[], + &necessity, + ); + assert!(added.is_empty(), "`b` is still missing"); + + // `unnecessary` is never performed, and isn't required. + let (added, _) = update( + &graph, + &mut surface, + true, + false, + None, + &set(&[a, b]), + &[b], + &empty, + &[], + &necessity, + ); + assert_eq!(added, set(&[child])); + } + + #[test] + fn update_removes_and_child_blocked_by_new_defense() { + let mut graph = dummy_graph(); + let parent = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let defense = add_dummy_node(&mut graph, "DummyDefenseAttackStep"); + let child = add_dummy_node(&mut graph, "DummyAndAttackStep"); + let unaffected = add_dummy_node(&mut graph, "DummyOrAttackStep"); + link(&mut graph, parent, child); + link(&mut graph, defense, child); + link(&mut graph, parent, unaffected); + // A disabled defense parent is unnecessary, so not required. + let necessity = necessity_except(&graph, &[defense]); + let performed = set(&[parent]); + + for skip_compromised in [false, true] { + let mut surface = NodeSet::default(); + update( + &graph, + &mut surface, + skip_compromised, + false, + None, + &performed, + &[parent], + &NodeSet::default(), + &[], + &necessity, + ); + assert_eq!(surface, set(&[child, unaffected])); + + let (added, removed) = update( + &graph, + &mut surface, + skip_compromised, + false, + None, + &performed, + &[], + &set(&[defense]), + &[defense], + &necessity, + ); + assert!(added.is_empty()); + assert_eq!(removed, set(&[child])); + assert_eq!(surface, set(&[unaffected])); + } + } + + #[test] + fn update_or_child_with_an_unblocking_parent_survives_new_defense() { + let mut graph = dummy_graph(); + let parent = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let defense = add_dummy_node(&mut graph, "DummyDefenseAttackStep"); + let child = add_dummy_node(&mut graph, "DummyOrAttackStep"); + link(&mut graph, parent, child); + link(&mut graph, defense, child); + let necessity = necessity_except(&graph, &[]); + let performed = set(&[parent]); + + let mut surface = NodeSet::default(); + update( + &graph, + &mut surface, + true, + false, + None, + &performed, + &[parent], + &NodeSet::default(), + &[], + &necessity, + ); + // An `or` step is blocked only when every parent blocks it. + let (added, removed) = update( + &graph, + &mut surface, + true, + false, + None, + &performed, + &[], + &set(&[defense]), + &[defense], + &necessity, + ); + assert!(added.is_empty() && removed.is_empty()); + assert_eq!(surface, set(&[child])); + } + + #[test] + fn update_performed_node_leaves_surface_only_with_skip_compromised() { + let mut graph = dummy_graph(); + let parent = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let child = add_dummy_node(&mut graph, "DummyOrAttackStep"); + link(&mut graph, parent, child); + let necessity = necessity_except(&graph, &[]); + let empty = NodeSet::default(); + + for skip_compromised in [false, true] { + let mut surface = NodeSet::default(); + update( + &graph, + &mut surface, + skip_compromised, + false, + None, + &set(&[parent]), + &[parent], + &empty, + &[], + &necessity, + ); + let (added, removed) = update( + &graph, + &mut surface, + skip_compromised, + false, + None, + &set(&[parent, child]), + &[child], + &empty, + &[], + &necessity, + ); + assert!(added.is_empty()); + if skip_compromised { + assert_eq!(removed, set(&[child])); + assert!(surface.is_empty()); + } else { + assert!(removed.is_empty()); + assert_eq!(surface, set(&[child])); + } + } + } + + #[test] + fn update_applies_effect_actionability_and_necessity_filters() { + let mut graph = dummy_graph(); + let parent = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let effect = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let not_actionable = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let unnecessary = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let plain = add_dummy_node(&mut graph, "DummyOrAttackStep"); + mark_effect(&mut graph, effect); + for child in [effect, not_actionable, unnecessary, plain] { + link(&mut graph, parent, child); + } + let necessity = necessity_except(&graph, &[unnecessary]); + let actionable = set(&[parent, effect, unnecessary, plain]); + let empty = NodeSet::default(); + + let mut surface = NodeSet::default(); + update( + &graph, + &mut surface, + true, + true, + Some(&actionable), + &set(&[parent]), + &[parent], + &empty, + &[], + &necessity, + ); + assert_eq!(surface, set(&[plain])); + + // Without `skip_unnecessary` the unnecessary node is kept. + let mut surface = NodeSet::default(); + update( + &graph, + &mut surface, + true, + false, + Some(&actionable), + &set(&[parent]), + &[parent], + &empty, + &[], + &necessity, + ); + assert_eq!(surface, set(&[unnecessary, plain])); + } + + #[test] + fn update_leaves_existing_members_out_of_the_delta() { + let mut graph = dummy_graph(); + let a = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let b = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let shared = add_dummy_node(&mut graph, "DummyOrAttackStep"); + link(&mut graph, a, shared); + link(&mut graph, b, shared); + let necessity = necessity_except(&graph, &[]); + let empty = NodeSet::default(); + + let mut surface = NodeSet::default(); + update( + &graph, + &mut surface, + false, + false, + None, + &set(&[a]), + &[a], + &empty, + &[], + &necessity, + ); + let (added, removed) = update( + &graph, + &mut surface, + false, + false, + None, + &set(&[a, b]), + &[b], + &empty, + &[], + &necessity, + ); + assert!(added.is_empty() && removed.is_empty()); + assert_eq!(surface, set(&[shared])); + } + + #[test] + fn update_ignores_children_of_removed_nodes() { + let mut graph = dummy_graph(); + let parent = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let child = add_dummy_node(&mut graph, "DummyOrAttackStep"); + link(&mut graph, parent, child); + let necessity = necessity_except(&graph, &[]); + graph.remove_node(parent).unwrap(); + + let mut surface = NodeSet::default(); + let (added, removed) = update( + &graph, + &mut surface, + false, + false, + None, + &set(&[parent]), + &[parent], + &NodeSet::default(), + &[], + &necessity, + ); + assert!(added.is_empty() && removed.is_empty() && surface.is_empty()); + } +} diff --git a/core/malsim-core/src/attacker_step.rs b/core/malsim-core/src/attacker_step.rs new file mode 100644 index 00000000..b876049e --- /dev/null +++ b/core/malsim-core/src/attacker_step.rs @@ -0,0 +1,724 @@ +//! Rust port of `python/malsim/mal_simulator/attacker_step.py` - see +//! `PORTING_NOTES.md` §5 Phase A7. +//! +//! `state_query.py::node_ttc_value` is pulled forward into this module (as +//! the private `resolve_ttc_value`, below) because `attempt_attacker_step` +//! is its only caller in the hot loop - same precedent as A3 pulling +//! `TtcMode` forward from `config/sim_settings.py` into `graph_state.rs`. +//! No `AttackerState` exists on the Rust side yet (full per-agent runtime +//! state is A9's job - see `PORTING_NOTES.md` §3/§5), so the two maps +//! `node_ttc_value` reads (`attacker_state.ttc_values` - an agent-level +//! override - and `sim_state.graph_state.ttc_values` - the graph-level +//! default computed once at `reset()` by `graph_state:: +//! compute_initial_graph_state`) are passed in directly, continuing A5/ +//! A6's "already-flattened" pattern rather than a ported settings/state +//! struct. + +use crate::types::{NodeDenseMap, NodeMap, NodeSet}; +use std::fmt; + +use maltoolbox_attackgraph::{AttackGraph, AttackGraphNodeId}; +use rand::Rng; + +use crate::attack_surface::get_effects_of_attack_step; +use crate::graph_state::{resolve_ttc_dist, GraphStateError, TtcMode}; +use crate::graph_utils::{node_is_live, node_is_traversable, sort_by_node_id, GraphUtilsError}; +use crate::ttc::TtcDist; + +#[derive(Debug, Clone, PartialEq)] +pub enum AttackerStepError { + GraphUtils(GraphUtilsError), + TtcDist(GraphStateError), + /// Mirrors Python's bare `assert node in attacker_state.sim_state. + /// graph_state.ttc_values` in `state_query.py::node_ttc_value`, + /// reached only in `ExpectedValue`/`PreSample` mode when neither the + /// agent-level override nor the graph-level default has a value for + /// this node. + MissingTtcValue(AttackGraphNodeId), + /// Mirrors Python's `assert node == sim_state.attack_graph.nodes[node.id]` + /// in `attacker_step` - translated to "this node id no longer exists + /// in the graph" since the Rust side works with ids, not stale Python + /// object references. See `PORTING_NOTES.md` §10 for why this is the + /// chosen translation. + NodeNotInGraph(AttackGraphNodeId), +} + +impl From for AttackerStepError { + fn from(e: GraphUtilsError) -> Self { + AttackerStepError::GraphUtils(e) + } +} + +impl From for AttackerStepError { + fn from(e: GraphStateError) -> Self { + AttackerStepError::TtcDist(e) + } +} + +impl fmt::Display for AttackerStepError { + fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { + match self { + AttackerStepError::GraphUtils(e) => write!(f, "{e}"), + AttackerStepError::TtcDist(e) => write!(f, "{e}"), + AttackerStepError::MissingTtcValue(id) => { + write!(f, "node {id:?} does not have a ttc value") + } + AttackerStepError::NodeNotInGraph(id) => write!( + f, + "tried to step a node ({id:?}) that is not part of this simulator's attack graph" + ), + } + } +} + +impl std::error::Error for AttackerStepError {} + +/// Port of `attacker_is_terminated`. Takes the attacker's current action +/// surface/goals/performed-nodes directly rather than an `AttackerState` - +/// no such type exists on the Rust side yet (A9's job). An empty `goals` +/// set mirrors Python's falsy-empty-frozenset check (`if goals:` - the +/// `AttackerSettings.goals` field defaults to an empty `frozenset`, never +/// `None`). +pub fn attacker_is_terminated( + action_surface: &NodeSet, + goals: &NodeSet, + performed_nodes: &NodeSet, +) -> bool { + if action_surface.is_empty() { + return true; + } + if !goals.is_empty() { + return goals.is_subset(performed_nodes); + } + false +} + +/// Pulled-forward port of `state_query.py::node_ttc_value` - see module +/// docs. Returns `None` exactly where Python's assert would fire. +fn resolve_ttc_value( + node_id: AttackGraphNodeId, + ttc_value_overrides: Option<&NodeMap>, + graph_ttc_values: &NodeMap, +) -> Option { + ttc_value_overrides + .and_then(|m| m.get(&node_id)) + .or_else(|| graph_ttc_values.get(&node_id)) + .copied() +} + +/// Port of `attempt_attacker_step`. +/// +/// `num_attempts_before` mirrors `agent.num_attempts[node]` (the attempt +/// count *before* this attempt) - defaulting a missing entry to 0 is the +/// caller's job (mirrors the `dict.fromkeys(attack_graph.attack_steps, 0)` +/// every node is seeded with at reset, per `attacker_state_factories.py`). +/// +/// `ttc_dist` is resolved **unconditionally**, even in `Disabled` mode +/// where its result goes unused - ported as-is from Python's own +/// ordering (`ttc_dist = ...` happens before the `if ttc_mode == +/// TTCMode.DISABLED` check), not simplified into a lazier short-circuit, +/// so a malformed per-node TTC dict still surfaces as an error in +/// `Disabled` mode exactly like it does in Python. +/// +/// **The `ExpectedValue`/`PreSample` branch's `num_attempts + 1 >= +/// ttc_value` comparison is two attempts ahead of what the variable name +/// suggests, ported as-is - not a transcription bug.** Python's own +/// `num_attempts = agent.num_attempts[node] + 1` already adds one before +/// this branch adds a second, so the comparison actually reads +/// `agent.num_attempts[node] + 2 >= ttc_value`. See `PORTING_NOTES.md` +/// §10 for the full callout. +#[allow(clippy::too_many_arguments)] +pub fn attempt_attacker_step( + graph: &AttackGraph, + rng: &mut impl Rng, + ttc_mode: TtcMode, + node_id: AttackGraphNodeId, + num_attempts_before: u64, + ttc_dist_override: Option<&TtcDist>, + ttc_value_overrides: Option<&NodeMap>, + graph_ttc_values: &NodeMap, +) -> Result { + let node = &graph.nodes[node_id]; + let num_attempts = num_attempts_before + 1; + let ttc_dist = resolve_ttc_dist(node, ttc_dist_override)?; + + match ttc_mode { + TtcMode::Disabled => Ok(true), + TtcMode::EffortBasedPerStepSample => { + Ok(ttc_dist.attempt_ttc_with_effort(num_attempts, rng)) + } + TtcMode::PerStepSample => Ok(ttc_dist.sample_value(rng) <= 1.0), + TtcMode::ExpectedValue | TtcMode::PreSample => { + let ttc_value = resolve_ttc_value(node_id, ttc_value_overrides, graph_ttc_values) + .ok_or(AttackerStepError::MissingTtcValue(node_id))?; + Ok((num_attempts + 1) as f64 >= ttc_value) + } + } +} + +/// Port of `attacker_step_effects`. +pub fn attacker_step_effects( + graph: &AttackGraph, + node_id: AttackGraphNodeId, + performed_nodes: &NodeSet, + impossible_attack_steps: &NodeSet, + enabled_defenses: &NodeSet, + necessity_per_node: &NodeDenseMap, +) -> Result, GraphUtilsError> { + let effects = get_effects_of_attack_step( + graph, + node_id, + performed_nodes, + impossible_attack_steps, + enabled_defenses, + necessity_per_node, + )?; + // The effects come out of a hash set; compromise (and, in the dyna + // path, model-effect/RNG) order must not depend on its layout. + let mut effects: Vec = effects.into_iter().collect(); + sort_by_node_id(graph, &mut effects); + Ok(effects) +} + +/// Port of `attacker_step`. +/// +/// **Entry points bypass both the action-surface and traversability +/// checks entirely** (`can_compromise = true` unconditionally) - ported +/// as-is from Python's own `# TODO: should this actually be the case?` +/// comment on this exact branch, not tightened into a stricter check. +/// +/// Like Python, a node that fails the `can_compromise` check is silently +/// skipped (Python logs a warning) rather than treated as an error - only +/// the graph-membership check below is a hard failure. +#[allow(clippy::too_many_arguments)] +pub fn attacker_step( + graph: &AttackGraph, + rng: &mut impl Rng, + ttc_mode: TtcMode, + nodes: &[AttackGraphNodeId], + entry_points: &NodeSet, + action_surface: &NodeSet, + performed_nodes: &NodeSet, + num_attempts: &NodeMap, + ttc_dist_overrides: Option<&NodeMap>, + ttc_value_overrides: Option<&NodeMap>, + graph_ttc_values: &NodeMap, + impossible_attack_steps: &NodeSet, + enabled_defenses: &NodeSet, + necessity_per_node: &NodeDenseMap, +) -> Result<(Vec, Vec), AttackerStepError> { + let mut successful_compromises = Vec::new(); + let mut attempted_compromises = Vec::new(); + + for &node_id in nodes { + if !node_is_live(graph, node_id) { + return Err(AttackerStepError::NodeNotInGraph(node_id)); + } + + let can_compromise = if entry_points.contains(&node_id) { + true + } else { + action_surface.contains(&node_id) + && node_is_traversable( + graph, + node_id, + performed_nodes, + impossible_attack_steps, + enabled_defenses, + necessity_per_node, + )? + }; + + if !can_compromise { + continue; + } + + let num_attempts_before = num_attempts.get(&node_id).copied().unwrap_or(0); + let ttc_override = ttc_dist_overrides.and_then(|m| m.get(&node_id)); + + let succeeded = attempt_attacker_step( + graph, + rng, + ttc_mode, + node_id, + num_attempts_before, + ttc_override, + ttc_value_overrides, + graph_ttc_values, + )?; + + if succeeded { + successful_compromises.push(node_id); + let effects = attacker_step_effects( + graph, + node_id, + performed_nodes, + impossible_attack_steps, + enabled_defenses, + necessity_per_node, + )?; + successful_compromises.extend(effects); + } else { + attempted_compromises.push(node_id); + } + } + + Ok((successful_compromises, attempted_compromises)) +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::test_fixtures::{add_dummy_node, dummy_graph}; + use rand::rngs::StdRng; + use rand::SeedableRng; + + fn rng() -> StdRng { + StdRng::seed_from_u64(1) + } + + // --- attacker_is_terminated --- + + #[test] + fn terminated_when_action_surface_empty() { + let empty = NodeSet::default(); + assert!(attacker_is_terminated(&empty, &empty, &empty)); + } + + #[test] + fn not_terminated_without_goals_and_nonempty_surface() { + let mut graph = dummy_graph(); + let node = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let surface: NodeSet = [node].into_iter().collect(); + let empty = NodeSet::default(); + assert!(!attacker_is_terminated(&surface, &empty, &empty)); + } + + #[test] + fn terminated_when_all_goals_performed() { + let mut graph = dummy_graph(); + let node = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let goal = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let surface: NodeSet = [node].into_iter().collect(); + let goals: NodeSet = [goal].into_iter().collect(); + let performed: NodeSet = [goal].into_iter().collect(); + assert!(attacker_is_terminated(&surface, &goals, &performed)); + } + + #[test] + fn not_terminated_when_some_goals_unperformed() { + let mut graph = dummy_graph(); + let node = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let goal1 = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let goal2 = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let surface: NodeSet = [node].into_iter().collect(); + let goals: NodeSet = [goal1, goal2].into_iter().collect(); + let performed: NodeSet = [goal1].into_iter().collect(); + assert!(!attacker_is_terminated(&surface, &goals, &performed)); + } + + // --- attempt_attacker_step --- + + #[test] + fn attempt_disabled_mode_always_succeeds() { + let mut graph = dummy_graph(); + let node = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let empty_map = NodeMap::default(); + let mut r = rng(); + + let result = attempt_attacker_step( + &graph, + &mut r, + TtcMode::Disabled, + node, + 0, + None, + None, + &empty_map, + ) + .unwrap(); + assert!(result); + } + + #[test] + fn attempt_disabled_mode_still_surfaces_malformed_ttc_dict() { + // Ported subtlety: Python resolves `ttc_dist` before checking + // `ttc_mode == DISABLED`, so a malformed per-node `ttc` dict still + // errors out even though the result would never be used. + let mut graph = dummy_graph(); + let node = add_dummy_node(&mut graph, "DummyOrAttackStep"); + graph.nodes[node].ttc = Some(serde_json::json!({})); + let empty_map = NodeMap::default(); + let mut r = rng(); + + let result = attempt_attacker_step( + &graph, + &mut r, + TtcMode::Disabled, + node, + 0, + None, + None, + &empty_map, + ); + assert!(matches!(result, Err(AttackerStepError::TtcDist(_)))); + } + + #[test] + fn attempt_effort_based_mode_uses_ttc_dist_success_probability() { + let mut graph = dummy_graph(); + let node = add_dummy_node(&mut graph, "DummyOrAttackStep"); + // Default dist for "or" steps is "Instant" (Bernoulli(1.0)): + // success_probability(effort) is 1.0 for any effort >= 1. + let empty_map = NodeMap::default(); + let mut r = rng(); + + let result = attempt_attacker_step( + &graph, + &mut r, + TtcMode::EffortBasedPerStepSample, + node, + 0, + None, + None, + &empty_map, + ) + .unwrap(); + assert!(result); + } + + #[test] + fn attempt_per_step_sample_mode_uses_sampled_value() { + let mut graph = dummy_graph(); + let node = add_dummy_node(&mut graph, "DummyOrAttackStep"); + // Default "Instant" dist always samples 1.0, so <= 1 is always true. + let empty_map = NodeMap::default(); + let mut r = rng(); + + let result = attempt_attacker_step( + &graph, + &mut r, + TtcMode::PerStepSample, + node, + 0, + None, + None, + &empty_map, + ) + .unwrap(); + assert!(result); + } + + #[test] + fn attempt_expected_value_mode_requires_a_ttc_value() { + let mut graph = dummy_graph(); + let node = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let empty_map = NodeMap::default(); + let mut r = rng(); + + let result = attempt_attacker_step( + &graph, + &mut r, + TtcMode::ExpectedValue, + node, + 0, + None, + None, + &empty_map, + ); + assert!(matches!(result, Err(AttackerStepError::MissingTtcValue(_)))); + } + + #[test] + fn attempt_expected_value_mode_agent_override_wins_over_graph_value() { + let mut graph = dummy_graph(); + let node = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let graph_values: NodeMap<_> = [(node, 100.0)].into_iter().collect(); + let agent_override: NodeMap<_> = [(node, 1.0)].into_iter().collect(); + let mut r = rng(); + + // Agent override (1.0) is used instead of the graph value (100.0): + // with num_attempts_before=0, `num_attempts + 1 == 2 >= 1.0` succeeds. + let result = attempt_attacker_step( + &graph, + &mut r, + TtcMode::ExpectedValue, + node, + 0, + None, + Some(&agent_override), + &graph_values, + ) + .unwrap(); + assert!(result); + } + + #[test] + fn attempt_expected_value_mode_comparison_is_two_attempts_ahead() { + // Documents/locks in the "two attempts ahead" oddity: with + // num_attempts_before=0, the comparison is `0 + 2 >= ttc_value`. + // A ttc_value of 2.0 succeeds on the very first attempt even + // though no attempt has happened yet. + let mut graph = dummy_graph(); + let node = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let graph_values: NodeMap<_> = [(node, 2.0)].into_iter().collect(); + let mut r = rng(); + + let result = attempt_attacker_step( + &graph, + &mut r, + TtcMode::ExpectedValue, + node, + 0, + None, + None, + &graph_values, + ) + .unwrap(); + assert!(result); + + // A ttc_value just above that boundary (2.1) does not succeed yet. + let graph_values: NodeMap<_> = [(node, 2.1)].into_iter().collect(); + let mut r = rng(); + let result = attempt_attacker_step( + &graph, + &mut r, + TtcMode::ExpectedValue, + node, + 0, + None, + None, + &graph_values, + ) + .unwrap(); + assert!(!result); + } + + // --- attacker_step --- + + fn empty_sets() -> (NodeSet, NodeSet, NodeSet) { + (NodeSet::default(), NodeSet::default(), NodeSet::default()) + } + + #[test] + fn step_fails_node_not_in_graph() { + let mut graph = dummy_graph(); + let node = add_dummy_node(&mut graph, "DummyOrAttackStep"); + graph.remove_node(node).unwrap(); + + let (empty_set, empty_set2, empty_set3) = empty_sets(); + let empty_map_u64: NodeMap = NodeMap::default(); + let empty_map_f64: NodeMap = NodeMap::default(); + let empty_map_bool: NodeDenseMap = NodeDenseMap::default(); + let mut r = rng(); + + let result = attacker_step( + &graph, + &mut r, + TtcMode::Disabled, + &[node], + &empty_set, + &empty_set2, + &empty_set3, + &empty_map_u64, + None, + None, + &empty_map_f64, + &empty_set, + &empty_set, + &empty_map_bool, + ); + assert!(matches!(result, Err(AttackerStepError::NodeNotInGraph(_)))); + } + + #[test] + fn step_skips_node_outside_action_surface_and_not_entry_point() { + let mut graph = dummy_graph(); + let node = add_dummy_node(&mut graph, "DummyOrAttackStep"); + + let empty_set: NodeSet = NodeSet::default(); + let empty_map_u64: NodeMap = NodeMap::default(); + let empty_map_f64: NodeMap = NodeMap::default(); + let empty_map_bool: NodeDenseMap = NodeDenseMap::default(); + let mut r = rng(); + + // Not in entry_points, not in action_surface -> skipped, no error. + let (successful, attempted) = attacker_step( + &graph, + &mut r, + TtcMode::Disabled, + &[node], + &empty_set, + &empty_set, + &empty_set, + &empty_map_u64, + None, + None, + &empty_map_f64, + &empty_set, + &empty_set, + &empty_map_bool, + ) + .unwrap(); + assert!(successful.is_empty()); + assert!(attempted.is_empty()); + } + + /// Port of the first case of the old + /// `tests/test_mal_simulator.py::test_attacker_step` ("Can not attack + /// the notPresent step"): a defense node passed as an attacker action, + /// neither on the action surface nor an entry point, is skipped. + #[test] + fn step_skips_defense_node_outside_action_surface_and_not_entry_point() { + let mut graph = dummy_graph(); + let defense = add_dummy_node(&mut graph, "DummyDefenseAttackStep"); + + let empty_set: NodeSet = NodeSet::default(); + let empty_map_u64: NodeMap = NodeMap::default(); + let empty_map_f64: NodeMap = NodeMap::default(); + let empty_map_bool: NodeDenseMap = NodeDenseMap::default(); + let mut r = rng(); + + let (successful, attempted) = attacker_step( + &graph, + &mut r, + TtcMode::Disabled, + &[defense], + &empty_set, + &empty_set, + &empty_set, + &empty_map_u64, + None, + None, + &empty_map_f64, + &empty_set, + &empty_set, + &empty_map_bool, + ) + .unwrap(); + assert!(successful.is_empty()); + assert!(attempted.is_empty()); + } + + #[test] + fn step_entry_point_bypasses_action_surface_and_traversability() { + let mut graph = dummy_graph(); + // An "and" step with an unperformed necessary parent is not + // traversable - but entry points bypass that check entirely. + let parent = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let node = add_dummy_node(&mut graph, "DummyAndAttackStep"); + graph.nodes[node].parents.insert(parent); + graph.nodes[parent].children.insert(node); + + let entry_points: NodeSet = [node].into_iter().collect(); + let empty_set: NodeSet = NodeSet::default(); + let empty_map_u64: NodeMap = NodeMap::default(); + let empty_map_f64: NodeMap = NodeMap::default(); + let necessity: NodeDenseMap<_> = [(node, true), (parent, true)].into_iter().collect(); + let mut r = rng(); + + let (successful, attempted) = attacker_step( + &graph, + &mut r, + TtcMode::Disabled, + &[node], + &entry_points, + &empty_set, + &empty_set, + &empty_map_u64, + None, + None, + &empty_map_f64, + &empty_set, + &empty_set, + &necessity, + ) + .unwrap(); + assert_eq!(successful, vec![node]); + assert!(attempted.is_empty()); + } + + #[test] + fn step_traversable_action_surface_node_succeeds_and_collects_effects() { + let mut graph = dummy_graph(); + let parent = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let node = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let effect = add_dummy_node(&mut graph, "DummyOrAttackStep"); + graph.nodes[parent].children.insert(node); + graph.nodes[node].parents.insert(parent); + graph.nodes[node].children.insert(effect); + graph.nodes[effect].parents.insert(node); + graph.nodes[effect].causal_mode = + Some(maltoolbox_language::graph::attack_step::CausalMode::Effect); + + let performed: NodeSet = [parent].into_iter().collect(); + let action_surface: NodeSet = [node].into_iter().collect(); + let empty_set: NodeSet = NodeSet::default(); + let empty_entry_points: NodeSet = NodeSet::default(); + let empty_map_u64: NodeMap = NodeMap::default(); + let empty_map_f64: NodeMap = NodeMap::default(); + let necessity: NodeDenseMap<_> = [(node, true), (effect, true)].into_iter().collect(); + let mut r = rng(); + + let (successful, attempted) = attacker_step( + &graph, + &mut r, + TtcMode::Disabled, + &[node], + &empty_entry_points, + &action_surface, + &performed, + &empty_map_u64, + None, + None, + &empty_map_f64, + &empty_set, + &empty_set, + &necessity, + ) + .unwrap(); + assert_eq!(successful, vec![node, effect]); + assert!(attempted.is_empty()); + } + + #[test] + fn step_failed_attempt_is_recorded_without_compromising() { + let mut graph = dummy_graph(); + let parent = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let node = add_dummy_node(&mut graph, "DummyOrAttackStep"); + graph.nodes[parent].children.insert(node); + graph.nodes[node].parents.insert(parent); + + let empty_set: NodeSet = NodeSet::default(); + let performed: NodeSet = [parent].into_iter().collect(); + let action_surface: NodeSet = [node].into_iter().collect(); + let empty_map_u64: NodeMap = NodeMap::default(); + let empty_map_bool: NodeDenseMap = NodeDenseMap::default(); + // ExpectedValue mode with no resolvable ttc_value is an error, not + // a "failed attempt" - use a ttc_value that fails the comparison + // instead (num_attempts_before=0 -> 0 + 2 >= ttc_value is false + // for ttc_value > 2). + let graph_values: NodeMap<_> = [(node, 100.0)].into_iter().collect(); + let mut r = rng(); + + let (successful, attempted) = attacker_step( + &graph, + &mut r, + TtcMode::ExpectedValue, + &[node], + &empty_set, + &action_surface, + &performed, + &empty_map_u64, + None, + None, + &graph_values, + &empty_set, + &empty_set, + &empty_map_bool, + ) + .unwrap(); + assert!(successful.is_empty()); + assert_eq!(attempted, vec![node]); + } +} diff --git a/core/malsim-core/src/defender_step.rs b/core/malsim-core/src/defender_step.rs new file mode 100644 index 00000000..b625189c --- /dev/null +++ b/core/malsim-core/src/defender_step.rs @@ -0,0 +1,154 @@ +//! Rust port of `python/malsim/mal_simulator/defender_step.py` - see +//! `PORTING_NOTES.md` §5 Phase A7. + +use crate::types::NodeSet; +use std::fmt; + +use maltoolbox_attackgraph::{AttackGraph, AttackGraphNodeId}; + +use crate::attacker_step::attacker_is_terminated; +use crate::graph_utils::node_is_live; + +#[derive(Debug, Clone, PartialEq, Eq)] +pub enum DefenderStepError { + /// Mirrors Python's `assert node == sim_state.attack_graph.nodes[node.id]` + /// in `defender_step` - see `attacker_step::AttackerStepError:: + /// NodeNotInGraph`'s doc comment for why this id-liveness check is the + /// chosen translation on the Rust side. + NodeNotInGraph(AttackGraphNodeId), +} + +impl fmt::Display for DefenderStepError { + fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { + match self { + DefenderStepError::NodeNotInGraph(id) => write!( + f, + "tried to step a node ({id:?}) that is not part of this simulator's attack graph" + ), + } + } +} + +impl std::error::Error for DefenderStepError {} + +/// Port of `defender_step`. Like Python, a node outside the defender's +/// action surface is silently skipped (Python logs a warning) rather than +/// treated as an error - only the graph-membership check is a hard +/// failure. +pub fn defender_step( + graph: &AttackGraph, + nodes: &[AttackGraphNodeId], + action_surface: &NodeSet, +) -> Result, DefenderStepError> { + let mut enabled_defenses = Vec::new(); + + for &node_id in nodes { + if !node_is_live(graph, node_id) { + return Err(DefenderStepError::NodeNotInGraph(node_id)); + } + if action_surface.contains(&node_id) { + enabled_defenses.push(node_id); + } + } + + Ok(enabled_defenses) +} + +/// Port of `defender_is_terminated`. Takes each attacker's +/// `(action_surface, goals, performed_nodes)` triple directly rather than +/// an `AgentStates`/`AttackerState` collection - neither exists on the +/// Rust side yet (A9's job). `Iterator::all` over an empty iterator +/// mirrors Python's `all(...)` over an empty `attacker_states(...)` dict - +/// both vacuously `true`. +pub fn defender_is_terminated<'a>( + attackers: impl IntoIterator, +) -> bool { + attackers + .into_iter() + .all(|(action_surface, goals, performed_nodes)| { + attacker_is_terminated(action_surface, goals, performed_nodes) + }) +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::test_fixtures::{add_dummy_node, dummy_graph}; + + #[test] + fn defender_step_enables_nodes_in_action_surface() { + let mut graph = dummy_graph(); + let defense = add_dummy_node(&mut graph, "DummyDefenseAttackStep"); + let action_surface: NodeSet = [defense].into_iter().collect(); + + let enabled = defender_step(&graph, &[defense], &action_surface).unwrap(); + assert_eq!(enabled, vec![defense]); + } + + #[test] + fn defender_step_skips_nodes_outside_action_surface() { + let mut graph = dummy_graph(); + let attack_step = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let empty = NodeSet::default(); + + let enabled = defender_step(&graph, &[attack_step], &empty).unwrap(); + assert!(enabled.is_empty()); + } + + /// Mirrors the second case of the old + /// `tests/test_mal_simulator.py::test_defender_step` ("Can not defend + /// attack_step"), where the defender's action surface was non-empty + /// (it held every defense in the graph) - the requested attack step is + /// still skipped because it isn't one of them. + #[test] + fn defender_step_skips_node_outside_non_empty_action_surface() { + let mut graph = dummy_graph(); + let defense = add_dummy_node(&mut graph, "DummyDefenseAttackStep"); + let attack_step = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let action_surface: NodeSet = [defense].into_iter().collect(); + + let enabled = defender_step(&graph, &[attack_step], &action_surface).unwrap(); + assert!(enabled.is_empty()); + } + + #[test] + fn defender_step_fails_on_node_not_in_graph() { + let mut graph = dummy_graph(); + let defense = add_dummy_node(&mut graph, "DummyDefenseAttackStep"); + graph.remove_node(defense).unwrap(); + let action_surface: NodeSet = [defense].into_iter().collect(); + + let result = defender_step(&graph, &[defense], &action_surface); + assert!(matches!(result, Err(DefenderStepError::NodeNotInGraph(_)))); + } + + // --- defender_is_terminated --- + + #[test] + fn defender_terminated_vacuously_with_no_attackers() { + assert!(defender_is_terminated(std::iter::empty())); + } + + #[test] + fn defender_terminated_when_all_attackers_terminated() { + let empty = NodeSet::default(); + // Empty action surface -> terminated, for every attacker. + assert!(defender_is_terminated([ + (&empty, &empty, &empty), + (&empty, &empty, &empty) + ])); + } + + #[test] + fn defender_not_terminated_when_one_attacker_is_not() { + let mut graph = dummy_graph(); + let node = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let surface: NodeSet = [node].into_iter().collect(); + let empty = NodeSet::default(); + + assert!(!defender_is_terminated([ + (&empty, &empty, &empty), + (&surface, &empty, &empty), + ])); + } +} diff --git a/core/malsim-core/src/defense_surface.rs b/core/malsim-core/src/defense_surface.rs new file mode 100644 index 00000000..bc1acd9f --- /dev/null +++ b/core/malsim-core/src/defense_surface.rs @@ -0,0 +1,97 @@ +//! Rust port of `python/malsim/mal_simulator/defense_surface.py` - see +//! `PORTING_NOTES.md` §5 Phase A5. +//! +//! Like `attack_surface.rs`, actionability is taken as an already- +//! flattened id-set rather than a `NodePropertyRule` - see that module's +//! docs for the exact `None`/`Some` semantics mirrored here. + +use crate::types::NodeSet; + +use maltoolbox_attackgraph::{AttackGraph, AttackGraphNodeId}; + +use crate::graph_utils::{node_is_blocked, GraphUtilsError}; + +fn node_is_actionable_flat(actionable_steps: Option<&NodeSet>, node_id: AttackGraphNodeId) -> bool { + match actionable_steps { + Some(steps) => steps.contains(&node_id), + None => true, + } +} + +/// Port of `get_defense_surface`: all non-suppressed defense steps that are +/// actionable, not blocked, and not already enabled. +pub fn get_defense_surface( + graph: &AttackGraph, + actionable_steps: Option<&NodeSet>, + impossible_attack_steps: &NodeSet, + enabled_defenses: &NodeSet, +) -> Result { + let mut surface = NodeSet::default(); + for &node_id in &graph.defense_steps { + if !node_is_actionable_flat(actionable_steps, node_id) { + continue; + } + if node_is_blocked(graph, node_id, impossible_attack_steps, enabled_defenses)? { + continue; + } + if enabled_defenses.contains(&node_id) { + continue; + } + if graph.nodes[node_id].tags.iter().any(|t| t == "suppress") { + continue; + } + surface.insert(node_id); + } + Ok(surface) +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::test_fixtures::{add_dummy_node, dummy_graph}; + + #[test] + fn defense_surface_includes_plain_unenabled_defense() { + let mut graph = dummy_graph(); + let defense = add_dummy_node(&mut graph, "DummyDefenseAttackStep"); + + let empty = NodeSet::default(); + let result = get_defense_surface(&graph, None, &empty, &empty).unwrap(); + assert_eq!(result, [defense].into_iter().collect()); + } + + #[test] + fn defense_surface_excludes_already_enabled_defense() { + let mut graph = dummy_graph(); + let defense = add_dummy_node(&mut graph, "DummyDefenseAttackStep"); + + let empty = NodeSet::default(); + let enabled: NodeSet = [defense].into_iter().collect(); + let result = get_defense_surface(&graph, None, &empty, &enabled).unwrap(); + assert!(result.is_empty()); + } + + #[test] + fn defense_surface_excludes_suppressed_defense() { + let mut graph = dummy_graph(); + let defense = add_dummy_node(&mut graph, "DummyDefenseAttackStep"); + graph.nodes[defense].tags.push("suppress".to_string()); + + let empty = NodeSet::default(); + let result = get_defense_surface(&graph, None, &empty, &empty).unwrap(); + assert!(result.is_empty()); + } + + #[test] + fn defense_surface_respects_flattened_actionable_steps() { + let mut graph = dummy_graph(); + let actionable_defense = add_dummy_node(&mut graph, "DummyDefenseAttackStep"); + let non_actionable_defense = add_dummy_node(&mut graph, "DummyDefenseAttackStep"); + + let empty = NodeSet::default(); + let actionable_set: NodeSet = [actionable_defense].into_iter().collect(); + let result = get_defense_surface(&graph, Some(&actionable_set), &empty, &empty).unwrap(); + assert_eq!(result, [actionable_defense].into_iter().collect()); + assert!(!result.contains(&non_actionable_defense)); + } +} diff --git a/core/malsim-core/src/dyna_attacker_step.rs b/core/malsim-core/src/dyna_attacker_step.rs new file mode 100644 index 00000000..5f45ff13 --- /dev/null +++ b/core/malsim-core/src/dyna_attacker_step.rs @@ -0,0 +1,372 @@ +//! Rust port of `python/malsim/dyna_mal_simulator/attacker_step.py` - see +//! `PORTING_NOTES.md` §6 Phase B2. +//! +//! Wraps Phase A7's `attacker_step::attempt_attacker_step`/ +//! `attacker_step_effects` with Phase B1's `model_effects:: +//! execute_model_effects`, called on every successful compromise and every +//! resulting effect node - mirrors Python's `dyna_attacker_step` +//! reassigning `sim_state` after each model effect, which the *next* node +//! in the same batch (and the effect-chain computation for the *current* +//! node) reads back. `graph_state`/`enabled_defenses` are threaded through +//! as `&mut` rather than returned copies for exactly that reason. +//! +//! **No separate `dyna_attempt_attacker_step` here.** Python's version is +//! identical to A7's `attempt_attacker_step` except for resolving +//! `agent.num_attempts.get(node, 0)` (a node created mid-simulation may +//! not be in the dict yet) instead of `agent.num_attempts[node]` - but +//! A7's Rust `attacker_step` already resolves its `num_attempts_before` +//! argument the same defensive way (`num_attempts.get(&node_id).copied() +//! .unwrap_or(0)`, for the same "new nodes aren't seeded yet" reason), so +//! `attacker_step::attempt_attacker_step` is reused directly below with +//! no behavioral gap. + +use crate::types::{NodeMap, NodeSet}; +use std::fmt; + +use maltoolbox_attackgraph::{AttackGraph, AttackGraphNodeId}; +use maltoolbox_model::Model; +use rand::Rng; + +use crate::attacker_step::{attacker_step_effects, attempt_attacker_step, AttackerStepError}; +use crate::dyna_graph_state::{fold_new_nodes_into_graph_state, GraphStateDelta}; +use crate::graph_state::{GraphState, GraphStateError, TtcMode}; +use crate::graph_utils::{node_is_live, node_is_traversable, GraphUtilsError}; +use crate::model_effects::{execute_model_effects, ModEffectOp, ModelEffectsError}; +use crate::ttc::TtcDist; + +#[derive(Debug)] +pub enum DynaAttackerStepError { + GraphUtils(GraphUtilsError), + Attacker(AttackerStepError), + GraphState(GraphStateError), + ModelEffects(Box), + /// Mirrors Python's `assert node == sim_state.attack_graph.nodes[node.id]`, + /// translated to "this node id no longer exists in the graph", same + /// as A7's `AttackerStepError::NodeNotInGraph`. + NodeNotInGraph(AttackGraphNodeId), +} + +impl From for DynaAttackerStepError { + fn from(e: GraphUtilsError) -> Self { + DynaAttackerStepError::GraphUtils(e) + } +} + +impl From for DynaAttackerStepError { + fn from(e: AttackerStepError) -> Self { + DynaAttackerStepError::Attacker(e) + } +} + +impl From for DynaAttackerStepError { + fn from(e: GraphStateError) -> Self { + DynaAttackerStepError::GraphState(e) + } +} + +impl From for DynaAttackerStepError { + fn from(e: ModelEffectsError) -> Self { + DynaAttackerStepError::ModelEffects(Box::new(e)) + } +} + +impl fmt::Display for DynaAttackerStepError { + fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { + match self { + DynaAttackerStepError::GraphUtils(e) => write!(f, "{e}"), + DynaAttackerStepError::Attacker(e) => write!(f, "{e}"), + DynaAttackerStepError::GraphState(e) => write!(f, "{e}"), + DynaAttackerStepError::ModelEffects(e) => write!(f, "{e}"), + DynaAttackerStepError::NodeNotInGraph(id) => write!( + f, + "tried to step a node ({id:?}) that is not part of this simulator's attack graph" + ), + } + } +} + +impl std::error::Error for DynaAttackerStepError {} + +/// (successful compromises, attempted-but-failed compromises, modification +/// record from every model effect executed along the way, what the folds +/// changed in `graph_state`). +pub type DynaAttackerStepOutcome = ( + Vec, + Vec, + Vec, + GraphStateDelta, +); + +/// Port of `dyna_attacker_step`. `performed_nodes` is read-only here, same +/// contract as A7's `attacker_step` - it does not grow mid-batch even as +/// nodes in `nodes` succeed, matching Python's `agent.performed_nodes` +/// (an immutable per-step snapshot on the frozen `AttackerState`). +#[allow(clippy::too_many_arguments)] +pub fn dyna_attacker_step( + graph: &mut AttackGraph, + model: &mut Model, + rng: &mut impl Rng, + ttc_mode: TtcMode, + run_defense_step_bernoullis: bool, + run_attack_step_bernoullis: bool, + nodes: &[AttackGraphNodeId], + entry_points: &NodeSet, + action_surface: &NodeSet, + performed_nodes: &NodeSet, + num_attempts: &NodeMap, + ttc_dist_overrides: Option<&NodeMap>, + ttc_value_overrides: Option<&NodeMap>, + graph_state: &mut GraphState, + enabled_defenses: &mut NodeSet, +) -> Result { + let mut successful_compromises = Vec::new(); + let mut attempted_compromises = Vec::new(); + let mut modification_record = Vec::new(); + let mut delta = GraphStateDelta::default(); + + for &node_id in nodes { + if !node_is_live(graph, node_id) { + return Err(DynaAttackerStepError::NodeNotInGraph(node_id)); + } + + let can_compromise = if entry_points.contains(&node_id) { + true + } else { + action_surface.contains(&node_id) + && node_is_traversable( + graph, + node_id, + performed_nodes, + &graph_state.impossible_attack_steps, + enabled_defenses, + &graph_state.necessity_per_node, + )? + }; + + if !can_compromise { + continue; + } + + let num_attempts_before = num_attempts.get(&node_id).copied().unwrap_or(0); + let ttc_override = ttc_dist_overrides.and_then(|m| m.get(&node_id)); + + let succeeded = attempt_attacker_step( + graph, + rng, + ttc_mode, + node_id, + num_attempts_before, + ttc_override, + ttc_value_overrides, + &graph_state.ttc_values, + )?; + + if !succeeded { + attempted_compromises.push(node_id); + continue; + } + + successful_compromises.push(node_id); + let (ops, new_nodes) = execute_model_effects(graph, model, node_id, rng)?; + fold_new_nodes_into_graph_state( + graph, + graph_state, + enabled_defenses, + &ops, + &new_nodes, + ttc_mode, + run_defense_step_bernoullis, + run_attack_step_bernoullis, + rng, + &mut delta, + )?; + modification_record.extend(ops); + + let effects = attacker_step_effects( + graph, + node_id, + performed_nodes, + &graph_state.impossible_attack_steps, + enabled_defenses, + &graph_state.necessity_per_node, + )?; + for effect_node in effects { + successful_compromises.push(effect_node); + // `effects` was computed once, above, against the graph as it + // stood right after `node_id`'s own model effects ran - but an + // *earlier* `effect_node` in this same list can itself remove + // the asset a *later* one belongs to (the same self-removal + // shape `PORTING_NOTES.md` §0 B5 traces for `node_id` itself, + // one level deeper). `effect_node` was genuinely satisfied at + // the moment `attacker_step_effects` found it - that's already + // recorded above - there's just no live node left to run a + // model effect for. + if !node_is_live(graph, effect_node) { + continue; + } + let (ops, new_nodes) = execute_model_effects(graph, model, effect_node, rng)?; + fold_new_nodes_into_graph_state( + graph, + graph_state, + enabled_defenses, + &ops, + &new_nodes, + ttc_mode, + run_defense_step_bernoullis, + run_attack_step_bernoullis, + rng, + &mut delta, + )?; + modification_record.extend(ops); + } + } + + Ok(( + successful_compromises, + attempted_compromises, + modification_record, + delta, + )) +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::graph_state::compute_initial_graph_state; + use crate::model_effects::AssetOp; + use crate::test_fixtures::wiper_attack_graph; + use rand::rngs::StdRng; + use rand::SeedableRng; + + fn rng() -> StdRng { + StdRng::seed_from_u64(1) + } + + fn empty_graph_state() -> GraphState { + GraphState { + ttc_values: NodeMap::default(), + pre_enabled_defenses: NodeSet::default(), + impossible_attack_steps: NodeSet::default(), + necessity_per_node: Default::default(), + } + } + + #[test] + fn dyna_attacker_step_fails_node_not_in_graph() { + let (mut graph, mut model) = wiper_attack_graph(); + let infect_id = graph.full_name_to_node["InfectedDevice:infect"]; + graph.remove_node(infect_id).unwrap(); + let mut r = rng(); + let mut graph_state = empty_graph_state(); + let mut enabled_defenses = NodeSet::default(); + let empty_set: NodeSet = NodeSet::default(); + let empty_map_u64: NodeMap = NodeMap::default(); + + let result = dyna_attacker_step( + &mut graph, + &mut model, + &mut r, + TtcMode::Disabled, + false, + false, + &[infect_id], + &empty_set, + &empty_set, + &empty_set, + &empty_map_u64, + None, + None, + &mut graph_state, + &mut enabled_defenses, + ); + assert!(matches!( + result, + Err(DynaAttackerStepError::NodeNotInGraph(_)) + )); + } + + #[test] + fn dyna_attacker_step_skips_node_outside_action_surface_and_not_entry_point() { + let (mut graph, mut model) = wiper_attack_graph(); + let infect_id = graph.full_name_to_node["InfectedDevice:infect"]; + let mut r = rng(); + let mut graph_state = + compute_initial_graph_state(&graph, TtcMode::Disabled, false, false, &mut r).unwrap(); + let mut enabled_defenses = NodeSet::default(); + let empty_set: NodeSet = NodeSet::default(); + let empty_map_u64: NodeMap = NodeMap::default(); + + let (successful, attempted, record, _) = dyna_attacker_step( + &mut graph, + &mut model, + &mut r, + TtcMode::Disabled, + false, + false, + &[infect_id], + &empty_set, + &empty_set, + &empty_set, + &empty_map_u64, + None, + None, + &mut graph_state, + &mut enabled_defenses, + ) + .unwrap(); + assert!(successful.is_empty()); + assert!(attempted.is_empty()); + assert!(record.is_empty()); + } + + #[test] + fn dyna_attacker_step_entry_point_compromise_runs_model_effects_and_folds_graph_state() { + let (mut graph, mut model) = wiper_attack_graph(); + let mut r = rng(); + let mut graph_state = + compute_initial_graph_state(&graph, TtcMode::Disabled, false, false, &mut r).unwrap(); + let mut enabled_defenses: NodeSet = NodeSet::default(); + + let infect_id = graph.full_name_to_node["InfectedDevice:infect"]; + let entry_points: NodeSet = [infect_id].into_iter().collect(); + let empty_set: NodeSet = NodeSet::default(); + let empty_map_u64: NodeMap = NodeMap::default(); + + let (successful, attempted, record, _) = dyna_attacker_step( + &mut graph, + &mut model, + &mut r, + TtcMode::Disabled, + false, + false, + &[infect_id], + &entry_points, + &empty_set, + &empty_set, + &empty_map_u64, + None, + None, + &mut graph_state, + &mut enabled_defenses, + ) + .unwrap(); + + assert_eq!(successful, vec![infect_id]); + assert!(attempted.is_empty()); + assert!( + record + .iter() + .any(|op| matches!(op, ModEffectOp::Asset(AssetOp::Added { .. }))), + "infect's model effect should have created the Wiper asset" + ); + assert!(model.get_asset_by_name("Wiper-7").is_some()); + + // The newly-regenerated Wiper-7:* nodes are folded into the + // returned graph_state (every node in the graph has a necessity + // entry, including ones that didn't exist before this step). + let wiper_activate_id = graph.full_name_to_node["Wiper-7:activate"]; + assert!(graph_state + .necessity_per_node + .contains_key(wiper_activate_id)); + } +} diff --git a/core/malsim-core/src/dyna_defender_step.rs b/core/malsim-core/src/dyna_defender_step.rs new file mode 100644 index 00000000..1d643942 --- /dev/null +++ b/core/malsim-core/src/dyna_defender_step.rs @@ -0,0 +1,220 @@ +//! Rust port of `python/malsim/dyna_mal_simulator/defender_step.py` - see +//! `PORTING_NOTES.md` §6 Phase B2. +//! +//! Same wrapping shape as `dyna_attacker_step.rs`, simpler: no TTC +//! attempt/effect-chain logic, just "enable the defense, then run its +//! model effects." + +use crate::types::NodeSet; +use std::fmt; + +use maltoolbox_attackgraph::{AttackGraph, AttackGraphNodeId}; +use maltoolbox_model::Model; +use rand::Rng; + +use crate::dyna_graph_state::{fold_new_nodes_into_graph_state, GraphStateDelta}; +use crate::graph_state::{GraphState, GraphStateError, TtcMode}; +use crate::graph_utils::{node_is_live, GraphUtilsError}; +use crate::model_effects::{execute_model_effects, ModEffectOp, ModelEffectsError}; + +#[derive(Debug)] +pub enum DynaDefenderStepError { + GraphUtils(GraphUtilsError), + GraphState(GraphStateError), + ModelEffects(Box), + NodeNotInGraph(AttackGraphNodeId), +} + +impl From for DynaDefenderStepError { + fn from(e: GraphUtilsError) -> Self { + DynaDefenderStepError::GraphUtils(e) + } +} + +impl From for DynaDefenderStepError { + fn from(e: GraphStateError) -> Self { + DynaDefenderStepError::GraphState(e) + } +} + +impl From for DynaDefenderStepError { + fn from(e: ModelEffectsError) -> Self { + DynaDefenderStepError::ModelEffects(Box::new(e)) + } +} + +impl fmt::Display for DynaDefenderStepError { + fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { + match self { + DynaDefenderStepError::GraphUtils(e) => write!(f, "{e}"), + DynaDefenderStepError::GraphState(e) => write!(f, "{e}"), + DynaDefenderStepError::ModelEffects(e) => write!(f, "{e}"), + DynaDefenderStepError::NodeNotInGraph(id) => write!( + f, + "tried to step a node ({id:?}) that is not part of this simulator's attack graph" + ), + } + } +} + +impl std::error::Error for DynaDefenderStepError {} + +/// Port of `dyna_defender_step`. A node outside `action_surface` is +/// silently skipped (Python logs a warning), not an error - only the +/// graph-membership check is a hard failure, same as A7's `defender_step`. +/// The last returned element records what the model-effect folds changed in +/// `graph_state`. +#[allow(clippy::too_many_arguments)] +pub fn dyna_defender_step( + graph: &mut AttackGraph, + model: &mut Model, + rng: &mut impl Rng, + ttc_mode: TtcMode, + run_defense_step_bernoullis: bool, + run_attack_step_bernoullis: bool, + nodes: &[AttackGraphNodeId], + action_surface: &NodeSet, + graph_state: &mut GraphState, + enabled_defenses: &mut NodeSet, +) -> Result<(Vec, Vec, GraphStateDelta), DynaDefenderStepError> { + let mut enabled = Vec::new(); + let mut modification_record = Vec::new(); + let mut delta = GraphStateDelta::default(); + + for &node_id in nodes { + if !node_is_live(graph, node_id) { + return Err(DynaDefenderStepError::NodeNotInGraph(node_id)); + } + + if !action_surface.contains(&node_id) { + continue; + } + + enabled.push(node_id); + let (ops, new_nodes) = execute_model_effects(graph, model, node_id, rng)?; + fold_new_nodes_into_graph_state( + graph, + graph_state, + enabled_defenses, + &ops, + &new_nodes, + ttc_mode, + run_defense_step_bernoullis, + run_attack_step_bernoullis, + rng, + &mut delta, + )?; + modification_record.extend(ops); + } + + Ok((enabled, modification_record, delta)) +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::types::NodeMap; + + use crate::graph_state::compute_initial_graph_state; + use crate::test_fixtures::{add_dummy_node, dummy_graph_and_model}; + use rand::rngs::StdRng; + use rand::SeedableRng; + + fn rng() -> StdRng { + StdRng::seed_from_u64(1) + } + + fn empty_graph_state() -> GraphState { + GraphState { + ttc_values: NodeMap::default(), + pre_enabled_defenses: NodeSet::default(), + impossible_attack_steps: NodeSet::default(), + necessity_per_node: Default::default(), + } + } + + #[test] + fn dyna_defender_step_fails_node_not_in_graph() { + let (mut graph, mut model) = dummy_graph_and_model(); + let node = add_dummy_node(&mut graph, "DummyDefenseAttackStep"); + graph.remove_node(node).unwrap(); + let action_surface: NodeSet = [node].into_iter().collect(); + let mut r = rng(); + let mut graph_state = empty_graph_state(); + let mut enabled_defenses = NodeSet::default(); + + let result = dyna_defender_step( + &mut graph, + &mut model, + &mut r, + TtcMode::Disabled, + false, + false, + &[node], + &action_surface, + &mut graph_state, + &mut enabled_defenses, + ); + assert!(matches!( + result, + Err(DynaDefenderStepError::NodeNotInGraph(_)) + )); + } + + #[test] + fn dyna_defender_step_skips_node_outside_action_surface() { + let (mut graph, mut model) = dummy_graph_and_model(); + let node = add_dummy_node(&mut graph, "DummyDefenseAttackStep"); + let empty_set: NodeSet = NodeSet::default(); + let mut r = rng(); + let mut graph_state = + compute_initial_graph_state(&graph, TtcMode::Disabled, false, false, &mut r).unwrap(); + let mut enabled_defenses = NodeSet::default(); + + let (enabled, record, _) = dyna_defender_step( + &mut graph, + &mut model, + &mut r, + TtcMode::Disabled, + false, + false, + &[node], + &empty_set, + &mut graph_state, + &mut enabled_defenses, + ) + .unwrap(); + assert!(enabled.is_empty()); + assert!(record.is_empty()); + } + + #[test] + fn dyna_defender_step_enables_defense_with_no_model_effects() { + let (mut graph, mut model) = dummy_graph_and_model(); + let node = add_dummy_node(&mut graph, "DummyDefenseAttackStep"); + let action_surface: NodeSet = [node].into_iter().collect(); + let mut r = rng(); + let mut graph_state = + compute_initial_graph_state(&graph, TtcMode::Disabled, false, false, &mut r).unwrap(); + let mut enabled_defenses = NodeSet::default(); + + let (enabled, record, _) = dyna_defender_step( + &mut graph, + &mut model, + &mut r, + TtcMode::Disabled, + false, + false, + &[node], + &action_surface, + &mut graph_state, + &mut enabled_defenses, + ) + .unwrap(); + assert_eq!(enabled, vec![node]); + assert!( + record.is_empty(), + "dummy_lang declares no model effects on this step" + ); + } +} diff --git a/core/malsim-core/src/dyna_graph_state.rs b/core/malsim-core/src/dyna_graph_state.rs new file mode 100644 index 00000000..3223f7a5 --- /dev/null +++ b/core/malsim-core/src/dyna_graph_state.rs @@ -0,0 +1,419 @@ +//! Rust port of `python/malsim/dyna_mal_simulator/graph_state.py` - see +//! `PORTING_NOTES.md` §6 Phase B2. +//! +//! Folds newly-created nodes (from +//! [`crate::model_effects::execute_model_effects`]'s +//! `partially_regenerate_graph` call) into a [`GraphState`] - a thin +//! composition of Phase A3's per-node TTC/pre-enabled-defense/impossible- +//! step helpers plus Phase A3's `calculate_necessity`, restricted to the +//! new nodes rather than the whole graph. + +use crate::types::NodeMap; +use crate::types::NodeSet; + +use maltoolbox_attackgraph::{AttackGraph, AttackGraphNodeId}; +use maltoolbox_language::graph::attack_step::AttackStepType; +use rand::Rng; + +use crate::graph_state::{ + attack_step_ttc_value, is_impossible_for_dist, is_pre_enabled_for_dist, resolve_ttc_dist, + GraphState, GraphStateError, TtcMode, +}; +use crate::graph_utils::sort_by_node_id; +use crate::model_effects::ModEffectOp; +use crate::necessity::calculate_necessity; + +/// What one step's model-effect folds changed in `graph_state`, so the step can be undone. +#[derive(Debug, Clone, Default)] +pub struct GraphStateDelta { + /// Every node the step's folds created. + pub(crate) new_nodes: NodeSet, + /// For each `necessity_per_node` key the folds changed, added or dropped: its value + /// before the step (`None` = it was absent). The first value seen wins. + pub(crate) necessity_prior: NodeMap>, +} + +impl GraphStateDelta { + /// Appends a later delta from the same step; earlier prior values win. + pub(crate) fn merge(&mut self, later: GraphStateDelta) { + self.new_nodes.extend(later.new_nodes); + for (id, prior) in later.necessity_prior { + self.necessity_prior.entry(id).or_insert(prior); + } + } +} + +/// Port of `add_new_nodes_to_graph_state`, in place: extends +/// `graph_state`'s `ttc_values`/`pre_enabled_defenses`/ +/// `impossible_attack_steps` with the new nodes' and recomputes +/// `necessity_per_node` against `pre_enabled_defenses ∪ new enabled`. +/// Takes the three relevant `MalSimulatorSettings` fields directly rather +/// than the whole settings struct, same pattern A3's +/// `compute_initial_graph_state` established. +/// RNG draws happen in the same order as the Python original (TTC values, +/// then defense Bernoullis, then attack-step Bernoullis). `graph_state` is +/// only mutated once every fallible RNG/TTC step has succeeded. +/// Records what changed in `delta`. Returns the newly pre-enabled defenses. +#[allow(clippy::too_many_arguments)] +fn extend_graph_state_with_new_nodes( + graph: &AttackGraph, + graph_state: &mut GraphState, + ttc_mode: TtcMode, + run_defense_step_bernoullis: bool, + run_attack_step_bernoullis: bool, + new_nodes: &NodeSet, + rng: &mut impl Rng, + delta: &mut GraphStateDelta, +) -> Result { + // RNG draws below follow these Vecs, so they are in node-id order + // rather than the order of the `new_nodes` set. + let mut sorted_new_nodes: Vec = new_nodes.iter().copied().collect(); + sort_by_node_id(graph, &mut sorted_new_nodes); + let new_attack_steps: Vec = sorted_new_nodes + .iter() + .copied() + .filter(|&id| { + matches!( + graph.nodes[id].step_type, + AttackStepType::And | AttackStepType::Or + ) + }) + .collect(); + let new_defense_steps: Vec = sorted_new_nodes + .iter() + .copied() + .filter(|&id| graph.nodes[id].step_type == AttackStepType::Defense) + .collect(); + + let mut new_ttc_values = NodeMap::default(); + for &node_id in &new_attack_steps { + let node = &graph.nodes[node_id]; + if let Some(value) = attack_step_ttc_value(node, None, ttc_mode, rng)? { + new_ttc_values.insert(node_id, value); + } + } + + let mut new_steps_enabled_defenses = NodeSet::default(); + for &node_id in &new_defense_steps { + let node = &graph.nodes[node_id]; + let ttc_dist = resolve_ttc_dist(node, None)?; + if is_pre_enabled_for_dist(&ttc_dist, run_defense_step_bernoullis, rng) { + new_steps_enabled_defenses.insert(node_id); + } + } + + let mut new_impossible_attack_steps = NodeSet::default(); + if run_attack_step_bernoullis { + for &node_id in &new_attack_steps { + let node = &graph.nodes[node_id]; + if is_impossible_for_dist(&resolve_ttc_dist(node, None)?, rng) { + new_impossible_attack_steps.insert(node_id); + } + } + } + + // Necessity is computed against pre_enabled ∪ new enabled (never the + // live `enabled_defenses`); the union is only materialised when there + // is something to add. + let necessity_per_node = if new_steps_enabled_defenses.is_empty() { + calculate_necessity(graph, &graph_state.pre_enabled_defenses)? + } else { + let union: NodeSet = graph_state + .pre_enabled_defenses + .union(&new_steps_enabled_defenses) + .copied() + .collect(); + calculate_necessity(graph, &union)? + }; + + // Node ids are versioned slotmap keys, so a regenerated node that reuses + // a removed node's slot still gets a key no earlier node had. The fold + // only ever adds keys; undoing it relies on that. + debug_assert!( + new_nodes.iter().all(|id| { + !graph_state.ttc_values.contains_key(id) + && !graph_state.pre_enabled_defenses.contains(id) + && !graph_state.impossible_attack_steps.contains(id) + }), + "a newly created node's key is already present in graph_state" + ); + graph_state.ttc_values.extend(new_ttc_values); + graph_state + .pre_enabled_defenses + .extend(new_steps_enabled_defenses.iter().copied()); + graph_state + .impossible_attack_steps + .extend(new_impossible_attack_steps); + let old_necessity = std::mem::replace(&mut graph_state.necessity_per_node, necessity_per_node); + delta.new_nodes.extend(new_nodes.iter().copied()); + for (id, &v) in old_necessity.iter() { + if graph_state.necessity_per_node.get(id) != Some(&v) { + delta.necessity_prior.entry(id).or_insert(Some(v)); + } + } + for (id, _) in graph_state.necessity_per_node.iter() { + if !old_necessity.contains_key(id) { + delta.necessity_prior.entry(id).or_insert(None); + } + } + Ok(new_steps_enabled_defenses) +} + +/// Shared by `dyna_attacker_step.rs`/`dyna_defender_step.rs`: folds the +/// node ids `execute_model_effects` just created into `graph_state`/ +/// `enabled_defenses` in place. +/// +/// Python's `execute_model_effects` calls `add_new_nodes_to_graph_state` +/// unconditionally; here the fold is skipped when `ops` and `new_nodes` +/// are both empty, which is equivalent: with no recorded ops no +/// asset/association was added or removed, so +/// `partially_regenerate_graph` ran with all-empty inputs and left the +/// graph untouched; an empty `new_nodes` makes every RNG loop in the fold +/// a zero-iteration loop (no draws) and adds nothing to +/// `ttc_values`/`pre_enabled_defenses`/`impossible_attack_steps`/ +/// `enabled_defenses`; and `necessity_per_node` would be recomputed over +/// the same graph with the same `pre_enabled_defenses` it was last +/// computed with (`compute_initial_graph_state` and every earlier fold +/// both use `pre_enabled_defenses`), so it comes out identical. +/// +/// What the fold changed is recorded in `delta`. +#[allow(clippy::too_many_arguments)] +pub(crate) fn fold_new_nodes_into_graph_state( + graph: &AttackGraph, + graph_state: &mut GraphState, + enabled_defenses: &mut NodeSet, + ops: &[ModEffectOp], + new_nodes: &NodeSet, + ttc_mode: TtcMode, + run_defense_step_bernoullis: bool, + run_attack_step_bernoullis: bool, + rng: &mut impl Rng, + delta: &mut GraphStateDelta, +) -> Result<(), GraphStateError> { + if ops.is_empty() && new_nodes.is_empty() { + return Ok(()); + } + let new_enabled = extend_graph_state_with_new_nodes( + graph, + graph_state, + ttc_mode, + run_defense_step_bernoullis, + run_attack_step_bernoullis, + new_nodes, + rng, + delta, + )?; + enabled_defenses.extend(new_enabled); + Ok(()) +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::graph_state::compute_initial_graph_state; + use crate::test_fixtures::wiper_attack_graph; + use rand::rngs::StdRng; + use rand::SeedableRng; + use std::collections::{HashMap, HashSet}; + + fn rng() -> StdRng { + StdRng::seed_from_u64(3) + } + + #[test] + fn extend_graph_state_with_new_nodes_covers_newly_regenerated_nodes() { + let (mut graph, mut model) = wiper_attack_graph(); + let mut r = rng(); + let initial_graph_state = + compute_initial_graph_state(&graph, TtcMode::ExpectedValue, false, false, &mut r) + .unwrap(); + + let next_id = model.next_id; + let wiper_id = model + .add_asset( + "Wiper", + Some("Wiper".to_string()), + Some(next_id), + None, + None, + false, + ) + .unwrap(); + let infected_device = model.get_asset_by_name("InfectedDevice").unwrap().id; + model + .add_associated_assets(wiper_id, "victim", HashSet::from([infected_device])) + .unwrap(); + + let new_assets = HashSet::from([wiper_id]); + let new_associations = HashSet::from([(wiper_id, "victim".to_string(), infected_device)]); + let new_nodes = graph + .partially_regenerate_graph( + &model, + &new_assets, + &new_associations, + &HashMap::new(), + &HashSet::new(), + ) + .unwrap() + .into_iter() + .collect::(); + assert!( + !new_nodes.is_empty(), + "adding the Wiper asset should create new attack steps" + ); + + let mut new_state = initial_graph_state.clone(); + let new_enabled = extend_graph_state_with_new_nodes( + &graph, + &mut new_state, + TtcMode::ExpectedValue, + false, + false, + &new_nodes, + &mut r, + &mut GraphStateDelta::default(), + ) + .unwrap(); + + assert!(new_enabled.is_empty(), "wiperLang has no defense steps"); + for &node_id in &new_nodes { + assert!( + new_state.necessity_per_node.contains_key(node_id), + "new node {node_id:?} missing from folded necessity map" + ); + } + // Pre-existing ttc_values/pre_enabled_defenses survive the fold. + for (&node_id, &value) in &initial_graph_state.ttc_values { + assert_eq!(new_state.ttc_values.get(&node_id), Some(&value)); + } + assert_eq!( + new_state.pre_enabled_defenses, + initial_graph_state.pre_enabled_defenses + ); + } + + /// The slot index of a node key (the low 32 bits of its FFI form). + fn slot_index(id: AttackGraphNodeId) -> u32 { + use slotmap::Key; + id.data().as_ffi() as u32 + } + + /// A node created after another node was removed can reuse its slot, + /// but never its key: slotmap bumps the slot's version. So the keys a + /// fold adds to `graph_state` are always new ones, which is what lets a + /// step's fold be undone by removing exactly the step's new nodes. + #[test] + fn regenerated_nodes_reuse_slots_but_never_keys() { + use crate::model_effects::regenerate_graph; + + let (mut graph, mut model) = wiper_attack_graph(); + let mut r = rng(); + let mut state = + compute_initial_graph_state(&graph, TtcMode::ExpectedValue, false, false, &mut r) + .unwrap(); + let keys_before: NodeSet = graph.nodes.keys().collect(); + + // Remove InfectedData, freeing the slots of its nodes. + let data_id = model.get_asset_by_name("InfectedData").unwrap().id; + let device_id = model.get_asset_by_name("InfectedDevice").unwrap().id; + let snapshot = model.remove_asset(data_id).unwrap(); + regenerate_graph( + &mut graph, + &model, + &HashSet::new(), + &HashSet::new(), + &HashMap::from([(data_id, snapshot)]), + &HashSet::from([(data_id, "node".to_string(), device_id)]), + ) + .unwrap(); + let removed: NodeSet = keys_before + .iter() + .copied() + .filter(|&id| !graph.nodes.contains_key(id)) + .collect(); + assert!( + !removed.is_empty(), + "removing InfectedData removed no nodes" + ); + + // Add a new Data asset; its nodes land in the freed slots. + let next_id = model.next_id; + let new_data = model + .add_asset( + "Data", + Some("NewData".to_string()), + Some(next_id), + None, + None, + false, + ) + .unwrap(); + model + .add_associated_assets(new_data, "node", HashSet::from([device_id])) + .unwrap(); + let new_nodes = regenerate_graph( + &mut graph, + &model, + &HashSet::from([new_data]), + &HashSet::from([(new_data, "node".to_string(), device_id)]), + &HashMap::new(), + &HashSet::new(), + ) + .unwrap(); + + // Positive control: without slot reuse this test proves nothing. + let removed_slots: HashSet = removed.iter().map(|&id| slot_index(id)).collect(); + let reused: Vec = new_nodes + .iter() + .copied() + .filter(|&id| removed_slots.contains(&slot_index(id))) + .collect(); + assert!(!reused.is_empty(), "no new node reused a freed slot"); + + // Every new key differs from every key the graph ever held, so none + // is already in graph_state. + assert!(new_nodes.is_disjoint(&keys_before)); + for id in &new_nodes { + assert!(!state.ttc_values.contains_key(id)); + assert!(!state.pre_enabled_defenses.contains(id)); + assert!(!state.impossible_attack_steps.contains(id)); + } + + // Folding keeps the removed nodes' stale entries and adds the new + // ones next to them (also exercises the fold's debug_assert). + let ttc_before = state.ttc_values.clone(); + extend_graph_state_with_new_nodes( + &graph, + &mut state, + TtcMode::ExpectedValue, + false, + false, + &new_nodes, + &mut r, + &mut GraphStateDelta::default(), + ) + .unwrap(); + for (id, value) in &ttc_before { + assert_eq!(state.ttc_values.get(id), Some(value)); + } + + // The necessity map is slot-indexed: a reused slot holds the new + // node's entry, and the removed node's older key reads as absent. + // Restoring an old entry there means removing the new key first, + // since `SecondaryMap::insert` ignores keys older than the slot's. + for &new_id in &reused { + assert!(state.necessity_per_node.contains_key(new_id)); + let old_id = *removed + .iter() + .find(|&&id| slot_index(id) == slot_index(new_id)) + .unwrap(); + assert!(!state.necessity_per_node.contains_key(old_id)); + let mut necessity = state.necessity_per_node.clone(); + assert_eq!(necessity.insert(old_id, true), None); + assert!(!necessity.contains_key(old_id), "older key was inserted"); + necessity.remove(new_id); + necessity.insert(old_id, true); + assert!(necessity.contains_key(old_id)); + } + } +} diff --git a/core/malsim-core/src/dyna_simulator.rs b/core/malsim-core/src/dyna_simulator.rs new file mode 100644 index 00000000..7083c9cd --- /dev/null +++ b/core/malsim-core/src/dyna_simulator.rs @@ -0,0 +1,366 @@ +//! The pure-Rust reset/step orchestration of `malsim`'s simulator over a +//! *dynamic* instance model/attack graph, where model effects attached to +//! attack steps/defenses add/remove assets and associations (and so attack +//! graph nodes) mid-episode: a port of +//! `python/malsim/dyna_mal_simulator/simulator.py::dyna_reset`/`dyna_step`. +//! +//! Built by composition over [`Simulator`] - the Rust analogue of +//! `DynaMalSimulator(MalSimulator)` (`PORTING_NOTES.md` §11): reset is +//! "restore the model to its pristine snapshot, then the `Simulator` +//! reset", and step swaps `defender_step`/`attacker_step` for the +//! model-effect-aware `dyna_defender_step`/`dyna_attacker_step` but shares +//! the `Simulator`'s per-agent bookkeeping (`update_attacker_runtimes`/ +//! `update_defender_runtimes`). + +use crate::types::NodeSet; +use std::cell::RefCell; +use std::collections::{BTreeMap, HashMap}; +use std::rc::Rc; + +use maltoolbox_attackgraph::{AttackGraph, AttackGraphNodeId}; +use maltoolbox_model::Model; + +use crate::dyna_attacker_step::dyna_attacker_step; +use crate::dyna_defender_step::dyna_defender_step; +use crate::dyna_graph_state::GraphStateDelta; +use crate::history::StepRecord; +use crate::model_effects::ModEffectOp; +use crate::model_state::{capture_model_snapshot, reset_model_effects, ModelSnapshot}; +use crate::settings::{FlatAgentSettings, MalSimulatorSettings}; +use crate::simulator::{ + agent_actions, check_action_agents, push_step_record, update_attacker_runtimes, + update_defender_runtimes, AttackerResults, SimState, Simulator, SimulatorError, StepOutcome, +}; + +/// Everything a single `DynaSimulator::step` produced: the same per-agent +/// deltas as a static step, plus the model modifications its model effects +/// made - mirrors `DynaMalSimulatorState` extending `MalSimulatorState` +/// with a `modification_record`. +#[derive(Debug, Clone)] +pub struct DynaStepOutcome { + pub step: StepOutcome, + /// This step's model-effect modification record, in the order the + /// modifications were applied. Non-empty means the graph (and + /// `graph_state`, which grows to cover newly created nodes) may have + /// changed shape this step. + pub step_modification_record: Vec, +} + +/// Port of `DynaMalSimulator`'s reset/step orchestration, over a dynamic +/// instance model/attack graph. Both the `Model` and the `AttackGraph` +/// built from it are shared handles (§2.2, §6 B3), mutated in place by +/// model effects. +pub struct DynaSimulator { + sim: Simulator, + model: Rc>, + /// The model as it was at `DynaSimulator::new` - every `reset` restores + /// it (mirrors `DynaMalSimulator.__init__` capturing + /// `attack_graph.model.to_dict()` once, before any mutation). + snapshot: ModelSnapshot, +} + +impl DynaSimulator { + /// A dyna simulator over `graph`/`model` (`graph` must have been built + /// from `model`): captures the pristine model snapshot every `reset` + /// restores to. + pub fn new(graph: Rc>, model: Rc>) -> Self { + let snapshot = capture_model_snapshot(&model.borrow()); + DynaSimulator { + sim: Simulator::new(graph), + model, + snapshot, + } + } + + pub fn graph(&self) -> &Rc> { + self.sim.graph() + } + + pub fn model(&self) -> &Rc> { + &self.model + } + + /// The state built by the last successful `reset` (and advanced by every + /// `step` since) - `None` before the first successful `reset`. + pub fn state(&self) -> Option<&SimState> { + self.sim.state() + } + + /// Restores the shared `Model`/`AttackGraph` to the snapshot captured at + /// `new` (`reset_model_effects`). Restoring an already-pristine model + /// is a no-op. + /// + /// `reset` does this itself, but a caller that resolves per-node agent + /// settings against the graph (`FlatAgentSettings`) must call this + /// first: nodes that the previous episode's model effects removed are + /// only regenerated (with new ids) by the restore, so flattening + /// against the mutated graph would miss them. + pub fn restore_model(&mut self) -> Result<(), SimulatorError> { + let mut graph = self.sim.graph().borrow_mut(); + let mut model = self.model.borrow_mut(); + reset_model_effects(&mut graph, &mut model, &self.snapshot)?; + Ok(()) + } + + /// Resets the simulator: first restores the shared `Model`/`AttackGraph` + /// (`restore_model`, mirroring `dyna_reset`'s `reset_model_effects` + /// call), then resets exactly like `Simulator::reset`. `agents` must + /// already be resolved against the restored graph, so call + /// `restore_model` before flattening them. + pub fn reset( + &mut self, + settings: &MalSimulatorSettings, + agents: Vec<(String, FlatAgentSettings)>, + seed: u64, + ) -> Result<&SimState, SimulatorError> { + self.restore_model()?; + self.sim.reset(settings, agents, seed) + } + + /// Same two-phase shape as `Simulator::step`, except defenders/attackers + /// act via `dyna_defender_step`/`dyna_attacker_step` (model-effect-aware, + /// mutating the shared `AttackGraph`/`Model` in place and folding any + /// newly-created nodes into `graph_state`/`enabled_defenses` + /// internally). `actions` works as in `Simulator::step`. + pub fn step( + &mut self, + actions: &HashMap>, + ) -> Result { + let graph_rc = self.sim.graph().clone(); + let model_rc = self.model.clone(); + let state = self.sim.state_mut()?; + check_action_agents(state, actions)?; + + let attacker_names: Vec = state.attackers.keys().cloned().collect(); + let defender_names: Vec = state.defenders.keys().cloned().collect(); + let mut modification_record: Vec = Vec::new(); + let mut graph_state_delta = GraphStateDelta::default(); + + // --- Defenders act first --- + let mut step_enabled_defenses: NodeSet = NodeSet::default(); + { + let mut graph = graph_rc.borrow_mut(); + let mut model = model_rc.borrow_mut(); + for name in &defender_names { + let runtime = &state.defenders[name]; + let (enabled, ops, delta) = dyna_defender_step( + &mut graph, + &mut model, + &mut state.rng, + state.settings.ttc_mode, + state.settings.run_defense_step_bernoullis, + state.settings.run_attack_step_bernoullis, + agent_actions(actions, name), + &runtime.action_surface, + &mut state.graph_state, + &mut state.enabled_defenses, + )?; + step_enabled_defenses.extend(enabled); + modification_record.extend(ops); + graph_state_delta.merge(delta); + } + } + // `dyna_defender_step` only folds *newly model-effect-created* + // defense nodes into `enabled_defenses` internally (via + // `fold_new_nodes_into_graph_state`) - the defenses actually + // enabled by this step's requested actions still need merging + // here, same as `Simulator::step` does for `defender_step`. + let new_enabled_defenses: NodeSet = step_enabled_defenses + .difference(&state.enabled_defenses) + .copied() + .collect(); + state + .enabled_defenses + .extend(step_enabled_defenses.iter().copied()); + + // --- Attackers act afterwards --- + let mut step_compromised_nodes: NodeSet = NodeSet::default(); + let mut attacker_results: AttackerResults = BTreeMap::new(); + { + let mut graph = graph_rc.borrow_mut(); + let mut model = model_rc.borrow_mut(); + for name in &attacker_names { + let runtime = &state.attackers[name]; + let (compromised, attempted, ops, delta) = dyna_attacker_step( + &mut graph, + &mut model, + &mut state.rng, + state.settings.ttc_mode, + state.settings.run_defense_step_bernoullis, + state.settings.run_attack_step_bernoullis, + agent_actions(actions, name), + &runtime.entry_points, + &runtime.action_surface, + &runtime.performed_nodes, + &runtime.num_attempts, + runtime.ttc_dist_overrides.as_ref(), + Some(&runtime.ttc_values), + &mut state.graph_state, + &mut state.enabled_defenses, + )?; + step_compromised_nodes.extend(compromised.iter().copied()); + modification_record.extend(ops); + graph_state_delta.merge(delta); + attacker_results.insert(name.clone(), (compromised, attempted)); + } + } + + let graph = graph_rc.borrow(); + let (attackers, attacker_records) = update_attacker_runtimes( + &graph, + state, + &attacker_names, + attacker_results, + // A model effect can change the graph and graph state, which + // the incremental surface update can't follow. + modification_record + .is_empty() + .then_some(&new_enabled_defenses), + )?; + let (defenders, defender_records) = update_defender_runtimes( + &graph, + state, + &defender_names, + &step_enabled_defenses, + &step_compromised_nodes, + )?; + let record = StepRecord { + new_enabled_defenses, + graph_state_delta, + modification_record_len: state.history.modification_record().len(), + ..StepRecord::default() + }; + push_step_record( + &graph, + state, + actions, + record, + attacker_records, + defender_records, + ); + state + .history + .extend_modification_record(modification_record.clone()); + + Ok(DynaStepOutcome { + step: StepOutcome { + attackers, + defenders, + step_enabled_defenses, + }, + step_modification_record: modification_record, + }) + } +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::settings::FlatAttackerSettings; + use crate::test_fixtures::wiper_attack_graph; + + /// Settings with every bernoulli off, so tests are not at the mercy of + /// sampled impossible steps/pre-enabled defenses. + fn settings() -> MalSimulatorSettings { + MalSimulatorSettings { + run_defense_step_bernoullis: false, + run_attack_step_bernoullis: false, + ..MalSimulatorSettings::default() + } + } + + fn attacker(entry_points: &[AttackGraphNodeId]) -> FlatAgentSettings { + FlatAgentSettings::Attacker(FlatAttackerSettings { + entry_points: entry_points.iter().copied().collect(), + ..FlatAttackerSettings::default() + }) + } + + fn actions( + entries: &[(&str, Vec)], + ) -> HashMap> { + entries + .iter() + .map(|(name, nodes)| (name.to_string(), nodes.clone())) + .collect() + } + + #[test] + fn step_before_reset_fails() { + let (graph, model) = wiper_attack_graph(); + let mut sim = + DynaSimulator::new(Rc::new(RefCell::new(graph)), Rc::new(RefCell::new(model))); + assert!(sim.state().is_none()); + assert!(matches!( + sim.step(&HashMap::new()), + Err(SimulatorError::NotReset) + )); + } + + #[test] + fn dyna_reset_and_step_run_model_effects() { + let (graph, model) = wiper_attack_graph(); + let infect = graph.full_name_to_node["InfectedDevice:infect"]; + let initial_node_count = graph.nodes.len(); + let graph_rc = Rc::new(RefCell::new(graph)); + let model_rc = Rc::new(RefCell::new(model)); + let mut sim = DynaSimulator::new(graph_rc.clone(), model_rc.clone()); + + let s = MalSimulatorSettings { + compromise_entrypoints_at_start: false, + ..settings() + }; + let agents = || vec![("WiperController".to_string(), attacker(&[infect]))]; + sim.reset(&s, agents(), 42).unwrap(); + + let outcome = sim + .step(&actions(&[("WiperController", vec![infect])])) + .unwrap(); + assert!(outcome.step.attackers["WiperController"] + .step_performed_nodes + .contains(&infect)); + assert!(!outcome.step_modification_record.is_empty()); + assert!(graph_rc.borrow().nodes.len() > initial_node_count); + assert!(model_rc + .borrow() + .assets + .values() + .any(|a| a.name == "Wiper-7")); + + // A second reset restores the pristine model/graph. + sim.reset(&s, agents(), 42).unwrap(); + assert_eq!(graph_rc.borrow().nodes.len(), initial_node_count); + } + + #[test] + fn restore_model_undoes_model_effects_and_is_idempotent() { + let (graph, model) = wiper_attack_graph(); + let infect = graph.full_name_to_node["InfectedDevice:infect"]; + let graph_rc = Rc::new(RefCell::new(graph)); + let mut sim = DynaSimulator::new(graph_rc.clone(), Rc::new(RefCell::new(model))); + + // Restoring a pristine model changes nothing (not even node ids), + // so `reset`'s own restore after an explicit one is harmless. + let pristine: Vec<_> = graph_rc.borrow().nodes.keys().collect(); + sim.restore_model().unwrap(); + assert_eq!(graph_rc.borrow().nodes.keys().collect::>(), pristine); + + let s = MalSimulatorSettings { + compromise_entrypoints_at_start: false, + ..settings() + }; + let agents = || vec![("WiperController".to_string(), attacker(&[infect]))]; + sim.reset(&s, agents(), 1).unwrap(); + sim.step(&actions(&[("WiperController", vec![infect])])) + .unwrap(); + assert!(graph_rc.borrow().nodes.len() > pristine.len()); + + // The explicit restore brings the graph back before `reset` runs, + // and `reset`'s own restore is then a no-op. + sim.restore_model().unwrap(); + let restored: Vec<_> = graph_rc.borrow().nodes.keys().collect(); + assert_eq!(restored.len(), pristine.len()); + sim.reset(&s, agents(), 1).unwrap(); + assert_eq!(graph_rc.borrow().nodes.keys().collect::>(), restored); + } +} diff --git a/core/malsim-core/src/event_logger.rs b/core/malsim-core/src/event_logger.rs new file mode 100644 index 00000000..9ed11e1d --- /dev/null +++ b/core/malsim-core/src/event_logger.rs @@ -0,0 +1,483 @@ +//! Rust port of `python/malsim/mal_simulator/event_logger.py` - see +//! `PORTING_NOTES.md` §5 Phase A6. +//! +//! Per A6's own scope note, `LogEntry`'s Python `detector: Detector`/ +//! `trigger: AttackGraphNode` fields become id-based here: `trigger` is an +//! `AttackGraphNodeId`, and a detector is identified by a `DetectorId` - +//! `(AttackGraphNodeId, String)`, the id of the node it's attached to plus +//! its label key in that node's `detectors: HashMap` map +//! (mal-toolbox's `Detector` type has no id of its own - confirmed by +//! reading `maltoolbox_attackgraph::generate::create_detectors`, which +//! always sets `Detector.node` to the very node whose `detectors` map it's +//! inserted into, so `(node_id, label)` is a sound, resolvable identity). +//! `context: HashMap` is the id-based +//! equivalent of Python's `dict[str, AttackGraphNode]`. Resolving a +//! `LogEntry` back to the Python `LogEntry` dataclass (ids -> real +//! `Detector`/`AttackGraphNode` objects) is Python's job when crossing the +//! FFI boundary (§3), not this module's - see `PORTING_NOTES.md` §10. + +use crate::graph_utils::{node_order_key, sort_by_node_id}; +use crate::types::NodeSet; +use std::collections::HashMap; +use std::fmt; + +use maltoolbox_attackgraph::{AttackGraph, AttackGraphNode, AttackGraphNodeId, Detector}; +use rand::{Rng, RngExt}; + +/// Identifies a `Detector` within the graph - see module docs for why this +/// (rather than some id native to `Detector` itself) is the right shape. +pub type DetectorId = (AttackGraphNodeId, String); + +/// Id-based port of the `LogEntry` Python dataclass - see module docs. +#[derive(Debug, Clone, PartialEq)] +pub struct LogEntry { + pub timestep: u64, + pub detector_id: DetectorId, + pub trigger: AttackGraphNodeId, + pub context: HashMap, + pub false_positive: bool, +} + +#[derive(Debug, Clone, PartialEq, Eq)] +pub enum EventLoggerError { + /// Mirrors Python's bare `assert attack_step.model_asset is not None` + /// in `collect_logs`. + MissingModelAsset(AttackGraphNodeId), + /// Mirrors Python's unhandled `StopIteration` out of `get_context`'s + /// `next(... if node in previous_compromised_nodes)` when none of a + /// context label's potential nodes were previously compromised. + NoCompromisedContextNode(AttackGraphNodeId, String), + /// Mirrors Python's unhandled `StopIteration` out of + /// `get_random_context`'s `next(node for node in + /// potential_context_nodes)` when a context label's potential-node set + /// is empty. + EmptyContextCandidates(AttackGraphNodeId, String), +} + +impl fmt::Display for EventLoggerError { + fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { + match self { + EventLoggerError::MissingModelAsset(id) => { + write!(f, "attack step {id:?} has no model asset") + } + EventLoggerError::NoCompromisedContextNode(id, label) => write!( + f, + "detector on {id:?} has no previously-compromised candidate for context label \"{label}\"" + ), + EventLoggerError::EmptyContextCandidates(id, label) => write!( + f, + "detector on {id:?} has no candidate nodes at all for context label \"{label}\"" + ), + } + } +} + +impl std::error::Error for EventLoggerError {} + +/// A rate is "truthy" in Python exactly when it's `Some` and non-zero +/// (`if detector.fprate:`/`if detector.tprate:`/`not detector.tprate` - +/// `None` and `0.0` are both falsy, matching every other numeric Python +/// truthiness check, e.g. a negative rate is still truthy). +fn rate_is_truthy(rate: Option) -> bool { + rate.is_some_and(|r| r != 0.0) +} + +/// Flattened `(node_id, label, detector)` view over every detector in the +/// graph - the graph-independent-of-Python equivalent of Python's +/// `attack_graph.detectors` list (itself seeded, in the PyO3 layer, from +/// exactly this same per-node walk - see `PORTING_NOTES.md` §10). +fn all_detectors( + graph: &AttackGraph, +) -> impl Iterator { + graph.nodes.iter().flat_map(|(node_id, node)| { + // `detectors` is a std `HashMap`: iterate it by label. + sorted_detectors(node) + .into_iter() + .map(move |(label, detector)| (node_id, label, detector)) + }) +} + +/// `node`'s detectors ordered by label (a std `HashMap` iterates in a +/// per-instance random order). +fn sorted_detectors(node: &AttackGraphNode) -> Vec<(&String, &Detector)> { + let mut detectors: Vec<(&String, &Detector)> = node.detectors.iter().collect(); + detectors.sort_unstable_by(|a, b| a.0.cmp(b.0)); + detectors +} + +/// `detector.potential_context` ordered by label. +fn sorted_potential_context( + detector: &Detector, +) -> Vec<(&String, &std::collections::HashSet)> { + let mut contexts: Vec<_> = detector.potential_context.iter().collect(); + contexts.sort_unstable_by(|a, b| a.0.cmp(b.0)); + contexts +} + +/// Port of `get_context`. When several candidates are compromised the one +/// with the smallest node id is the context. +fn get_context( + graph: &AttackGraph, + detector: &Detector, + previous_compromised_nodes: &NodeSet, +) -> Result, EventLoggerError> { + let mut context_nodes = HashMap::new(); + for (label, potential_context_nodes) in sorted_potential_context(detector) { + let node_id = potential_context_nodes + .iter() + .copied() + .filter(|id| previous_compromised_nodes.contains(id)) + .min_by_key(|&id| node_order_key(graph, id)) + .ok_or_else(|| { + EventLoggerError::NoCompromisedContextNode(detector.node, label.clone()) + })?; + context_nodes.insert(label.clone(), node_id); + } + Ok(context_nodes) +} + +/// Port of `get_random_context`. +fn get_random_context( + graph: &AttackGraph, + detector: &Detector, +) -> Result, EventLoggerError> { + let mut context_nodes = HashMap::new(); + for (label, potential_context_nodes) in sorted_potential_context(detector) { + // Python takes an arbitrary element; take the smallest node id. + let node_id = potential_context_nodes + .iter() + .copied() + .min_by_key(|&id| node_order_key(graph, id)) + .ok_or_else(|| { + EventLoggerError::EmptyContextCandidates(detector.node, label.clone()) + })?; + context_nodes.insert(label.clone(), node_id); + } + Ok(context_nodes) +} + +/// Port of `collect_false_positives`. `get_random_context` is only called +/// (and can only error) for a detector that actually fires, mirroring +/// Python calling it inside the `if` block, not before. +pub fn collect_false_positives( + iteration: u64, + graph: &AttackGraph, + rng: &mut impl Rng, +) -> Result, EventLoggerError> { + let mut logs = Vec::new(); + for (node_id, label, detector) in all_detectors(graph) { + let fires = + rate_is_truthy(detector.fprate) && detector.fprate.unwrap() >= rng.random::(); + if fires { + logs.push(LogEntry { + timestep: iteration, + detector_id: (node_id, label.clone()), + trigger: detector.node, + context: get_random_context(graph, detector)?, + false_positive: true, + }); + } + } + Ok(logs) +} + +/// Port of `collect_logs`. `get_context` is called unconditionally for +/// every detector, before the true-positive roll, mirroring Python's +/// `labeled_steps = get_context(...)` being evaluated before the +/// `if not detector.tprate or ...:` check. +pub fn collect_logs( + iteration: u64, + graph: &AttackGraph, + step_compromised_nodes: impl IntoIterator, + previous_compromised_nodes: &NodeSet, + rng: &mut impl Rng, +) -> Result, EventLoggerError> { + let mut logs = Vec::new(); + // Detector rolls draw from the RNG and the logs come out in this + // order, so go through the compromised nodes in node-id order. + let mut step_compromised_nodes: Vec = + step_compromised_nodes.into_iter().collect(); + sort_by_node_id(graph, &mut step_compromised_nodes); + for attack_step_id in step_compromised_nodes { + // A node can be genuinely compromised this step and still vanish + // from the graph before this loop reaches it: in the dyna path, a + // step's own model effect (executed right after its compromise - + // `dyna_attacker_step`) can remove the very asset that step + // belongs to, which removes the step's own node too + // (`AttackGraph::partially_regenerate_graph`'s `nodes_to_be_removed` + // - see `PORTING_NOTES.md` §0 B5 for the traced repro). The + // compromise itself is unaffected - it's already recorded in + // `performed_nodes` by the caller - there's just no live node left + // to read detector data from, so there's nothing to log for it. + let Some(node) = graph.nodes.get(attack_step_id) else { + continue; + }; + if node.detectors.is_empty() { + // Mirrors Python's `assert attack_step.model_asset is not + // None` living *inside* `for detector in + // attack_step.detectors.values()` - it only ever runs when + // there's at least one detector to process, so a + // model-asset-less node with no detectors is never checked + // (found during Phase A9 end-to-end wiring - see + // PORTING_NOTES.md §10). + continue; + } + if node.model_asset.is_none() { + return Err(EventLoggerError::MissingModelAsset(attack_step_id)); + } + for (label, detector) in sorted_detectors(node) { + let context = get_context(graph, detector, previous_compromised_nodes)?; + let fires = + !rate_is_truthy(detector.tprate) || detector.tprate.unwrap() >= rng.random::(); + if fires { + logs.push(LogEntry { + timestep: iteration, + detector_id: (attack_step_id, label.clone()), + trigger: attack_step_id, + context, + false_positive: false, + }); + } + } + } + Ok(logs) +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::test_fixtures::{add_dummy_node, dummy_graph}; + use rand::rngs::StdRng; + use rand::SeedableRng; + use std::collections::HashSet; + + fn add_detector( + graph: &mut AttackGraph, + node_id: AttackGraphNodeId, + label: &str, + tprate: Option, + fprate: Option, + potential_context: HashMap>, + ) { + graph.nodes[node_id].detectors.insert( + label.to_string(), + Detector { + name: Some(label.to_string()), + node: node_id, + potential_context, + tprate, + fprate, + }, + ); + } + + #[test] + fn collect_logs_missing_model_asset_errors() { + let mut graph = dummy_graph(); + let step = add_dummy_node(&mut graph, "DummyOrAttackStep"); + // `add_dummy_node` never sets `model_asset` - mirrors Python's + // `assert attack_step.model_asset is not None`, which only runs + // once the step actually has a detector to process. + add_detector(&mut graph, step, "d", None, None, HashMap::new()); + let mut rng = StdRng::seed_from_u64(1); + let result = collect_logs(0, &graph, [step], &NodeSet::default(), &mut rng); + assert_eq!(result, Err(EventLoggerError::MissingModelAsset(step))); + } + + #[test] + fn collect_logs_missing_model_asset_without_detectors_does_not_error() { + let mut graph = dummy_graph(); + let step = add_dummy_node(&mut graph, "DummyOrAttackStep"); + // No detectors attached and no `model_asset` set - mirrors + // Python's assert never firing when `attack_step.detectors` is + // empty (found during Phase A9 end-to-end wiring, see + // PORTING_NOTES.md §10: a real scenario's pre-compromised node + // with no model asset and no detectors used to error here). + let mut rng = StdRng::seed_from_u64(1); + let result = collect_logs(0, &graph, [step], &NodeSet::default(), &mut rng); + assert_eq!(result, Ok(Vec::new())); + } + + #[test] + fn collect_logs_no_tprate_always_true_positive_no_context() { + let mut graph = dummy_graph(); + let step = add_dummy_node(&mut graph, "DummyOrAttackStep"); + graph.nodes[step].model_asset = Some(1); + add_detector(&mut graph, step, "d", None, None, HashMap::new()); + + let mut rng = StdRng::seed_from_u64(2); + let logs = collect_logs(5, &graph, [step], &NodeSet::default(), &mut rng).unwrap(); + + assert_eq!(logs.len(), 1); + assert_eq!(logs[0].timestep, 5); + assert_eq!(logs[0].trigger, step); + assert_eq!(logs[0].detector_id, (step, "d".to_string())); + assert!(logs[0].context.is_empty()); + assert!(!logs[0].false_positive); + } + + #[test] + fn collect_logs_skips_a_node_removed_from_the_graph_since_compromise() { + // Mirrors the dyna-path repro in `PORTING_NOTES.md` §0 B5: a node + // id can be genuinely compromised this step and then vanish from + // the graph (its own model effect removed its own asset) before + // `collect_logs` runs - this must not panic, and a vanished node + // has no live detectors left to log, even though it had one + // (tprate=1.0, would have always fired) while it was still live. + let mut graph = dummy_graph(); + let step = add_dummy_node(&mut graph, "DummyOrAttackStep"); + graph.nodes[step].model_asset = Some(1); + add_detector(&mut graph, step, "d", Some(1.0), None, HashMap::new()); + graph.remove_node(step).unwrap(); + + let mut rng = StdRng::seed_from_u64(11); + let logs = collect_logs(0, &graph, [step], &NodeSet::default(), &mut rng).unwrap(); + assert!(logs.is_empty()); + } + + #[test] + fn collect_logs_tprate_one_always_true_positive() { + let mut graph = dummy_graph(); + let step = add_dummy_node(&mut graph, "DummyOrAttackStep"); + graph.nodes[step].model_asset = Some(1); + add_detector(&mut graph, step, "d", Some(1.0), None, HashMap::new()); + + let mut rng = StdRng::seed_from_u64(3); + let logs = collect_logs(0, &graph, [step], &NodeSet::default(), &mut rng).unwrap(); + assert_eq!(logs.len(), 1); + } + + #[test] + fn collect_logs_tprate_negative_is_truthy_but_never_fires() { + // Negative rates are still "truthy" in Python (only None/0.0 are + // falsy) but can never satisfy `rate >= roll` since `roll` is + // always >= 0.0 - mirrors `tests/test_event_logger.py:: + // test_logger_attacks_false_negative`'s `tprate=-1.0` case. + let mut graph = dummy_graph(); + let step = add_dummy_node(&mut graph, "DummyOrAttackStep"); + graph.nodes[step].model_asset = Some(1); + add_detector(&mut graph, step, "d", Some(-1.0), None, HashMap::new()); + + let mut rng = StdRng::seed_from_u64(4); + let logs = collect_logs(0, &graph, [step], &NodeSet::default(), &mut rng).unwrap(); + assert!(logs.is_empty()); + } + + #[test] + fn collect_logs_fills_context_from_previously_compromised_candidate() { + let mut graph = dummy_graph(); + let step = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let candidate = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let other_candidate = add_dummy_node(&mut graph, "DummyOrAttackStep"); + graph.nodes[step].model_asset = Some(1); + let potential: HashMap> = [( + "computer".to_string(), + [candidate, other_candidate].into_iter().collect(), + )] + .into_iter() + .collect(); + add_detector(&mut graph, step, "d", None, None, potential); + + let previous: NodeSet = [candidate].into_iter().collect(); + let mut rng = StdRng::seed_from_u64(5); + let logs = collect_logs(0, &graph, [step], &previous, &mut rng).unwrap(); + + assert_eq!(logs.len(), 1); + assert_eq!( + logs[0].context, + [("computer".to_string(), candidate)].into_iter().collect() + ); + } + + #[test] + fn collect_logs_context_errors_when_no_candidate_was_compromised() { + let mut graph = dummy_graph(); + let step = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let candidate = add_dummy_node(&mut graph, "DummyOrAttackStep"); + graph.nodes[step].model_asset = Some(1); + let potential: HashMap> = + [("computer".to_string(), [candidate].into_iter().collect())] + .into_iter() + .collect(); + add_detector(&mut graph, step, "d", None, None, potential); + + let mut rng = StdRng::seed_from_u64(6); + let result = collect_logs(0, &graph, [step], &NodeSet::default(), &mut rng); + assert_eq!( + result, + Err(EventLoggerError::NoCompromisedContextNode( + step, + "computer".to_string() + )) + ); + } + + #[test] + fn collect_false_positives_fprate_zero_never_fires() { + let mut graph = dummy_graph(); + let step = add_dummy_node(&mut graph, "DummyOrAttackStep"); + add_detector(&mut graph, step, "d", None, Some(0.0), HashMap::new()); + + let mut rng = StdRng::seed_from_u64(7); + let logs = collect_false_positives(0, &graph, &mut rng).unwrap(); + assert!(logs.is_empty()); + } + + #[test] + fn collect_false_positives_fprate_one_always_fires() { + let mut graph = dummy_graph(); + let step = add_dummy_node(&mut graph, "DummyOrAttackStep"); + add_detector(&mut graph, step, "d", None, Some(1.0), HashMap::new()); + + let mut rng = StdRng::seed_from_u64(8); + let logs = collect_false_positives(3, &graph, &mut rng).unwrap(); + + assert_eq!(logs.len(), 1); + assert_eq!(logs[0].timestep, 3); + assert_eq!(logs[0].trigger, step); + assert_eq!(logs[0].detector_id, (step, "d".to_string())); + assert!(logs[0].false_positive); + } + + #[test] + fn collect_false_positives_uses_random_context_candidate() { + let mut graph = dummy_graph(); + let step = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let candidate = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let potential: HashMap> = + [("computer".to_string(), [candidate].into_iter().collect())] + .into_iter() + .collect(); + add_detector(&mut graph, step, "d", None, Some(1.0), potential); + + let mut rng = StdRng::seed_from_u64(9); + let logs = collect_false_positives(0, &graph, &mut rng).unwrap(); + + assert_eq!(logs.len(), 1); + assert_eq!( + logs[0].context, + [("computer".to_string(), candidate)].into_iter().collect() + ); + } + + #[test] + fn collect_false_positives_empty_context_candidates_errors() { + let mut graph = dummy_graph(); + let step = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let potential: HashMap> = + [("computer".to_string(), HashSet::new())] + .into_iter() + .collect(); + add_detector(&mut graph, step, "d", None, Some(1.0), potential); + + let mut rng = StdRng::seed_from_u64(10); + let result = collect_false_positives(0, &graph, &mut rng); + assert_eq!( + result, + Err(EventLoggerError::EmptyContextCandidates( + step, + "computer".to_string() + )) + ); + } +} diff --git a/core/malsim-core/src/false_alerts.rs b/core/malsim-core/src/false_alerts.rs new file mode 100644 index 00000000..c672afd9 --- /dev/null +++ b/core/malsim-core/src/false_alerts.rs @@ -0,0 +1,209 @@ +//! Rust port of `python/malsim/mal_simulator/false_alerts.py` - see +//! `PORTING_NOTES.md` §5 Phase A6. +//! +//! Per §2.4, `NodePropertyRule[float]` (`false_positive_rates`/ +//! `false_negative_rates`) stays Python-only; Python flattens it once per +//! `reset()` into a plain `NodeMap` before crossing +//! into this hot path - the same pattern A5 established for +//! `NodePropertyRule[bool]` (`attack_surface.rs`'s `actionable_steps`). +//! `None` means "no rule configured at all" (mirroring +//! `if false_positive_rates_rule:`/`if false_negative_rate_rule:` being +//! false - zero draws, empty result, not even a 0.0-rate check per node); +//! `Some(map)` holds exactly the already-flattened per-node rates, and a +//! node missing from the map still defaults to `0.0`, mirroring +//! `NodePropertyRule.value(node, 0.0)`'s own default argument. + +use crate::graph_utils::sort_by_node_id; +use crate::types::{NodeMap, NodeSet}; + +use maltoolbox_attackgraph::{AttackGraph, AttackGraphNodeId}; +use rand::{Rng, RngExt}; + +/// Port of `node_false_negative_rate`. +pub fn node_false_negative_rate( + false_negative_rates: Option<&NodeMap>, + node_id: AttackGraphNodeId, +) -> f64 { + false_negative_rates + .and_then(|rates| rates.get(&node_id)) + .copied() + .unwrap_or(0.0) +} + +/// Port of `generate_false_negatives`. +pub fn generate_false_negatives( + graph: &AttackGraph, + false_negative_rates: Option<&NodeMap>, + observed_nodes: &NodeSet, + rng: &mut impl Rng, +) -> NodeSet { + let Some(rates) = false_negative_rates else { + return NodeSet::default(); + }; + // One RNG draw per node: draw in node-id order, not set order, so the + // result only depends on the seed. + let mut candidates: Vec = observed_nodes.iter().copied().collect(); + sort_by_node_id(graph, &mut candidates); + candidates + .into_iter() + .filter(|&id| rng.random::() < node_false_negative_rate(Some(rates), id)) + .collect() +} + +/// Port of `node_false_positive_rate`. +pub fn node_false_positive_rate( + false_positive_rates: Option<&NodeMap>, + node_id: AttackGraphNodeId, +) -> f64 { + false_positive_rates + .and_then(|rates| rates.get(&node_id)) + .copied() + .unwrap_or(0.0) +} + +/// Port of `generate_false_positives`. Takes the attack-step ids directly +/// (`attack_graph.attack_steps` on the Python side, e.g. +/// `AttackGraph::attack_steps` in Rust) rather than the whole graph, since +/// that's the only thing this function reads off it. +pub fn generate_false_positives( + false_positive_rates: Option<&NodeMap>, + attack_step_ids: impl Iterator, + rng: &mut impl Rng, +) -> NodeSet { + let Some(rates) = false_positive_rates else { + return NodeSet::default(); + }; + attack_step_ids + .filter(|&id| rng.random::() < node_false_positive_rate(Some(rates), id)) + .collect() +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::test_fixtures::{add_dummy_node, dummy_graph}; + use rand::rngs::StdRng; + use rand::SeedableRng; + + /// Mints `n` distinct, real `AttackGraphNodeId`s. These functions are + /// graph-independent (plain id-keyed maps/sets), but `AttackGraphNodeId` + /// is a slotmap generational key - unlike `AttackStepId` it needs no + /// specific `step_type`, but it still can't be fabricated from a raw + /// integer, so a throwaway graph mints real ones instead (mirroring + /// `graph_utils.rs`/`necessity.rs`'s own test fixtures). + fn ids(n: usize) -> Vec { + let mut graph = dummy_graph(); + (0..n) + .map(|_| add_dummy_node(&mut graph, "DummyOrAttackStep")) + .collect() + } + + #[test] + fn node_false_negative_rate_defaults_to_zero_without_rule() { + let [n1] = ids(1)[..] else { unreachable!() }; + assert_eq!(node_false_negative_rate(None, n1), 0.0); + } + + #[test] + fn node_false_negative_rate_defaults_to_zero_for_unmapped_node() { + let [n1, n2] = ids(2)[..] else { unreachable!() }; + let rates: NodeMap<_> = [(n1, 0.5)].into_iter().collect(); + assert_eq!(node_false_negative_rate(Some(&rates), n2), 0.0); + } + + #[test] + fn node_false_negative_rate_returns_mapped_value() { + let [n1] = ids(1)[..] else { unreachable!() }; + let rates: NodeMap<_> = [(n1, 0.5)].into_iter().collect(); + assert_eq!(node_false_negative_rate(Some(&rates), n1), 0.5); + } + + #[test] + fn node_false_positive_rate_defaults_to_zero_without_rule() { + let [n1] = ids(1)[..] else { unreachable!() }; + assert_eq!(node_false_positive_rate(None, n1), 0.0); + } + + #[test] + fn node_false_positive_rate_returns_mapped_value() { + let [n1] = ids(1)[..] else { unreachable!() }; + let rates: NodeMap<_> = [(n1, 0.5)].into_iter().collect(); + assert_eq!(node_false_positive_rate(Some(&rates), n1), 0.5); + } + + #[test] + fn generate_false_negatives_empty_without_rule() { + let observed: NodeSet = ids(2).into_iter().collect(); + let mut rng = StdRng::seed_from_u64(1); + let result = generate_false_negatives(&dummy_graph(), None, &observed, &mut rng); + assert!(result.is_empty()); + } + + #[test] + fn generate_false_negatives_rate_one_always_fires() { + let observed: NodeSet = ids(3).into_iter().collect(); + let rates: NodeMap<_> = observed.iter().map(|&n| (n, 1.0)).collect(); + let mut rng = StdRng::seed_from_u64(2); + let result = generate_false_negatives(&dummy_graph(), Some(&rates), &observed, &mut rng); + assert_eq!(result, observed); + } + + #[test] + fn generate_false_negatives_rate_zero_never_fires() { + let observed: NodeSet = ids(3).into_iter().collect(); + let rates: NodeMap<_> = observed.iter().map(|&n| (n, 0.0)).collect(); + let mut rng = StdRng::seed_from_u64(3); + let result = generate_false_negatives(&dummy_graph(), Some(&rates), &observed, &mut rng); + assert!(result.is_empty()); + } + + #[test] + fn generate_false_negatives_only_considers_observed_nodes() { + let [n1, n2] = ids(2)[..] else { unreachable!() }; + let observed: NodeSet = [n1].into_iter().collect(); + // n2 has rate 1.0 but isn't in `observed_nodes` - must never appear + // in the result, mirroring Python's comprehension iterating + // `observed_nodes`, never the rate map's own keys. + let rates: NodeMap<_> = [(n1, 1.0), (n2, 1.0)].into_iter().collect(); + let mut rng = StdRng::seed_from_u64(4); + let result = generate_false_negatives(&dummy_graph(), Some(&rates), &observed, &mut rng); + assert_eq!(result, observed); + } + + #[test] + fn generate_false_positives_empty_without_rule() { + let steps = ids(2).into_iter(); + let mut rng = StdRng::seed_from_u64(5); + let result = generate_false_positives(None, steps, &mut rng); + assert!(result.is_empty()); + } + + #[test] + fn generate_false_positives_rate_one_always_fires() { + let steps = ids(3); + let rates: NodeMap<_> = steps.iter().map(|&n| (n, 1.0)).collect(); + let mut rng = StdRng::seed_from_u64(6); + let result = generate_false_positives(Some(&rates), steps.iter().copied(), &mut rng); + assert_eq!(result, steps.into_iter().collect::()); + } + + #[test] + fn generate_false_positives_rate_zero_never_fires() { + let steps = ids(3); + let rates: NodeMap<_> = steps.iter().map(|&n| (n, 0.0)).collect(); + let mut rng = StdRng::seed_from_u64(7); + let result = generate_false_positives(Some(&rates), steps.into_iter(), &mut rng); + assert!(result.is_empty()); + } + + #[test] + fn generate_false_positives_unmapped_node_defaults_to_zero_rate() { + let [n1] = ids(1)[..] else { unreachable!() }; + // No entry for n1 in the rate map at all -> defaults to 0.0, never + // fires, mirroring `node_false_positive_rate`'s default. + let rates: NodeMap = NodeMap::default(); + let mut rng = StdRng::seed_from_u64(8); + let result = generate_false_positives(Some(&rates), std::iter::once(n1), &mut rng); + assert!(result.is_empty()); + } +} diff --git a/core/malsim-core/src/graph_state.rs b/core/malsim-core/src/graph_state.rs new file mode 100644 index 00000000..00cb1c5f --- /dev/null +++ b/core/malsim-core/src/graph_state.rs @@ -0,0 +1,506 @@ +//! Rust port of `python/malsim/mal_simulator/graph_state.py` and the +//! graph-node-dependent half of `ttc_utils.py` (`default_ttc_dist`, +//! `TTCDist.from_node`, `attack_step_ttc_value(s)`, +//! `get_pre_enabled_defenses`, `get_impossible_attack_steps`) - see +//! `PORTING_NOTES.md` §5 Phase A3. +//! +//! Each graph-dependent function here is split into a thin +//! `AttackGraphNode`-reading wrapper plus a graph-independent helper that +//! does the actual logic over plain data (`&TtcDist`, `Option<&Value>`, +//! `AttackStepType`) - e.g. `attack_step_ttc_value` delegates to +//! `ttc_value_for_dist`, `resolve_ttc_dist` delegates to +//! `resolve_ttc_dist_from_parts`. This isn't a stylistic preference: it's +//! what makes the actual logic unit-testable without a real +//! `AttackGraphNode`, which (per `PORTING_NOTES.md` §10) needs a real +//! `maltoolbox_language::graph::LanguageGraph` to mint (now available to +//! tests via `test_fixtures`, but these helpers stay graph-independent). +//! The thin wrappers themselves are untested one-liners. + +use crate::types::{NodeDenseMap, NodeMap, NodeSet}; +use std::fmt; + +use maltoolbox_attackgraph::{AttackGraph, AttackGraphNode}; +use maltoolbox_language::graph::attack_step::AttackStepType; +use rand::Rng; +use serde_json::Value; + +use crate::necessity::{calculate_necessity, NecessityError}; +use crate::ttc::{named_ttc_dist, TtcDist, TtcDistError}; + +/// Port of `config/sim_settings.py`'s `TTCMode`. Pulled forward from its +/// usual home (full `MalSimulatorSettings` porting is A9's job) because +/// `attack_step_ttc_value`'s mode dispatch needs it now. +#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)] +pub enum TtcMode { + EffortBasedPerStepSample, + PerStepSample, + PreSample, + ExpectedValue, + Disabled, +} + +impl TtcMode { + /// Parses the Python enum member name (`TTCMode[name]`), e.g. + /// `"PRE_SAMPLE"`. + pub fn from_name(name: &str) -> Option { + match name { + "EFFORT_BASED_PER_STEP_SAMPLE" => Some(TtcMode::EffortBasedPerStepSample), + "PER_STEP_SAMPLE" => Some(TtcMode::PerStepSample), + "PRE_SAMPLE" => Some(TtcMode::PreSample), + "EXPECTED_VALUE" => Some(TtcMode::ExpectedValue), + "DISABLED" => Some(TtcMode::Disabled), + _ => None, + } + } + + /// Parses the Python enum value (`TTCMode(value)`), e.g. `2` for + /// `PRE_SAMPLE`. + pub fn from_value(value: u8) -> Option { + match value { + 0 => Some(TtcMode::EffortBasedPerStepSample), + 1 => Some(TtcMode::PerStepSample), + 2 => Some(TtcMode::PreSample), + 3 => Some(TtcMode::ExpectedValue), + 4 => Some(TtcMode::Disabled), + _ => None, + } + } + + /// The Python enum value (`TTCMode.value`). + pub fn value(self) -> u8 { + match self { + TtcMode::EffortBasedPerStepSample => 0, + TtcMode::PerStepSample => 1, + TtcMode::PreSample => 2, + TtcMode::ExpectedValue => 3, + TtcMode::Disabled => 4, + } + } + + /// The Python enum member name (`TTCMode.name`). + pub fn name(self) -> &'static str { + match self { + TtcMode::EffortBasedPerStepSample => "EFFORT_BASED_PER_STEP_SAMPLE", + TtcMode::PerStepSample => "PER_STEP_SAMPLE", + TtcMode::PreSample => "PRE_SAMPLE", + TtcMode::ExpectedValue => "EXPECTED_VALUE", + TtcMode::Disabled => "DISABLED", + } + } +} + +#[derive(Debug, Clone, PartialEq)] +pub enum GraphStateError { + TtcDist(TtcDistError), + Necessity(NecessityError), + /// Mirrors Python's `default_ttc_dist`'s `ValueError` for a + /// `step_type` that isn't `defense`/`or`/`and`. + NoDefaultTtcDist(String), +} + +impl From for GraphStateError { + fn from(e: TtcDistError) -> Self { + GraphStateError::TtcDist(e) + } +} + +impl From for GraphStateError { + fn from(e: NecessityError) -> Self { + GraphStateError::Necessity(e) + } +} + +impl fmt::Display for GraphStateError { + fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { + match self { + GraphStateError::TtcDist(e) => write!(f, "{e}"), + GraphStateError::Necessity(e) => write!(f, "{e}"), + GraphStateError::NoDefaultTtcDist(step_type) => write!( + f, + "can only get default TTC of defense and attack steps, not of \"{step_type}\" steps" + ), + } + } +} + +impl std::error::Error for GraphStateError {} + +/// Port of `default_ttc_dist`. Takes the node's `AttackStepType` rather +/// than a whole `AttackGraphNode` (Python's version only ever reads +/// `.type` off the node) - pure enum dispatch, graph-independent. +/// `Exist`/`NotExist` have no default TTC and yield `NoDefaultTtcDist`. +pub fn default_ttc_dist_for_step_type( + step_type: AttackStepType, +) -> Result { + match step_type { + AttackStepType::Defense => { + Ok(named_ttc_dist("Disabled").expect("'Disabled' is a known named dist")) + } + AttackStepType::Or | AttackStepType::And => { + Ok(named_ttc_dist("Instant").expect("'Instant' is a known named dist")) + } + AttackStepType::Exist | AttackStepType::NotExist => Err(GraphStateError::NoDefaultTtcDist( + step_type.as_str().to_string(), + )), + } +} + +/// Port of `TTCDist.from_node`'s logic, decomposed onto plain data +/// (`ttc_json`/`step_type`) instead of a whole `AttackGraphNode` - see +/// module docs for why. +pub fn resolve_ttc_dist_from_parts( + ttc_json: Option<&Value>, + step_type: AttackStepType, + ttc_dist_override: Option<&TtcDist>, +) -> Result { + if let Some(dist) = ttc_dist_override { + return Ok(dist.clone()); + } + match ttc_json { + Some(value) => Ok(TtcDist::from_dict(value)?), + None => default_ttc_dist_for_step_type(step_type), + } +} + +/// Thin `AttackGraphNode`-reading wrapper around +/// `resolve_ttc_dist_from_parts`. +pub fn resolve_ttc_dist( + node: &AttackGraphNode, + ttc_dist_override: Option<&TtcDist>, +) -> Result { + resolve_ttc_dist_from_parts(node.ttc.as_ref(), node.step_type, ttc_dist_override) +} + +/// Graph-independent half of `attack_step_ttc_value`: what to do with an +/// already-resolved `TtcDist` for a given `TtcMode`. +pub fn ttc_value_for_dist( + ttc_dist: &TtcDist, + ttc_mode: TtcMode, + rng: &mut impl Rng, +) -> Option { + match ttc_mode { + TtcMode::ExpectedValue => Some(ttc_dist.expected_value()), + TtcMode::PreSample => Some(ttc_dist.sample_value(rng)), + TtcMode::EffortBasedPerStepSample | TtcMode::PerStepSample | TtcMode::Disabled => None, + } +} + +/// Port of `attack_step_ttc_value`. +pub fn attack_step_ttc_value( + node: &AttackGraphNode, + ttc_dist_override: Option<&TtcDist>, + ttc_mode: TtcMode, + rng: &mut impl Rng, +) -> Result, GraphStateError> { + let ttc_dist = resolve_ttc_dist(node, ttc_dist_override)?; + Ok(ttc_value_for_dist(&ttc_dist, ttc_mode, rng)) +} + +/// Port of `attack_step_ttc_values`. Iterates `graph.attack_steps` +/// (Rust's precomputed equivalent of Python's `graph.attack_steps` +/// property). +pub fn attack_step_ttc_values( + graph: &AttackGraph, + rng: &mut impl Rng, + ttc_mode: TtcMode, + ttc_dist_overrides: Option<&NodeMap>, +) -> Result, GraphStateError> { + let mut values = NodeMap::default(); + for &node_id in &graph.attack_steps { + let node = &graph.nodes[node_id]; + let override_dist = ttc_dist_overrides.and_then(|m| m.get(&node_id)); + if let Some(value) = attack_step_ttc_value(node, override_dist, ttc_mode, rng)? { + values.insert(node_id, value); + } + } + Ok(values) +} + +/// Graph-independent half of `get_pre_enabled_defenses`'s per-node body. +pub fn is_pre_enabled_for_dist(ttc_dist: &TtcDist, sample: bool, rng: &mut impl Rng) -> bool { + let p0 = ttc_dist.success_probability(0); + if p0 == 0.0 { + // never succeeds -> pre enabled + // TODO: is this correct? + return true; + } + if p0 == 1.0 { + // always succeeds -> not pre enabled + return false; + } + sample && ttc_dist.attempt_bernoulli(rng) +} + +/// Port of `get_pre_enabled_defenses`. +pub fn get_pre_enabled_defenses( + graph: &AttackGraph, + sample: bool, + rng: &mut impl Rng, +) -> Result { + let mut pre_enabled = NodeSet::default(); + for &node_id in &graph.defense_steps { + let node = &graph.nodes[node_id]; + if node.step_type != AttackStepType::Defense { + continue; + } + let ttc_dist = resolve_ttc_dist(node, None)?; + if is_pre_enabled_for_dist(&ttc_dist, sample, rng) { + pre_enabled.insert(node_id); + } + } + Ok(pre_enabled) +} + +/// Graph-independent half of `is_impossible_attack_step`. +pub fn is_impossible_for_dist(ttc_dist: &TtcDist, rng: &mut impl Rng) -> bool { + !ttc_dist.attempt_bernoulli(rng) +} + +/// Port of `is_impossible_attack_step`. +pub fn is_impossible_attack_step( + node: &AttackGraphNode, + ttc_dist_override: Option<&TtcDist>, + rng: &mut impl Rng, +) -> Result { + let ttc_dist = resolve_ttc_dist(node, ttc_dist_override)?; + Ok(is_impossible_for_dist(&ttc_dist, rng)) +} + +/// Port of `get_impossible_attack_steps`. +pub fn get_impossible_attack_steps( + graph: &AttackGraph, + rng: &mut impl Rng, + ttc_dist_overrides: Option<&NodeMap>, +) -> Result { + let mut impossible = NodeSet::default(); + for &node_id in &graph.attack_steps { + let node = &graph.nodes[node_id]; + let override_dist = ttc_dist_overrides.and_then(|m| m.get(&node_id)); + if is_impossible_attack_step(node, override_dist, rng)? { + impossible.insert(node_id); + } + } + Ok(impossible) +} + +/// Port of `GraphState`. +#[derive(Debug, Clone, PartialEq)] +pub struct GraphState { + pub ttc_values: NodeMap, + pub pre_enabled_defenses: NodeSet, + pub impossible_attack_steps: NodeSet, + pub necessity_per_node: NodeDenseMap, +} + +/// Port of `compute_initial_graph_state`. Takes the three relevant +/// `MalSimulatorSettings` fields directly rather than the whole settings +/// struct - full `MalSimulatorSettings` porting is A9's job (settings +/// flattening across the FFI boundary, §2.4), and this function doesn't +/// need any of its other fields. +pub fn compute_initial_graph_state( + graph: &AttackGraph, + ttc_mode: TtcMode, + run_defense_step_bernoullis: bool, + run_attack_step_bernoullis: bool, + rng: &mut impl Rng, +) -> Result { + let ttc_values = attack_step_ttc_values(graph, rng, ttc_mode, None)?; + let pre_enabled_defenses = get_pre_enabled_defenses(graph, run_defense_step_bernoullis, rng)?; + let impossible_attack_steps = if run_attack_step_bernoullis { + get_impossible_attack_steps(graph, rng, None)? + } else { + NodeSet::default() + }; + let necessity_per_node = calculate_necessity(graph, &pre_enabled_defenses)?; + + Ok(GraphState { + ttc_values, + pre_enabled_defenses, + impossible_attack_steps, + necessity_per_node, + }) +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::ttc::{DistFunction, Operation}; + use rand::rngs::StdRng; + use rand::SeedableRng; + + fn dist(function: DistFunction, args: &[f64]) -> TtcDist { + TtcDist::new(function, args.to_vec()).unwrap() + } + + // --- default_ttc_dist_for_step_type --- + + #[test] + fn default_ttc_dist_defense_is_disabled() { + assert_eq!( + default_ttc_dist_for_step_type(AttackStepType::Defense).unwrap(), + named_ttc_dist("Disabled").unwrap() + ); + } + + #[test] + fn default_ttc_dist_or_and_and_is_instant() { + assert_eq!( + default_ttc_dist_for_step_type(AttackStepType::Or).unwrap(), + named_ttc_dist("Instant").unwrap() + ); + assert_eq!( + default_ttc_dist_for_step_type(AttackStepType::And).unwrap(), + named_ttc_dist("Instant").unwrap() + ); + } + + #[test] + fn default_ttc_dist_unsupported_step_type_errors() { + for step_type in [AttackStepType::Exist, AttackStepType::NotExist] { + let err = default_ttc_dist_for_step_type(step_type).unwrap_err(); + assert!(matches!(err, GraphStateError::NoDefaultTtcDist(_))); + } + } + + // --- resolve_ttc_dist_from_parts --- + + #[test] + fn resolve_ttc_dist_override_wins_regardless_of_ttc_json_or_step_type() { + let override_dist = dist(DistFunction::Exponential, &[0.5]); + let resolved = + resolve_ttc_dist_from_parts(None, AttackStepType::Defense, Some(&override_dist)) + .unwrap(); + assert_eq!(resolved, override_dist); + + let ttc_json = dist(DistFunction::Uniform, &[1.0, 2.0]).to_dict(); + let resolved = + resolve_ttc_dist_from_parts(Some(&ttc_json), AttackStepType::Or, Some(&override_dist)) + .unwrap(); + assert_eq!(resolved, override_dist); + } + + #[test] + fn resolve_ttc_dist_parses_explicit_ttc_json() { + let explicit = dist(DistFunction::Binomial, &[10.0, 0.1]) + .with_combine(dist(DistFunction::Bernoulli, &[0.5]), Operation::Addition); + let ttc_json = explicit.to_dict(); + let resolved = + resolve_ttc_dist_from_parts(Some(&ttc_json), AttackStepType::Or, None).unwrap(); + assert_eq!(resolved, explicit); + } + + #[test] + fn resolve_ttc_dist_falls_back_to_default_when_no_ttc_json() { + let resolved = resolve_ttc_dist_from_parts(None, AttackStepType::Defense, None).unwrap(); + assert_eq!(resolved, named_ttc_dist("Disabled").unwrap()); + + let err = resolve_ttc_dist_from_parts(None, AttackStepType::Exist, None).unwrap_err(); + assert!(matches!(err, GraphStateError::NoDefaultTtcDist(_))); + } + + // --- ttc_value_for_dist --- + + #[test] + fn ttc_value_expected_value_mode_returns_expected_value() { + let d = dist(DistFunction::Exponential, &[0.1]); + let mut rng = StdRng::seed_from_u64(0); + assert_eq!( + ttc_value_for_dist(&d, TtcMode::ExpectedValue, &mut rng), + Some(d.expected_value()) + ); + } + + #[test] + fn ttc_value_pre_sample_mode_matches_direct_sample_value_call() { + let d = dist(DistFunction::Exponential, &[0.1]); + let mut rng_a = StdRng::seed_from_u64(42); + let mut rng_b = StdRng::seed_from_u64(42); + assert_eq!( + ttc_value_for_dist(&d, TtcMode::PreSample, &mut rng_a), + Some(d.sample_value(&mut rng_b)) + ); + } + + #[test] + fn ttc_value_other_modes_return_none() { + let d = dist(DistFunction::Exponential, &[0.1]); + let mut rng = StdRng::seed_from_u64(0); + for mode in [ + TtcMode::Disabled, + TtcMode::PerStepSample, + TtcMode::EffortBasedPerStepSample, + ] { + assert_eq!(ttc_value_for_dist(&d, mode, &mut rng), None); + } + } + + // --- is_pre_enabled_for_dist --- + + // Note the apparent inversion here: `success_probability(0)` is + // `dist.cdf(0)`, which for `Bernoulli(p)` is `1 - p` - *not* `p` + // itself. So the *named* "Disabled" dist (`Bernoulli(0.0)`) has + // `cdf(0) == 1.0` (the "always succeeds" branch -> not pre-enabled), + // and "Enabled" (`Bernoulli(1.0)`) has `cdf(0) == 0.0` (the "never + // succeeds" branch -> pre-enabled). This matches Python's + // `get_pre_enabled_defenses` exactly, including its own "TODO: is + // this correct?" comment on this branch - ported as-is, not "fixed". + + #[test] + fn pre_enabled_degenerate_disabled_dist_is_never_pre_enabled() { + let d = named_ttc_dist("Disabled").unwrap(); // Bernoulli(0.0), cdf(0) == 1.0 + let mut rng = StdRng::seed_from_u64(0); + assert!(!is_pre_enabled_for_dist(&d, false, &mut rng)); + assert!(!is_pre_enabled_for_dist(&d, true, &mut rng)); + } + + #[test] + fn pre_enabled_degenerate_enabled_dist_is_always_pre_enabled() { + let d = named_ttc_dist("Enabled").unwrap(); // Bernoulli(1.0), cdf(0) == 0.0 + let mut rng = StdRng::seed_from_u64(0); + assert!(is_pre_enabled_for_dist(&d, false, &mut rng)); + assert!(is_pre_enabled_for_dist(&d, true, &mut rng)); + } + + #[test] + fn pre_enabled_non_degenerate_without_sampling_is_never_pre_enabled() { + let d = dist(DistFunction::Bernoulli, &[0.5]); + let mut rng = StdRng::seed_from_u64(0); + assert!(!is_pre_enabled_for_dist(&d, false, &mut rng)); + } + + #[test] + fn pre_enabled_non_degenerate_with_sampling_matches_attempt_bernoulli() { + let d = dist(DistFunction::Bernoulli, &[0.5]); + let mut rng_a = StdRng::seed_from_u64(7); + let mut rng_b = StdRng::seed_from_u64(7); + assert_eq!( + is_pre_enabled_for_dist(&d, true, &mut rng_a), + d.attempt_bernoulli(&mut rng_b) + ); + } + + // --- is_impossible_for_dist --- + + #[test] + fn impossible_is_negated_attempt_bernoulli() { + let d = dist(DistFunction::Bernoulli, &[0.5]); + let mut rng_a = StdRng::seed_from_u64(3); + let mut rng_b = StdRng::seed_from_u64(3); + assert_eq!( + is_impossible_for_dist(&d, &mut rng_a), + !d.attempt_bernoulli(&mut rng_b) + ); + } + + #[test] + fn impossible_without_bernoulli_is_never_impossible() { + // attempt_bernoulli unconditionally succeeds when there's no + // Bernoulli anywhere in the dist (see ttc.rs) - so a plain + // Exponential can never be "impossible" regardless of rng draws. + let d = dist(DistFunction::Exponential, &[0.1]); + let mut rng = StdRng::seed_from_u64(0); + for _ in 0..10 { + assert!(!is_impossible_for_dist(&d, &mut rng)); + } + } +} diff --git a/core/malsim-core/src/graph_utils.rs b/core/malsim-core/src/graph_utils.rs new file mode 100644 index 00000000..209ec52f --- /dev/null +++ b/core/malsim-core/src/graph_utils.rs @@ -0,0 +1,581 @@ +//! Rust port of `python/malsim/mal_simulator/graph_utils.py`'s traversal +//! predicates - see `PORTING_NOTES.md` §5 Phase A4. +//! +//! `node_is_actionable`/`node_reward` are deliberately **not** ported here: +//! per §2.4/§4 they operate on `NodePropertyRule` directly (not the +//! flattened hot path) and stay in Python. +//! +//! Like `necessity.rs` (and unlike most of `graph_state.rs`), +//! `node_is_blocked`/`node_is_traversable` aren't split into a +//! graph-independent helper + thin wrapper: the interesting logic here +//! *is* graph traversal (resolving `node.parents` to real nodes), so +//! there's nothing graph-independent to factor out. `node_blocks_children` +//! is the exception - its own body only reads the node's `step_type`/ +//! `existence_status`/id, so it's decomposed the way `graph_state.rs` +//! does, same reasoning (testable without a real `AttackGraphNode`). +//! `is_attack_step` is decomposed the same way, trivially. +//! +//! Matches on the `maltoolbox_language::graph::attack_step::AttackStepType` +//! enum (`node.step_type`) directly, consistent with +//! `graph_state.rs`/`necessity.rs`/`viability.rs`; `maltoolbox-language` +//! is a regular (runtime) dependency of this crate. + +use crate::types::{NodeDenseMap, NodeSet}; +use std::fmt; + +use maltoolbox_attackgraph::{AttackGraph, AttackGraphNode, AttackGraphNodeId}; +use maltoolbox_language::graph::attack_step::AttackStepType; + +/// Sorts `nodes` by the stable `AttackGraphNode.id`, the deterministic +/// order everything that draws from the RNG or produces an ordered result +/// iterates node sets in. (Slotmap keys are not used for this: the keys of +/// nodes created at runtime depend on slot reuse.) Nodes no longer in the +/// graph sort first, by key. +pub fn sort_by_node_id(graph: &AttackGraph, nodes: &mut [AttackGraphNodeId]) { + nodes.sort_unstable_by_key(|&key| node_order_key(graph, key)); +} + +/// The key `sort_by_node_id` orders by, for `min_by_key` and friends. +pub fn node_order_key( + graph: &AttackGraph, + key: AttackGraphNodeId, +) -> (Option, AttackGraphNodeId) { + (graph.nodes.get(key).map(|node| node.id), key) +} + +#[derive(Debug, Clone, PartialEq, Eq)] +pub enum GraphUtilsError { + /// Mirrors Python's `assert isinstance(node.existence_status, bool)` + /// in `_node_blocks_children`. + MissingExistenceStatus(AttackGraphNodeId), + /// Mirrors Python's bare `necessity_per_node[parent]` `KeyError` in + /// `and_traversable` - the map must already hold every node's + /// necessity (computed once by `necessity::calculate_necessity`). + MissingNecessity(AttackGraphNodeId), + /// Mirrors Python's `TypeError` in `is_and_or_traversable` for a + /// `node.type` outside `or`/`and`. Unreachable through + /// `node_is_traversable` itself (callers only reach this match after + /// `is_attack_step` has already filtered to `or`/`and`), kept for the + /// defensive parity with the Python `TypeError`. + UnknownStepType(String, AttackGraphNodeId), +} + +impl fmt::Display for GraphUtilsError { + fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { + match self { + GraphUtilsError::MissingExistenceStatus(id) => { + write!(f, "existence status not defined for node {id:?}") + } + GraphUtilsError::MissingNecessity(id) => { + write!(f, "necessity not yet computed for parent node {id:?}") + } + GraphUtilsError::UnknownStepType(step_type, id) => { + write!(f, "node {id:?} has an unknown type \"{step_type}\"") + } + } + } +} + +impl std::error::Error for GraphUtilsError {} + +/// Graph-independent half of `is_attack_step`. +pub fn is_attack_step_type(step_type: AttackStepType) -> bool { + !matches!( + step_type, + AttackStepType::Defense | AttackStepType::Exist | AttackStepType::NotExist + ) +} + +/// Port of `is_attack_step`. Only attack steps (`or`/`and`) have +/// traversability. +pub fn is_attack_step(node: &AttackGraphNode) -> bool { + !matches!( + node.step_type, + AttackStepType::Defense | AttackStepType::Exist | AttackStepType::NotExist + ) +} + +/// Port of `node_is_live`. +pub fn node_is_live(graph: &AttackGraph, node_id: AttackGraphNodeId) -> bool { + graph.nodes.contains_key(node_id) +} + +/// Port of `node_is_necessary` - a thin, error-checked wrapper over a +/// `necessity_per_node` lookup (`GraphState::necessity_per_node` from A3). +pub fn node_is_necessary( + necessity_per_node: &NodeDenseMap, + node_id: AttackGraphNodeId, +) -> Result { + necessity_per_node + .get(node_id) + .copied() + .ok_or(GraphUtilsError::MissingNecessity(node_id)) +} + +/// Graph-independent half of `_node_blocks_children`. +pub fn node_blocks_children_from_parts( + step_type: AttackStepType, + existence_status: Option, + node_id: AttackGraphNodeId, + enabled_defenses: &NodeSet, +) -> Result { + match step_type { + AttackStepType::Exist => existence_status + .map(|exists| !exists) + .ok_or(GraphUtilsError::MissingExistenceStatus(node_id)), + AttackStepType::NotExist => { + existence_status.ok_or(GraphUtilsError::MissingExistenceStatus(node_id)) + } + AttackStepType::Defense => Ok(enabled_defenses.contains(&node_id)), + AttackStepType::Or | AttackStepType::And => Ok(false), + } +} + +/// Thin `AttackGraphNode`-reading wrapper around +/// `node_blocks_children_from_parts`. +fn node_blocks_children( + node_id: AttackGraphNodeId, + node: &AttackGraphNode, + enabled_defenses: &NodeSet, +) -> Result { + node_blocks_children_from_parts( + node.step_type, + node.existence_status, + node_id, + enabled_defenses, + ) +} + +/// Port of `node_is_blocked`. +/// +/// Note the `and`/`or` branches are not a copy-paste of each other with +/// the connective swapped by mistake: an `and` node is blocked if *any* +/// parent blocks it (one missing path is enough to cut off a step that +/// needs them all), an `or` node is blocked only if *all* parents block it +/// (any single open path keeps it reachable) - this matches Python's +/// `node_is_blocked` (`any(...)` for `and`, `all(...)` for `or`) exactly. +pub fn node_is_blocked( + graph: &AttackGraph, + node_id: AttackGraphNodeId, + impossible_attack_steps: &NodeSet, + enabled_defenses: &NodeSet, +) -> Result { + let node = &graph.nodes[node_id]; + match node.step_type { + AttackStepType::And => { + if impossible_attack_steps.contains(&node_id) { + return Ok(true); + } + for &parent_id in &node.parents { + let parent = &graph.nodes[parent_id]; + if node_blocks_children(parent_id, parent, enabled_defenses)? { + return Ok(true); + } + } + Ok(false) + } + AttackStepType::Or => { + if impossible_attack_steps.contains(&node_id) { + return Ok(true); + } + for &parent_id in &node.parents { + let parent = &graph.nodes[parent_id]; + if !node_blocks_children(parent_id, parent, enabled_defenses)? { + return Ok(false); + } + } + Ok(true) + } + _ => Ok(false), + } +} + +/// Port of `and_traversable`: all of `node`'s *necessary* parents must be +/// in `performed_nodes` (unnecessary parents are skipped, not required). +fn and_traversable( + node: &AttackGraphNode, + performed_nodes: &NodeSet, + necessity_per_node: &NodeDenseMap, +) -> Result { + for &parent_id in &node.parents { + if node_is_necessary(necessity_per_node, parent_id)? + && !performed_nodes.contains(&parent_id) + { + return Ok(false); + } + } + Ok(true) +} + +/// Port of `node_is_traversable`. +/// +/// Arguments mirror the Python function's `sim_state`/`performed_nodes`/ +/// `node`, decomposed into the specific `GraphState`/`MalSimulatorState` +/// fields this logic actually reads (full settings/state porting is later +/// A-phase work). +pub fn node_is_traversable( + graph: &AttackGraph, + node_id: AttackGraphNodeId, + performed_nodes: &NodeSet, + impossible_attack_steps: &NodeSet, + enabled_defenses: &NodeSet, + necessity_per_node: &NodeDenseMap, +) -> Result { + let node = &graph.nodes[node_id]; + + if !is_attack_step(node) { + return Ok(false); + } + if node_is_blocked(graph, node_id, impossible_attack_steps, enabled_defenses)? { + return Ok(false); + } + // If no parent is reached, the node can not be traversable. + let parents_reached = node.parents.iter().any(|p| performed_nodes.contains(p)); + if !parents_reached { + return Ok(false); + } + + match node.step_type { + AttackStepType::Or => Ok(true), + AttackStepType::And => and_traversable(node, performed_nodes, necessity_per_node), + other => Err(GraphUtilsError::UnknownStepType( + other.as_str().to_string(), + node_id, + )), + } +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::test_fixtures::{add_dummy_node, dummy_graph}; + + // --- is_attack_step_type / is_attack_step --- + + #[test] + fn is_attack_step_type_true_for_or_and_and() { + assert!(is_attack_step_type(AttackStepType::Or)); + assert!(is_attack_step_type(AttackStepType::And)); + } + + #[test] + fn is_attack_step_type_false_for_defense_exist_not_exist() { + assert!(!is_attack_step_type(AttackStepType::Defense)); + assert!(!is_attack_step_type(AttackStepType::Exist)); + assert!(!is_attack_step_type(AttackStepType::NotExist)); + } + + #[test] + fn is_attack_step_reads_node_step_type() { + let mut graph = dummy_graph(); + let or_id = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let defense_id = add_dummy_node(&mut graph, "DummyDefenseAttackStep"); + assert!(is_attack_step(&graph.nodes[or_id])); + assert!(!is_attack_step(&graph.nodes[defense_id])); + } + + // --- node_blocks_children_from_parts --- + + #[test] + fn node_blocks_children_exist_blocks_when_not_existing() { + let defenses = NodeSet::default(); + let id = AttackGraphNodeId::default(); + assert!( + node_blocks_children_from_parts(AttackStepType::Exist, Some(false), id, &defenses) + .unwrap() + ); + assert!( + !node_blocks_children_from_parts(AttackStepType::Exist, Some(true), id, &defenses) + .unwrap() + ); + } + + #[test] + fn node_blocks_children_not_exist_blocks_when_existing() { + let defenses = NodeSet::default(); + let id = AttackGraphNodeId::default(); + assert!(node_blocks_children_from_parts( + AttackStepType::NotExist, + Some(true), + id, + &defenses + ) + .unwrap()); + assert!(!node_blocks_children_from_parts( + AttackStepType::NotExist, + Some(false), + id, + &defenses + ) + .unwrap()); + } + + #[test] + fn node_blocks_children_missing_existence_status_errors() { + let defenses = NodeSet::default(); + let id = AttackGraphNodeId::default(); + assert!(matches!( + node_blocks_children_from_parts(AttackStepType::Exist, None, id, &defenses), + Err(GraphUtilsError::MissingExistenceStatus(_)) + )); + assert!(matches!( + node_blocks_children_from_parts(AttackStepType::NotExist, None, id, &defenses), + Err(GraphUtilsError::MissingExistenceStatus(_)) + )); + } + + #[test] + fn node_blocks_children_defense_blocks_when_enabled() { + let mut graph = dummy_graph(); + let defense_id = add_dummy_node(&mut graph, "DummyDefenseAttackStep"); + let mut defenses = NodeSet::default(); + assert!(!node_blocks_children_from_parts( + AttackStepType::Defense, + None, + defense_id, + &defenses + ) + .unwrap()); + defenses.insert(defense_id); + assert!(node_blocks_children_from_parts( + AttackStepType::Defense, + None, + defense_id, + &defenses + ) + .unwrap()); + } + + #[test] + fn node_blocks_children_or_and_never_block() { + let defenses = NodeSet::default(); + let id = AttackGraphNodeId::default(); + assert!(!node_blocks_children_from_parts(AttackStepType::Or, None, id, &defenses).unwrap()); + assert!( + !node_blocks_children_from_parts(AttackStepType::And, None, id, &defenses).unwrap() + ); + } + + // --- node_is_live --- + + #[test] + fn node_is_live_true_for_existing_node_false_after_removal() { + let mut graph = dummy_graph(); + let node_id = add_dummy_node(&mut graph, "DummyOrAttackStep"); + assert!(node_is_live(&graph, node_id)); + graph.remove_node(node_id).unwrap(); + assert!(!node_is_live(&graph, node_id)); + } + + // --- node_is_blocked (ported from `tests/test_graph_processing.py::test_node_is_blocked`) --- + + #[test] + fn node_is_blocked_matches_python_test_node_is_blocked() { + let mut graph = dummy_graph(); + + let exist_node = add_dummy_node(&mut graph, "DummyExistAttackStep"); + graph.nodes[exist_node].existence_status = Some(false); + + let not_exist_node = add_dummy_node(&mut graph, "DummyNotExistAttackStep"); + graph.nodes[not_exist_node].existence_status = Some(true); + + let defense_node = add_dummy_node(&mut graph, "DummyDefenseAttackStep"); + + let and_blocked_by_defense = add_dummy_node(&mut graph, "DummyAndAttackStep"); + graph.nodes[and_blocked_by_defense] + .parents + .insert(defense_node); + + let and_blocked_by_exist = add_dummy_node(&mut graph, "DummyAndAttackStep"); + graph.nodes[and_blocked_by_exist].parents.insert(exist_node); + + let and_blocked_by_not_exist = add_dummy_node(&mut graph, "DummyAndAttackStep"); + graph.nodes[and_blocked_by_not_exist] + .parents + .insert(not_exist_node); + + let or_blocked_by_defense = add_dummy_node(&mut graph, "DummyOrAttackStep"); + graph.nodes[or_blocked_by_defense] + .parents + .insert(defense_node); + + let or_blocked_by_exist = add_dummy_node(&mut graph, "DummyOrAttackStep"); + graph.nodes[or_blocked_by_exist].parents.insert(exist_node); + + let or_blocked_by_not_exist = add_dummy_node(&mut graph, "DummyOrAttackStep"); + graph.nodes[or_blocked_by_not_exist] + .parents + .insert(not_exist_node); + + // Or node with any parent that is not blocking will not be blocked. + let or_not_blocked = add_dummy_node(&mut graph, "DummyOrAttackStep"); + graph.nodes[or_not_blocked] + .parents + .insert(and_blocked_by_defense); + + let impossible_and_node = add_dummy_node(&mut graph, "DummyAndAttackStep"); + + let enabled_defenses: NodeSet = [defense_node].into_iter().collect(); + let impossible_attack_steps: NodeSet = [impossible_and_node].into_iter().collect(); + + let blocked = + |id| node_is_blocked(&graph, id, &impossible_attack_steps, &enabled_defenses).unwrap(); + + assert!(blocked(and_blocked_by_defense)); + assert!(blocked(and_blocked_by_exist)); + assert!(blocked(and_blocked_by_not_exist)); + assert!(blocked(or_blocked_by_defense)); + assert!(blocked(or_blocked_by_exist)); + assert!(blocked(or_blocked_by_not_exist)); + assert!(!blocked(or_not_blocked)); + assert!(blocked(impossible_and_node)); + } + + // --- node_is_traversable --- + + #[test] + fn node_is_traversable_false_for_non_attack_step() { + let mut graph = dummy_graph(); + let defense_id = add_dummy_node(&mut graph, "DummyDefenseAttackStep"); + let empty = NodeSet::default(); + let empty_map = NodeDenseMap::default(); + assert!( + !node_is_traversable(&graph, defense_id, &empty, &empty, &empty, &empty_map).unwrap() + ); + } + + #[test] + fn node_is_traversable_false_when_no_parent_performed() { + let mut graph = dummy_graph(); + let parent = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let or_node = add_dummy_node(&mut graph, "DummyOrAttackStep"); + graph.nodes[or_node].parents.insert(parent); + + let empty = NodeSet::default(); + let empty_map = NodeDenseMap::default(); + assert!(!node_is_traversable(&graph, or_node, &empty, &empty, &empty, &empty_map).unwrap()); + } + + #[test] + fn node_is_traversable_or_true_once_any_parent_performed() { + let mut graph = dummy_graph(); + let parent = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let or_node = add_dummy_node(&mut graph, "DummyOrAttackStep"); + graph.nodes[or_node].parents.insert(parent); + + let performed: NodeSet = [parent].into_iter().collect(); + let empty = NodeSet::default(); + let empty_map = NodeDenseMap::default(); + assert!( + node_is_traversable(&graph, or_node, &performed, &empty, &empty, &empty_map).unwrap() + ); + } + + #[test] + fn node_is_traversable_and_requires_all_necessary_parents_performed() { + let mut graph = dummy_graph(); + let necessary_parent = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let unnecessary_parent = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let and_node = add_dummy_node(&mut graph, "DummyAndAttackStep"); + graph.nodes[and_node].parents.insert(necessary_parent); + graph.nodes[and_node].parents.insert(unnecessary_parent); + + let necessity: NodeDenseMap<_> = [(necessary_parent, true), (unnecessary_parent, false)] + .into_iter() + .collect(); + let empty = NodeSet::default(); + + // Only the unnecessary parent performed: parents_reached is true, + // but the necessary parent is still missing -> not traversable. + let only_unnecessary: NodeSet = [unnecessary_parent].into_iter().collect(); + assert!(!node_is_traversable( + &graph, + and_node, + &only_unnecessary, + &empty, + &empty, + &necessity + ) + .unwrap()); + + // Necessary parent performed (unnecessary one isn't) -> traversable. + let only_necessary: NodeSet = [necessary_parent].into_iter().collect(); + assert!(node_is_traversable( + &graph, + and_node, + &only_necessary, + &empty, + &empty, + &necessity + ) + .unwrap()); + } + + #[test] + fn node_is_traversable_or_true_when_only_some_parents_block() { + let mut graph = dummy_graph(); + let defense_node = add_dummy_node(&mut graph, "DummyDefenseAttackStep"); + let parent = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let or_node = add_dummy_node(&mut graph, "DummyOrAttackStep"); + graph.nodes[or_node].parents.insert(parent); + graph.nodes[or_node].parents.insert(defense_node); + + let performed: NodeSet = [parent].into_iter().collect(); + let enabled_defenses: NodeSet = [defense_node].into_iter().collect(); + let empty = NodeSet::default(); + let empty_map = NodeDenseMap::default(); + + // `or` node blocked only once *all* parents block - here only the + // defense parent blocks, the other parent doesn't, so it's not + // blocked (and is traversable, since that parent is performed). + assert!(node_is_traversable( + &graph, + or_node, + &performed, + &empty, + &enabled_defenses, + &empty_map + ) + .unwrap()); + } + + #[test] + fn node_is_traversable_and_false_when_blocked_by_enabled_defense() { + let mut graph = dummy_graph(); + let defense_node = add_dummy_node(&mut graph, "DummyDefenseAttackStep"); + let parent = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let and_node = add_dummy_node(&mut graph, "DummyAndAttackStep"); + graph.nodes[and_node].parents.insert(parent); + graph.nodes[and_node].parents.insert(defense_node); + + let performed: NodeSet = [parent].into_iter().collect(); + let enabled_defenses: NodeSet = [defense_node].into_iter().collect(); + // The defense parent is marked unnecessary in both calls below, so + // `and_traversable` alone would pass (only `parent` is required and + // it's performed) - the only difference between the two calls is + // whether the defense is enabled, isolating the `node_is_blocked` + // check. + let necessity: NodeDenseMap<_> = [(parent, true), (defense_node, false)] + .into_iter() + .collect(); + let empty = NodeSet::default(); + + // Defense not enabled -> traversable. + assert!( + node_is_traversable(&graph, and_node, &performed, &empty, &empty, &necessity).unwrap() + ); + + // `and` node blocked as soon as *any* parent blocks - the enabled + // defense parent alone is enough, even with the other parent + // performed. + assert!(!node_is_traversable( + &graph, + and_node, + &performed, + &empty, + &enabled_defenses, + &necessity + ) + .unwrap()); + } +} diff --git a/core/malsim-core/src/history.rs b/core/malsim-core/src/history.rs new file mode 100644 index 00000000..40c6ffa0 --- /dev/null +++ b/core/malsim-core/src/history.rs @@ -0,0 +1,437 @@ +//! Per-episode step history kept inside [`SimState`], so the state at any +//! earlier iteration of the current episode can be rebuilt on demand +//! ([`SimState::snapshot_at`]), plus the per-step `recording` and the +//! dyna path's accumulated modification record. +//! +//! This is what lets the Python `MalSimulator`/`DynaMalSimulator` hold no +//! state of their own: `agent_states`, `sim_state` and `recording` are +//! translated from the native simulator on access, and an +//! `AttackerState`/`DefenderState`'s `previous_state` is rebuilt from this +//! history when it is read (`PORTING_NOTES.md` §14). +//! +//! The history stores, per step, only what that step *added* (and the +//! action-surface diff), so rebuilding iteration `k` is "clone the current +//! state, undo the steps after `k`, newest first". Rebuilding the previous +//! iteration, the common case, therefore costs one undo. + +use std::collections::BTreeMap; + +use maltoolbox_attackgraph::AttackGraphNodeId; + +use crate::dyna_graph_state::GraphStateDelta; +use crate::event_logger::LogEntry; +use crate::graph_state::GraphState; +use crate::model_effects::ModEffectOp; +use crate::simulator::{AttackerRuntime, DefenderRuntime, SimState}; +use crate::types::{NodeMap, NodeSet}; + +/// What one step changed for one attacker. +#[derive(Debug, Clone, Default)] +pub(crate) struct AttackerStepRecord { + /// The nodes the step reported as compromised, as reported (the + /// `step_performed_nodes` delta) - what `performed_nodes_order` keys. + pub(crate) step_performed_nodes: NodeSet, + /// `performed_history.len()` before the step; the nodes after it are + /// the ones this step newly added to `performed_nodes`. + pub(crate) performed_history_len: usize, + /// Nodes this step newly added to `attempted_nodes`. + pub(crate) new_attempted_nodes: NodeSet, + /// Every node attempted this step (each bumped `num_attempts` by one). + pub(crate) step_attempted_nodes: NodeSet, + pub(crate) surface_added: NodeSet, + pub(crate) surface_removed: NodeSet, +} + +/// What one step changed for one defender. +#[derive(Debug, Clone, Default)] +pub(crate) struct DefenderStepRecord { + /// The defenses the step reported as enabled, as reported (the + /// `step_performed_nodes` delta) - what `performed_nodes_order` keys. + pub(crate) step_performed_nodes: NodeSet, + pub(crate) new_performed_nodes: NodeSet, + pub(crate) new_compromised_nodes: NodeSet, + pub(crate) new_observed_nodes: NodeSet, + /// `logs.len()` before the step. + pub(crate) logs_len: usize, + pub(crate) surface_added: NodeSet, + pub(crate) surface_removed: NodeSet, +} + +/// What one step changed, for undoing it. +#[derive(Debug, Clone, Default)] +pub(crate) struct StepRecord { + pub(crate) attackers: BTreeMap, + pub(crate) defenders: BTreeMap, + /// Defenses this step newly added to `enabled_defenses`. + pub(crate) new_enabled_defenses: NodeSet, + /// What the step's model-effect folds changed in `graph_state` (empty + /// for steps without model effects). + pub(crate) graph_state_delta: GraphStateDelta, + /// `modification_record.len()` before the step. + pub(crate) modification_record_len: usize, + /// This step's `recording` entry: per agent, the nodes it newly + /// performed, requested actions first (in request order), then the + /// rest (effects) in the order they were performed. + pub(crate) recording: BTreeMap>, +} + +/// The step history of the current episode - see module docs. +#[derive(Debug, Clone, Default)] +pub struct EpisodeHistory { + /// Per agent, the nodes it had performed right after reset (what + /// `performed_nodes_order[0]` holds). + reset_performed: BTreeMap, + steps: Vec, + modification_record: Vec, +} + +impl EpisodeHistory { + pub(crate) fn from_reset( + attackers: &BTreeMap, + defenders: &BTreeMap, + ) -> EpisodeHistory { + let reset_performed = attackers + .iter() + .map(|(name, a)| (name.clone(), a.performed_nodes.clone())) + .chain( + defenders + .iter() + .map(|(name, d)| (name.clone(), d.performed_nodes.clone())), + ) + .collect(); + EpisodeHistory { + reset_performed, + steps: Vec::new(), + modification_record: Vec::new(), + } + } + + /// The current iteration: the number of steps since reset. + pub fn iteration(&self) -> u64 { + self.steps.len() as u64 + } + + /// Every model modification of this episode, in the order applied. + /// Always empty for the static `Simulator`. + pub fn modification_record(&self) -> &[ModEffectOp] { + &self.modification_record + } + + /// The recording of step `iteration` (1-based: the step that took the + /// simulator from `iteration - 1` to `iteration`): per agent, the nodes + /// it newly performed, requested actions first. `None` for an + /// iteration that has not happened. + pub fn recording(&self, iteration: u64) -> Option<&BTreeMap>> { + let index = usize::try_from(iteration).ok()?.checked_sub(1)?; + self.steps.get(index).map(|s| &s.recording) + } + + pub(crate) fn push_step(&mut self, record: StepRecord) { + self.steps.push(record); + } + + pub(crate) fn extend_modification_record(&mut self, ops: Vec) { + self.modification_record.extend(ops); + } + + /// `performed_nodes_order` of agent `name` at `iteration`: iteration -> + /// nodes performed then, for every iteration up to `iteration` in which + /// it performed something (0 is reset). + fn performed_nodes_order(&self, name: &str, iteration: u64) -> BTreeMap { + let mut order = BTreeMap::new(); + if let Some(at_reset) = self.reset_performed.get(name) { + if !at_reset.is_empty() { + order.insert(0, at_reset.clone()); + } + } + for (index, step) in self.steps.iter().take(iteration as usize).enumerate() { + let performed = step + .attackers + .get(name) + .map(|a| &a.step_performed_nodes) + .or_else(|| step.defenders.get(name).map(|d| &d.step_performed_nodes)); + if let Some(performed) = performed { + if !performed.is_empty() { + order.insert(index as u64 + 1, performed.clone()); + } + } + } + order + } +} + +/// The state of one attacker at some iteration - the fields of +/// `AttackerRuntime` an `AttackerState` exposes, plus its +/// `performed_nodes_order`. +#[derive(Debug, Clone, PartialEq)] +pub struct AttackerSnapshot { + pub entry_points: NodeSet, + pub goals: NodeSet, + pub performed_nodes: NodeSet, + pub attempted_nodes: NodeSet, + pub action_surface: NodeSet, + pub num_attempts: NodeMap, + pub ttc_values: NodeMap, + pub impossible_steps: NodeSet, + pub performed_nodes_order: BTreeMap, + pub iteration: u64, +} + +/// The state of one defender at some iteration - see `AttackerSnapshot`. +#[derive(Debug, Clone, PartialEq)] +pub struct DefenderSnapshot { + pub performed_nodes: NodeSet, + pub compromised_nodes: NodeSet, + pub observed_nodes: NodeSet, + pub action_surface: NodeSet, + pub logs: Vec, + pub performed_nodes_order: BTreeMap, + pub iteration: u64, +} + +/// The whole simulator state at some iteration of the current episode - +/// what [`SimState::snapshot_at`] rebuilds. +#[derive(Debug, Clone)] +pub struct StateSnapshot { + pub iteration: u64, + pub graph_state: GraphState, + pub enabled_defenses: NodeSet, + /// The modification record up to this iteration (dyna only). + pub modification_record: Vec, + pub attackers: BTreeMap, + pub defenders: BTreeMap, +} + +fn undo_surface(surface: &mut NodeSet, added: &NodeSet, removed: &NodeSet) { + for id in added { + surface.remove(id); + } + surface.extend(removed.iter().copied()); +} + +/// `(added, removed)` going from `before` to `after`. +pub(crate) fn surface_diff(before: &NodeSet, after: &NodeSet) -> (NodeSet, NodeSet) { + ( + after.difference(before).copied().collect(), + before.difference(after).copied().collect(), + ) +} + +/// The recording order of `new_nodes`: the `requested` ones first, in +/// request order, then the rest in `rest_order`'s order. +pub(crate) fn recording_order( + requested: &[AttackGraphNodeId], + new_nodes: &NodeSet, + rest_order: impl IntoIterator, +) -> Vec { + let mut out: Vec = requested + .iter() + .copied() + .filter(|id| new_nodes.contains(id)) + .collect(); + out.extend( + rest_order + .into_iter() + .filter(|id| new_nodes.contains(id) && !requested.contains(id)), + ); + out +} + +/// A copy of an episode's state and history, without the RNG: enough to +/// rebuild any iteration of it (`snapshot_at`) after the simulator has +/// moved on to a new episode. +#[derive(Debug, Clone)] +pub struct EpisodeRecord { + graph_state: GraphState, + enabled_defenses: NodeSet, + attackers: BTreeMap, + defenders: BTreeMap, + history: EpisodeHistory, +} + +impl EpisodeRecord { + /// The step history of the episode. + pub fn history(&self) -> &EpisodeHistory { + &self.history + } + + /// Same as [`SimState::snapshot_at`]. + pub fn snapshot_at(&self, iteration: u64) -> Option { + snapshot_at( + &self.graph_state, + &self.enabled_defenses, + &self.attackers, + &self.defenders, + &self.history, + iteration, + ) + } +} + +impl SimState { + /// The step history of the current episode. + pub fn history(&self) -> &EpisodeHistory { + &self.history + } + + /// The state at `iteration` (0 is right after reset) of the current + /// episode, rebuilt from the current state and the step history. + /// `None` for an iteration after the current one. + pub fn snapshot_at(&self, iteration: u64) -> Option { + snapshot_at( + &self.graph_state, + &self.enabled_defenses, + &self.attackers, + &self.defenders, + &self.history, + iteration, + ) + } + + /// A copy of this episode's state and history, without the RNG. + pub fn episode_record(&self) -> EpisodeRecord { + EpisodeRecord { + graph_state: self.graph_state.clone(), + enabled_defenses: self.enabled_defenses.clone(), + attackers: self.attackers.clone(), + defenders: self.defenders.clone(), + history: self.history.clone(), + } + } +} + +fn snapshot_at( + graph_state: &GraphState, + enabled_defenses: &NodeSet, + attackers: &BTreeMap, + defenders: &BTreeMap, + history: &EpisodeHistory, + iteration: u64, +) -> Option { + if iteration > history.iteration() { + return None; + } + let mut attackers = attackers.clone(); + let mut defenders = defenders.clone(); + let mut graph_state = graph_state.clone(); + let mut enabled_defenses = enabled_defenses.clone(); + let mut modification_record_len = history.modification_record.len(); + + for step in history.steps[iteration as usize..].iter().rev() { + for (name, record) in &step.attackers { + let Some(a) = attackers.get_mut(name) else { + continue; + }; + for id in &a.performed_history[record.performed_history_len..] { + a.performed_nodes.remove(id); + } + a.performed_history.truncate(record.performed_history_len); + for id in &record.new_attempted_nodes { + a.attempted_nodes.remove(id); + } + for id in &record.step_attempted_nodes { + if let Some(count) = a.num_attempts.get_mut(id) { + *count -= 1; + if *count == 0 { + a.num_attempts.remove(id); + } + } + } + undo_surface( + &mut a.action_surface, + &record.surface_added, + &record.surface_removed, + ); + a.iteration -= 1; + } + for (name, record) in &step.defenders { + let Some(d) = defenders.get_mut(name) else { + continue; + }; + for id in &record.new_performed_nodes { + d.performed_nodes.remove(id); + } + for id in &record.new_compromised_nodes { + d.compromised_nodes.remove(id); + } + for id in &record.new_observed_nodes { + d.observed_nodes.remove(id); + } + d.logs.truncate(record.logs_len); + undo_surface( + &mut d.action_surface, + &record.surface_added, + &record.surface_removed, + ); + d.iteration -= 1; + } + let delta = &step.graph_state_delta; + for id in &delta.new_nodes { + graph_state.ttc_values.remove(id); + graph_state.pre_enabled_defenses.remove(id); + graph_state.impossible_attack_steps.remove(id); + enabled_defenses.remove(id); + } + // Remove before inserting: a SecondaryMap ignores an insert of an older + // key while a newer key holds the slot. + for (&id, prior) in &delta.necessity_prior { + if prior.is_none() { + graph_state.necessity_per_node.remove(id); + } + } + for (&id, prior) in &delta.necessity_prior { + if let Some(v) = *prior { + graph_state.necessity_per_node.insert(id, v); + } + } + for id in &step.new_enabled_defenses { + enabled_defenses.remove(id); + } + modification_record_len = step.modification_record_len; + } + + let attackers = attackers + .into_iter() + .map(|(name, a)| { + let performed_nodes_order = history.performed_nodes_order(&name, iteration); + let snapshot = AttackerSnapshot { + entry_points: a.entry_points, + goals: a.goals, + performed_nodes: a.performed_nodes, + attempted_nodes: a.attempted_nodes, + action_surface: a.action_surface, + num_attempts: a.num_attempts, + ttc_values: a.ttc_values, + impossible_steps: a.impossible_steps, + performed_nodes_order, + iteration: a.iteration, + }; + (name, snapshot) + }) + .collect(); + let defenders = defenders + .into_iter() + .map(|(name, d)| { + let performed_nodes_order = history.performed_nodes_order(&name, iteration); + let snapshot = DefenderSnapshot { + performed_nodes: d.performed_nodes, + compromised_nodes: d.compromised_nodes, + observed_nodes: d.observed_nodes, + action_surface: d.action_surface, + logs: d.logs, + performed_nodes_order, + iteration: d.iteration, + }; + (name, snapshot) + }) + .collect(); + + Some(StateSnapshot { + iteration, + graph_state, + enabled_defenses, + modification_record: history.modification_record[..modification_record_len].to_vec(), + attackers, + defenders, + }) +} diff --git a/core/malsim-core/src/lib.rs b/core/malsim-core/src/lib.rs new file mode 100644 index 00000000..e0e8deb8 --- /dev/null +++ b/core/malsim-core/src/lib.rs @@ -0,0 +1,35 @@ +//! Rust core of `malsim`, the MAL simulator. Depends on `maltoolbox-attackgraph` +//! (from the `mal-toolbox` `rust-rewrite` branch) for the attack graph types +//! this simulator steps through. + +pub mod agent_state; +pub mod assoc_traversal; +pub mod attack_surface; +pub mod attacker_step; +pub mod defender_step; +pub mod defense_surface; +pub mod dyna_attacker_step; +pub mod dyna_defender_step; +pub mod dyna_graph_state; +pub mod dyna_simulator; +pub mod event_logger; +pub mod false_alerts; +pub mod graph_state; +pub mod graph_utils; +pub mod history; +pub mod model_effects; +pub mod model_state; +pub mod necessity; +pub mod observability; +pub mod policies; +pub mod rewards; +pub mod run_simulation; +pub mod scenario; +pub mod settings; +pub mod simulator; +pub mod ttc; +pub mod types; +pub mod viability; + +#[cfg(test)] +mod test_fixtures; diff --git a/core/malsim-core/src/model_effects.rs b/core/malsim-core/src/model_effects.rs new file mode 100644 index 00000000..5ce1c65c --- /dev/null +++ b/core/malsim-core/src/model_effects.rs @@ -0,0 +1,810 @@ +//! Rust port of `python/malsim/dyna_mal_simulator/model_effects.py` - see +//! `PORTING_NOTES.md` §6 Phase B1. +//! +//! Applies a [`LanguageGraphModelEffect`] (mal-toolbox's already-parsed +//! model-effect declaration) to a live [`Model`], mutating it via +//! `Model::add_asset`/`remove_asset`/`add_associated_assets`/ +//! `remove_associated_assets` and the attack graph via +//! `AttackGraph::partially_regenerate_graph` - no new graph-mutation +//! primitives invented here, per §6's framing: those already exist +//! upstream in mal-toolbox. +//! +//! **Deliberate split from Python's `execute_model_effects`:** this +//! module's [`execute_model_effects`] stops at `partially_regenerate_graph` +//! and returns the new node ids it created, rather than also folding them +//! into a `GraphState` (Python's version calls dyna `graph_state.py`'s +//! `add_new_nodes_to_graph_state` inline). That composition is Phase B2's +//! job (`PORTING_NOTES.md` §6's own module split - `model_effects.py` vs. +//! dyna `graph_state.py`); B1 shouldn't reach into B2-scoped code, so the +//! caller (B2's `dyna_attacker_step`/`dyna_defender_step`) does that fold +//! itself using this function's returned node ids. + +use crate::types::NodeSet; +use std::collections::{HashMap, HashSet}; +use std::fmt; + +use maltoolbox_attackgraph::{AttackGraph, AttackGraphNode, AttackGraphNodeId}; +use maltoolbox_language::graph::attack_step::AttackStepType; +use maltoolbox_language::graph::model_effect::{ + DynTarget, LanguageGraphModelEffect, ModelEffectType, +}; +use maltoolbox_model::{AssetSnapshot, Model, ModelError}; +use rand::Rng; + +use crate::assoc_traversal::{ + apply_quantity_filter, parse_addition, parse_removal, sample_size, sorted_vec, + traverse_association_chain, AdditionTarget, AssocTraversalError, +}; + +#[derive(Debug)] +pub enum ModelEffectsError { + AssocTraversal(AssocTraversalError), + Model(ModelError), + Graph(maltoolbox_attackgraph::GraphError), + /// Mirrors the bare `assert node.model_asset` guard in + /// `_apply_model_effect`/`execute_model_effects`. + NodeHasNoModelAsset, + /// Mirrors Python's `raise ValueError(f'Target association traversal + /// cannot be empty for step {node.full_name}.')`. + EmptyTargetTraversal(String), + /// Mirrors Python's two (identically-reached) `raise ValueError(...)` + /// branches in `add_asset` for a target that resolved to an + /// *existing* model asset instead of a type to instantiate - both + /// branches raise regardless of which asset it is, so this one + /// variant covers both. + CannotAddExistingAsset(i64), +} + +impl From for ModelEffectsError { + fn from(e: AssocTraversalError) -> Self { + ModelEffectsError::AssocTraversal(e) + } +} + +impl From for ModelEffectsError { + fn from(e: ModelError) -> Self { + ModelEffectsError::Model(e) + } +} + +impl From for ModelEffectsError { + fn from(e: maltoolbox_attackgraph::GraphError) -> Self { + ModelEffectsError::Graph(e) + } +} + +impl fmt::Display for ModelEffectsError { + fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { + match self { + ModelEffectsError::AssocTraversal(e) => write!(f, "{e}"), + ModelEffectsError::Model(e) => write!(f, "{e}"), + ModelEffectsError::Graph(e) => write!(f, "{e}"), + ModelEffectsError::NodeHasNoModelAsset => { + write!(f, "attack graph node does not have a model asset") + } + ModelEffectsError::EmptyTargetTraversal(full_name) => { + write!( + f, + "target association traversal cannot be empty for step {full_name}" + ) + } + ModelEffectsError::CannotAddExistingAsset(asset_id) => write!( + f, + "cannot add an existing asset ({asset_id}) to the model - it already exists" + ), + } + } +} + +impl std::error::Error for ModelEffectsError {} + +/// A self-contained reference to a model asset (id + type + name), +/// captured at the moment a [`ModEffectOp`] is recorded rather than +/// resolved later from the id alone - see `PORTING_NOTES.md` §6 Phase +/// B4/B5: a later op in the *same* modification record can remove the +/// very asset an earlier op referenced (directly, e.g. an asset added +/// then removed within one record, or indirectly, e.g. `remove_asset_op` +/// below records an asset's about-to-be-removed associations before +/// removing the asset itself), so by the time a caller across the FFI +/// boundary reads the full record, `model.assets[id]` may no longer +/// resolve for an id this type already described - carrying the +/// snapshot inline avoids needing a live lookup at all. +#[derive(Debug, Clone, PartialEq, Eq)] +pub struct AssetRef { + pub id: i64, + pub asset_type: String, + pub name: String, +} + +impl AssetRef { + pub(crate) fn from_model(model: &Model, id: i64) -> Self { + let asset = model + .get_asset_by_id(id) + .expect("AssetRef::from_model called with an id missing from the live model"); + AssetRef { + id, + asset_type: asset.asset_type.clone(), + name: asset.name.clone(), + } + } +} + +/// A single modification applied to the model, as a result of executing +/// one model effect. `AssetOp::Removed` additionally carries a full +/// `AssetSnapshot` (not just the `AssetRef` every variant has) to feed +/// `AttackGraph::partially_regenerate_graph` directly - see +/// `execute_model_effects`. +#[derive(Debug, Clone)] +pub enum AssetOp { + Added { + asset: AssetRef, + }, + Removed { + asset: AssetRef, + snapshot: Box, + }, +} + +#[derive(Debug, Clone)] +pub enum AssocOp { + Added { + left: AssetRef, + field_name: String, + right: AssetRef, + }, + Removed { + left: AssetRef, + field_name: String, + right: AssetRef, + }, +} + +#[derive(Debug, Clone)] +pub enum ModEffectOp { + Asset(AssetOp), + Assoc(AssocOp), +} + +impl ModEffectOp { + pub fn effect_type(&self) -> ModelEffectType { + match self { + ModEffectOp::Asset(AssetOp::Added { .. }) + | ModEffectOp::Assoc(AssocOp::Added { .. }) => ModelEffectType::Additive, + ModEffectOp::Asset(AssetOp::Removed { .. }) + | ModEffectOp::Assoc(AssocOp::Removed { .. }) => ModelEffectType::Subtractive, + } + } +} + +/// Port of `target_op`'s dispatch (the `if`/`elif` chain at the bottom of +/// the Python function, not the four closures themselves - see +/// `add_asset_op`/`remove_asset_op`/`add_assoc_op`/`remove_assoc_op` +/// below). `ModelEffectType`/`bool` is exhaustively matched by this 2x2, +/// so Python's trailing `else: raise ValueError(...)` has no Rust +/// equivalent - unreachable by construction. +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +enum TargetKind { + AddAsset, + RemoveAsset, + AddAssoc, + RemoveAssoc, +} + +fn classify(model_effect_type: ModelEffectType, dyn_target: &DynTarget) -> TargetKind { + match (model_effect_type, dyn_target.assoc_op) { + (ModelEffectType::Additive, false) => TargetKind::AddAsset, + (ModelEffectType::Subtractive, false) => TargetKind::RemoveAsset, + (ModelEffectType::Additive, true) => TargetKind::AddAssoc, + (ModelEffectType::Subtractive, true) => TargetKind::RemoveAssoc, + } +} + +/// Port of `target_op`'s `add_asset` closure. +fn add_asset_op( + model: &mut Model, + node_asset_id: i64, + base: &HashSet, + target: &DynTarget, + rng: &mut impl Rng, +) -> Result, ModelEffectsError> { + let (addition_info, quantity) = + parse_addition(model, node_asset_id, base, &target.assoc_traversal, rng)?; + let size = sample_size(quantity, rng, None); + let mut record = Vec::new(); + for (left_asset_id, field_name, right) in sorted_vec(&addition_info) { + for _ in 0..size { + let asset_type = match right { + AdditionTarget::ExistingAsset(existing_id) => { + return Err(ModelEffectsError::CannotAddExistingAsset(existing_id)); + } + AdditionTarget::NewAssetType(asset_type) => asset_type, + }; + let type_name = model.lang_graph.asset(asset_type).name.clone(); + let new_name = format!("{type_name}-{}", model.next_id); + let new_id = model.add_asset(&type_name, Some(new_name), None, None, None, false)?; + + match model.add_associated_assets(left_asset_id, &field_name, HashSet::from([new_id])) { + Ok(()) => { + let new_asset = AssetRef::from_model(model, new_id); + let left = AssetRef::from_model(model, left_asset_id); + record.push(ModEffectOp::Asset(AssetOp::Added { + asset: new_asset.clone(), + })); + record.push(ModEffectOp::Assoc(AssocOp::Added { + left, + field_name: field_name.clone(), + right: new_asset, + })); + } + Err(_) => { + // Mirrors Python's `logger.error(...)` + undo the + // just-created asset + `continue`. + model.remove_asset(new_id)?; + } + } + } + } + Ok(record) +} + +/// Port of `target_op`'s `remove_asset` closure. +fn remove_asset_op( + model: &mut Model, + node_asset_id: i64, + base: &HashSet, + target: &DynTarget, + rng: &mut impl Rng, +) -> Result, ModelEffectsError> { + let (removal_info, quantity) = + parse_removal(model, node_asset_id, base, &target.assoc_traversal, rng)?; + let removal_info = match quantity { + Some(q) => apply_quantity_filter(&removal_info, Some(q), rng), + None => removal_info, + }; + + let mut record = Vec::new(); + let mut removal_assets: HashSet = HashSet::new(); + for (_left, _field_name, right_id) in sorted_vec(&removal_info) { + if let Some(asset) = model.get_asset_by_id(right_id) { + // Captured here, before `remove_asset` below - by the time a + // caller across the FFI boundary reads this record, `right_id` + // itself is gone from the live model (see `AssetRef`'s doc + // comment), so its `AssetRef` must be built while it's still + // live, same as the asset's own removal snapshot further down. + let left_ref = AssetRef::from_model(model, right_id); + // Snapshot both the dict and its sets: `remove_associated_assets` + // mutates the live `associated_assets` sets in place. + // In field-name order: `associated_assets` is a std map. + let mut snapshot: Vec<(String, HashSet)> = asset + .associated_assets + .iter() + .map(|(field_name, assoc_assets)| (field_name.clone(), assoc_assets.clone())) + .collect(); + snapshot.sort_unstable_by(|a, b| a.0.cmp(&b.0)); + for (field_name, assoc_assets) in snapshot { + if model + .remove_associated_assets(right_id, &field_name, &assoc_assets) + .is_ok() + { + for assoc_id in sorted_vec(&assoc_assets) { + let right_ref = AssetRef::from_model(model, assoc_id); + record.push(ModEffectOp::Assoc(AssocOp::Removed { + left: left_ref.clone(), + field_name: field_name.clone(), + right: right_ref, + })); + } + } + // else: mirrors Python's `logger.error(...)` + continue. + } + } + removal_assets.insert(right_id); + } + + for right_id in sorted_vec(&removal_assets) { + let asset_ref = AssetRef::from_model(model, right_id); + let snapshot = model.remove_asset(right_id)?; + record.push(ModEffectOp::Asset(AssetOp::Removed { + asset: asset_ref, + snapshot: Box::new(snapshot), + })); + } + Ok(record) +} + +/// Port of `target_op`'s `add_assoc` closure. Traverses from +/// `{node_asset_id}` (not `base`) - `base` is only used below as the +/// candidate pool when the target resolves to a *type* rather than an +/// existing asset, same as Python. +fn add_assoc_op( + model: &mut Model, + node_asset_id: i64, + base: &HashSet, + target: &DynTarget, + rng: &mut impl Rng, +) -> Result, ModelEffectsError> { + let instigating = HashSet::from([node_asset_id]); + let (addition_info, quantity) = parse_addition( + model, + node_asset_id, + &instigating, + &target.assoc_traversal, + rng, + )?; + let addition_info = match quantity { + Some(q) => apply_quantity_filter(&addition_info, Some(q), rng), + None => addition_info, + }; + + let mut record = Vec::new(); + for (left_id, field_name, right) in sorted_vec(&addition_info) { + match right { + AdditionTarget::ExistingAsset(right_id) => { + let already_associated = model + .get_asset_by_id(left_id) + .and_then(|a| a.associated_assets.get(&field_name)) + .map(|s| s.contains(&right_id)) + .unwrap_or(false); + if already_associated { + continue; + } + model.add_associated_assets(left_id, &field_name, HashSet::from([right_id]))?; + record.push(ModEffectOp::Assoc(AssocOp::Added { + left: AssetRef::from_model(model, left_id), + field_name, + right: AssetRef::from_model(model, right_id), + })); + } + AdditionTarget::NewAssetType(_) => { + for candidate_id in sorted_vec(base) { + let already_associated = model + .get_asset_by_id(left_id) + .and_then(|a| a.associated_assets.get(&field_name)) + .map(|s| s.contains(&candidate_id)) + .unwrap_or(false); + if already_associated { + continue; + } + if model + .add_associated_assets(left_id, &field_name, HashSet::from([candidate_id])) + .is_ok() + { + record.push(ModEffectOp::Assoc(AssocOp::Added { + left: AssetRef::from_model(model, left_id), + field_name: field_name.clone(), + right: AssetRef::from_model(model, candidate_id), + })); + } + // else: mirrors Python's `logger.error(...)` + continue. + } + } + } + } + Ok(record) +} + +/// Port of `target_op`'s `remove_assoc` closure. Traverses from `base` +/// (not `{node_asset_id}`), unlike `add_assoc_op` above - matches Python. +fn remove_assoc_op( + model: &mut Model, + node_asset_id: i64, + base: &HashSet, + target: &DynTarget, + rng: &mut impl Rng, +) -> Result, ModelEffectsError> { + let (removal_info, quantity) = + parse_removal(model, node_asset_id, base, &target.assoc_traversal, rng)?; + let removal_info = match quantity { + Some(q) => apply_quantity_filter(&removal_info, Some(q), rng), + None => removal_info, + }; + + let mut record = Vec::new(); + for (left_id, field_name, right_id) in sorted_vec(&removal_info) { + let has_field = model + .get_asset_by_id(left_id) + .map(|a| a.associated_assets.contains_key(&field_name)) + .unwrap_or(false); + if has_field { + let left_ref = AssetRef::from_model(model, left_id); + let right_ref = AssetRef::from_model(model, right_id); + model.remove_associated_assets(left_id, &field_name, &HashSet::from([right_id]))?; + record.push(ModEffectOp::Assoc(AssocOp::Removed { + left: left_ref, + field_name, + right: right_ref, + })); + } + // else: mirrors Python's `logger.error(...)` + continue. + } + Ok(record) +} + +/// Port of `_apply_model_effect`. +pub fn apply_model_effect( + model: &mut Model, + node: &AttackGraphNode, + model_effect: &LanguageGraphModelEffect, + rng: &mut impl Rng, +) -> Result, ModelEffectsError> { + let node_asset_id = node + .model_asset + .ok_or(ModelEffectsError::NodeHasNoModelAsset)?; + let base_set = traverse_association_chain( + model, + &HashSet::from([node_asset_id]), + &model_effect.base, + rng, + )?; + + let mut record = Vec::new(); + for dyn_target in &model_effect.targets { + if dyn_target.assoc_traversal.is_empty() { + return Err(ModelEffectsError::EmptyTargetTraversal(node.name.clone())); + } + let op_result = match classify(model_effect.model_effect_type, dyn_target) { + TargetKind::AddAsset => add_asset_op(model, node_asset_id, &base_set, dyn_target, rng)?, + TargetKind::RemoveAsset => { + remove_asset_op(model, node_asset_id, &base_set, dyn_target, rng)? + } + TargetKind::AddAssoc => add_assoc_op(model, node_asset_id, &base_set, dyn_target, rng)?, + TargetKind::RemoveAssoc => { + remove_assoc_op(model, node_asset_id, &base_set, dyn_target, rng)? + } + }; + record.extend(op_result); + } + Ok(record) +} + +/// Port of `execute_model_effects`, minus the `add_new_nodes_to_graph_state` +/// fold - see this module's doc comment for why. Returns the full +/// modification record plus the set of newly-created node ids from +/// `partially_regenerate_graph`, which the caller (Phase B2) folds into +/// its `GraphState`. +pub fn execute_model_effects( + graph: &mut AttackGraph, + model: &mut Model, + action_id: AttackGraphNodeId, + rng: &mut impl Rng, +) -> Result<(Vec, NodeSet), ModelEffectsError> { + let mut record = Vec::new(); + { + let node = &graph.nodes[action_id]; + let additive = node.additive_model_effects.as_deref().unwrap_or(&[]); + for model_effect in additive { + record.extend(apply_model_effect(model, node, model_effect, rng)?); + } + let subtractive = node.subtractive_model_effects.as_deref().unwrap_or(&[]); + for model_effect in subtractive { + record.extend(apply_model_effect(model, node, model_effect, rng)?); + } + } + + let mut new_assets = HashSet::new(); + let mut removed_assets: HashMap = HashMap::new(); + let mut new_associations = HashSet::new(); + let mut removed_associations = HashSet::new(); + for op in &record { + match op { + ModEffectOp::Asset(AssetOp::Added { asset }) => { + new_assets.insert(asset.id); + } + ModEffectOp::Asset(AssetOp::Removed { asset, snapshot }) => { + removed_assets.insert(asset.id, (**snapshot).clone()); + } + ModEffectOp::Assoc(AssocOp::Added { + left, + field_name, + right, + }) => { + new_associations.insert((left.id, field_name.clone(), right.id)); + } + ModEffectOp::Assoc(AssocOp::Removed { + left, + field_name, + right, + }) => { + removed_associations.insert((left.id, field_name.clone(), right.id)); + } + } + } + + let new_nodes = regenerate_graph( + graph, + model, + &new_assets, + &new_associations, + &removed_assets, + &removed_associations, + )?; + Ok((record, new_nodes)) +} + +/// `AttackGraph::partially_regenerate_graph`, followed by a renumbering of +/// the nodes it created. +/// +/// mal-toolbox creates the nodes of `new_assets` in the iteration order of +/// that std `HashSet`, which is random per set instance, so which new node +/// gets which `AttackGraphNode.id` (and which place in +/// `attack_steps`/`defense_steps`) would differ from run to run. The +/// created nodes are therefore renumbered here in a canonical order (by +/// model asset id, then by the order mal-toolbox created an asset's own +/// nodes in), contiguously from the graph's `next_node_id` before the call. +/// This makes node ids reproducible, and ids of nodes that mal-toolbox +/// created and removed again within the batch are not skipped. +pub(crate) fn regenerate_graph( + graph: &mut AttackGraph, + model: &Model, + new_assets: &HashSet, + new_associations: &HashSet<(i64, String, i64)>, + removed_assets: &HashMap, + removed_associations: &HashSet<(i64, String, i64)>, +) -> Result { + let first_new_id = graph.next_node_id; + let new_nodes = graph.partially_regenerate_graph( + model, + new_assets, + new_associations, + removed_assets, + removed_associations, + )?; + + // The surviving nodes created by the call above, in canonical order. + let mut created: Vec<(Option, i64, AttackGraphNodeId)> = graph + .id_to_node + .iter() + .filter(|&(&id, _)| id >= first_new_id) + .map(|(&id, &key)| (graph.nodes[key].model_asset, id, key)) + .collect(); + created.sort_unstable(); + let canonical = created + .iter() + .zip(first_new_id..) + .all(|(&(_, id, _), canonical_id)| id == canonical_id); + if !canonical || graph.next_node_id != first_new_id + created.len() as i64 { + let created_keys: NodeSet = created.iter().map(|&(_, _, key)| key).collect(); + graph.id_to_node.retain(|&id, _| id < first_new_id); + graph.attack_steps.retain(|key| !created_keys.contains(key)); + graph + .defense_steps + .retain(|key| !created_keys.contains(key)); + for (&(_, _, key), id) in created.iter().zip(first_new_id..) { + graph.nodes[key].id = id; + graph.id_to_node.insert(id, key); + match graph.nodes[key].step_type { + AttackStepType::Or | AttackStepType::And => graph.attack_steps.push(key), + AttackStepType::Defense => graph.defense_steps.push(key), + _ => {} + } + } + graph.next_node_id = first_new_id + created.len() as i64; + } + // `partially_regenerate_graph` returns a std `HashSet`; convert at the boundary. + Ok(new_nodes.into_iter().collect()) +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::test_fixtures::wiper_attack_graph; + use crate::types::NodeMap; + use rand::rngs::StdRng; + use rand::SeedableRng; + + fn rng() -> StdRng { + StdRng::seed_from_u64(42) + } + + /// Direct port of + /// `test_dyna_mal_simulator.py::test_apply_model_effect`. + #[test] + fn apply_model_effect_infect_then_exfiltrate() { + let (mut graph, mut model) = wiper_attack_graph(); + let infected_device = model.get_asset_by_name("InfectedDevice").unwrap().id; + assert!(!model + .get_asset_by_id(infected_device) + .unwrap() + .associated_assets + .contains_key("malware")); + + let infect_id = graph.full_name_to_node["InfectedDevice:infect"]; + let infect_effect = graph.nodes[infect_id] + .additive_model_effects + .as_ref() + .unwrap()[0] + .clone(); + let mut r = rng(); + let record = + apply_model_effect(&mut model, &graph.nodes[infect_id], &infect_effect, &mut r) + .unwrap(); + + assert!(model + .get_asset_by_id(infected_device) + .unwrap() + .associated_assets + .contains_key("malware")); + let wiper_id = model.get_asset_by_name("Wiper-7").unwrap().id; + assert!(record.iter().any(|op| matches!( + op, + ModEffectOp::Assoc(AssocOp::Added { left, field_name, right }) + if left.id == infected_device && field_name == "malware" && right.id == wiper_id + ))); + assert!(model + .get_asset_by_id(wiper_id) + .unwrap() + .associated_assets + .get("victim") + .map(|s| s.contains(&infected_device)) + .unwrap_or(false)); + assert!(record.iter().any( + |op| matches!(op, ModEffectOp::Asset(AssetOp::Added { asset }) if asset.id == wiper_id) + )); + + let infected_data = model.get_asset_by_name("InfectedData").unwrap().id; + assert_eq!( + model + .get_asset_by_id(infected_data) + .unwrap() + .associated_assets + .get("node"), + Some(&HashSet::from([infected_device])) + ); + + graph.regenerate_graph(&model).unwrap(); + let exfiltrate_id = graph.full_name_to_node["Wiper-7:exfiltrate"]; + let exfiltrate_effect = graph.nodes[exfiltrate_id] + .additive_model_effects + .as_ref() + .unwrap()[0] + .clone(); + let record2 = apply_model_effect( + &mut model, + &graph.nodes[exfiltrate_id], + &exfiltrate_effect, + &mut r, + ) + .unwrap(); + let c2_server = model.get_asset_by_name("C2Server").unwrap().id; + assert_eq!( + model + .get_asset_by_id(infected_data) + .unwrap() + .associated_assets + .get("node"), + Some(&HashSet::from([infected_device, c2_server])) + ); + assert!(record2.iter().any(|op| matches!( + op, + ModEffectOp::Assoc(AssocOp::Added { left, field_name, right }) + if left.id == c2_server && field_name == "data" && right.id == infected_data + ))); + } + + /// Full-name-based structural comparison of two attack graphs, mirroring + /// `test_dyna_mal_simulator.py::check_graph_equivalence` - used to + /// confirm `execute_model_effects`' incremental + /// `partially_regenerate_graph` call produces the exact same graph a + /// fresh `AttackGraph::from_model` build would, after the same model + /// mutation. Spiritual counterpart to + /// `test_apply_model_effect_modification_record_partially_regenerates_graph`, + /// using wiperLang (hand-built) instead of the + /// `dynamic_remove_many_assoc` scenario (needs Phase C's not-yet-ported + /// scenario-YAML loader to build in Rust). + /// + /// Edges are compared by each endpoint's *full* name (`"Asset:step"`, + /// resolved through the reverse of `full_name_to_node`), not the bare + /// step name, so same-named steps on different assets stay distinct - + /// mirrors Python's `node_id_to_full_name` lookups. + fn assert_graph_equivalent(incremental: &AttackGraph, fresh: &AttackGraph) { + assert_eq!( + incremental.nodes.len(), + fresh.nodes.len(), + "number of nodes differ" + ); + + let incremental_names: HashSet<&String> = incremental.full_name_to_node.keys().collect(); + let fresh_names: HashSet<&String> = fresh.full_name_to_node.keys().collect(); + assert_eq!(incremental_names, fresh_names, "node full_names differ"); + + let id_to_full_name = |g: &AttackGraph| -> NodeMap { + g.full_name_to_node + .iter() + .map(|(name, &id)| (id, name.clone())) + .collect() + }; + let incremental_id_to_name = id_to_full_name(incremental); + let fresh_id_to_name = id_to_full_name(fresh); + + let edge_names = |id_to_name: &NodeMap, ids: &NodeSet| -> HashSet { + ids.iter() + .map(|id| { + id_to_name + .get(id) + .unwrap_or_else(|| { + panic!("edge to node {id:?} missing from full_name_to_node") + }) + .clone() + }) + .collect() + }; + for (full_name, &incr_id) in &incremental.full_name_to_node { + let fresh_id = fresh.full_name_to_node[full_name]; + assert_eq!( + edge_names( + &incremental_id_to_name, + &incremental.nodes[incr_id].children + ), + edge_names(&fresh_id_to_name, &fresh.nodes[fresh_id].children), + "different children for {full_name}" + ); + assert_eq!( + edge_names(&incremental_id_to_name, &incremental.nodes[incr_id].parents), + edge_names(&fresh_id_to_name, &fresh.nodes[fresh_id].parents), + "different parents for {full_name}" + ); + } + } + + /// Port of `test_dyna_mal_simulator.py::assert_no_dangling_associations`: + /// every id in every asset's `associated_assets` must still resolve to + /// a live model asset. + fn assert_no_dangling_associations(model: &Model) { + for asset in model.assets.values() { + for (field_name, others) in &asset.associated_assets { + for &other in others { + assert!( + model.get_asset_by_id(other).is_some(), + "{}.{field_name} points to removed asset id {other}", + asset.name + ); + } + } + } + } + + /// Exercises the same property + /// `test_apply_model_effect_modification_record_partially_regenerates_graph` + /// does - that applying a subtractive model effect's modification + /// record through `partially_regenerate_graph` leaves the graph + /// equivalent to a fresh rebuild from the mutated model - via + /// `Wiper:trigger`'s `R> victim / malware ^ ~data` effect instead of + /// that test's `dynamic_remove_many_assoc` scenario fixture. + #[test] + fn execute_model_effects_removal_matches_fresh_graph_rebuild() { + let (_, mut model) = wiper_attack_graph(); + let next_id = model.next_id; + let wiper_id = model + .add_asset( + "Wiper", + Some("Wiper".to_string()), + Some(next_id), + None, + None, + false, + ) + .unwrap(); + let infected_device = model.get_asset_by_name("InfectedDevice").unwrap().id; + model + .add_associated_assets(wiper_id, "victim", HashSet::from([infected_device])) + .unwrap(); + + let mut graph = AttackGraph::from_model(&model).unwrap(); + let trigger_id = graph.full_name_to_node["Wiper:trigger"]; + let mut r = rng(); + + let (record, _new_nodes) = + execute_model_effects(&mut graph, &mut model, trigger_id, &mut r).unwrap(); + assert!( + record + .iter() + .any(|op| matches!(op, ModEffectOp::Asset(AssetOp::Removed { .. }))), + "Wiper:trigger should remove the Wiper asset itself" + ); + + assert_no_dangling_associations(&model); + + let fresh_graph = AttackGraph::from_model(&model).unwrap(); + assert_graph_equivalent(&graph, &fresh_graph); + } +} diff --git a/core/malsim-core/src/model_state.rs b/core/malsim-core/src/model_state.rs new file mode 100644 index 00000000..8994cddb --- /dev/null +++ b/core/malsim-core/src/model_state.rs @@ -0,0 +1,325 @@ +//! Rust port of `python/malsim/dyna_mal_simulator/model_state.py` - see +//! `PORTING_NOTES.md` §6 Phase B2. +//! +//! Restores a live [`Model`] (and the [`AttackGraph`] derived from it) to +//! a previously-captured snapshot, by computing the minimal asset/ +//! association diff and applying it via the same +//! `Model::add_asset`/`remove_asset`/`add_associated_assets`/ +//! `remove_associated_assets` + `AttackGraph::partially_regenerate_graph` +//! primitives B1 already uses. +//! +//! **Snapshot type diverges from Python's `dict[str, Any]` (`Model:: +//! to_dict()`'s JSON shape), and deliberately isn't ported as-is - see +//! §10.** Python's `reconcile_model_to_snapshot` computes its diff by +//! comparing `frozenset(other.id for other in others)` (asset ids, as +//! Python `int`s) against `frozenset(other_ids)` where `other_ids` is a +//! `dict[str, str]` (`{"": ""}`) - iterating a dict yields its +//! *keys*, so that frozenset holds `str`s, not `int`s. The two frozensets +//! can never intersect, so every reconciliation silently falls back to a +//! full remove-and-re-add of every association on every call - the +//! observable end state is still correct (a full teardown-and-rebuild +//! reaches the same target), just never the "minimal diff" the function's +//! own docstring promises. Rust's [`ModelSnapshot`] stores association +//! targets as `i64` on both sides (no JSON round-trip, no string keys), +//! so this type mismatch cannot exist here - porting the bug faithfully +//! would mean deliberately mistyping one side, which contradicts writing +//! correct Rust. This function therefore implements the *intended* +//! minimal-diff behavior the Python docstring describes, not its actual +//! current behavior. + +use crate::types::NodeSet; +use std::collections::{HashMap, HashSet}; +use std::fmt; + +use maltoolbox_attackgraph::AttackGraph; +use maltoolbox_model::{AssetSnapshot, Model, ModelError}; + +#[derive(Debug, Clone)] +pub struct ModelSnapshotAsset { + pub asset_type: String, + pub name: String, + pub associated_assets: HashMap>, +} + +/// Asset id -> snapshot of that asset's type/name/associations, mirroring +/// `Model.to_dict()['assets']`'s shape minus the JSON string-id detour - +/// see this module's doc comment. +pub type ModelSnapshot = HashMap; + +#[derive(Debug)] +pub enum ModelStateError { + Model(ModelError), + Graph(maltoolbox_attackgraph::GraphError), +} + +impl From for ModelStateError { + fn from(e: ModelError) -> Self { + ModelStateError::Model(e) + } +} + +impl From for ModelStateError { + fn from(e: maltoolbox_attackgraph::GraphError) -> Self { + ModelStateError::Graph(e) + } +} + +impl fmt::Display for ModelStateError { + fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { + match self { + ModelStateError::Model(e) => write!(f, "{e}"), + ModelStateError::Graph(e) => write!(f, "{e}"), + } + } +} + +impl std::error::Error for ModelStateError {} + +/// Captures `model`'s current state as a [`ModelSnapshot`] - the Rust +/// equivalent of Python's `attack_graph.model.to_dict()` call at +/// `DynaMalSimulator.__init__` time (see `dyna_mal_simulator/ +/// simulator.py`), taken once and restored on every `reset()` via +/// `reset_model_effects`. +pub fn capture_model_snapshot(model: &Model) -> ModelSnapshot { + model + .assets + .iter() + .map(|(&id, asset)| { + ( + id, + ModelSnapshotAsset { + asset_type: asset.asset_type.clone(), + name: asset.name.clone(), + associated_assets: asset.associated_assets.clone(), + }, + ) + }) + .collect() +} + +/// `(new_assets, removed_assets, new_associations, removed_associations)` - +/// the same shape `AttackGraph::partially_regenerate_graph` takes, and B1's +/// `execute_model_effects` builds for the same call. +pub type ModelReconciliation = ( + HashSet, + HashMap, + HashSet<(i64, String, i64)>, + HashSet<(i64, String, i64)>, +); + +/// Port of `reconcile_model_to_snapshot`. Mutates `model` in place to +/// match `snapshot` exactly and returns a [`ModelReconciliation`]. +pub fn reconcile_model_to_snapshot( + model: &mut Model, + snapshot: &ModelSnapshot, +) -> Result { + let current_ids: HashSet = model.assets.keys().copied().collect(); + let target_ids: HashSet = snapshot.keys().copied().collect(); + + let mut new_assets = HashSet::new(); + let mut removed_assets: HashMap = HashMap::new(); + + // The std sets/maps here iterate in a per-instance random order; the + // model and graph are mutated in the order below, so use id order. + let mut to_remove: Vec = current_ids.difference(&target_ids).copied().collect(); + to_remove.sort_unstable(); + for asset_id in to_remove { + let snap = model.remove_asset(asset_id)?; + removed_assets.insert(asset_id, snap); + } + + let mut to_add: Vec = target_ids.difference(¤t_ids).copied().collect(); + to_add.sort_unstable(); + for asset_id in to_add { + let asset_snapshot = &snapshot[&asset_id]; + model.add_asset( + &asset_snapshot.asset_type, + Some(asset_snapshot.name.clone()), + Some(asset_id), + None, + None, + false, + )?; + new_assets.insert(asset_id); + } + + let mut new_associations = HashSet::new(); + let mut removed_associations = HashSet::new(); + + let mut snapshot_ids: Vec = snapshot.keys().copied().collect(); + snapshot_ids.sort_unstable(); + for asset_id in snapshot_ids { + let asset_snapshot = &snapshot[&asset_id]; + let current_associations: HashMap> = model + .get_asset_by_id(asset_id) + .map(|a| a.associated_assets.clone()) + .unwrap_or_default(); + let target_associations = &asset_snapshot.associated_assets; + + let mut field_names: HashSet<&String> = current_associations.keys().collect(); + field_names.extend(target_associations.keys()); + + let mut field_names: Vec<&String> = field_names.into_iter().collect(); + field_names.sort_unstable(); + for field_name in field_names { + let current_for_field = current_associations + .get(field_name) + .cloned() + .unwrap_or_default(); + let target_for_field = target_associations + .get(field_name) + .cloned() + .unwrap_or_default(); + + let ids_to_remove: HashSet = current_for_field + .difference(&target_for_field) + .copied() + .collect(); + if !ids_to_remove.is_empty() { + model.remove_associated_assets(asset_id, field_name, &ids_to_remove)?; + for &other in &ids_to_remove { + removed_associations.insert((asset_id, field_name.clone(), other)); + } + } + + let ids_to_add: HashSet = target_for_field + .difference(¤t_for_field) + .copied() + .collect(); + if !ids_to_add.is_empty() { + model.add_associated_assets(asset_id, field_name, ids_to_add.clone())?; + for &other in &ids_to_add { + new_associations.insert((asset_id, field_name.clone(), other)); + } + } + } + } + + Ok(( + new_assets, + removed_assets, + new_associations, + removed_associations, + )) +} + +/// Port of `reset_model_effects`. Python's version returns `None` - its +/// one caller (`dyna_reset`) always rebuilds `GraphState` from scratch via +/// `compute_initial_graph_state` afterwards rather than folding in new +/// nodes incrementally, so the new-node ids go unused there. This port +/// returns them anyway (same shape as `execute_model_effects`'s return) +/// since discarding them costs the caller nothing and some callers may +/// want them. +pub fn reset_model_effects( + graph: &mut AttackGraph, + model: &mut Model, + snapshot: &ModelSnapshot, +) -> Result { + let (new_assets, removed_assets, new_associations, removed_associations) = + reconcile_model_to_snapshot(model, snapshot)?; + Ok(crate::model_effects::regenerate_graph( + graph, + model, + &new_assets, + &new_associations, + &removed_assets, + &removed_associations, + )?) +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::model_effects::execute_model_effects; + use crate::test_fixtures::wiper_attack_graph; + use rand::rngs::StdRng; + use rand::SeedableRng; + + fn rng() -> StdRng { + StdRng::seed_from_u64(5) + } + + #[test] + fn reconcile_model_to_snapshot_restores_removed_and_re_adds_associations() { + let (_graph, mut model) = wiper_attack_graph(); + let snapshot = capture_model_snapshot(&model); + + let next_id = model.next_id; + let wiper_id = model + .add_asset( + "Wiper", + Some("Wiper".to_string()), + Some(next_id), + None, + None, + false, + ) + .unwrap(); + let infected_device = model.get_asset_by_name("InfectedDevice").unwrap().id; + model + .add_associated_assets(wiper_id, "victim", HashSet::from([infected_device])) + .unwrap(); + + let c2_server = model.get_asset_by_name("C2Server").unwrap().id; + model + .remove_associated_assets(infected_device, "sendTo", &HashSet::from([c2_server])) + .unwrap(); + + let (new_assets, removed_assets, new_associations, _removed_associations) = + reconcile_model_to_snapshot(&mut model, &snapshot).unwrap(); + + assert!(new_assets.is_empty()); + assert!(removed_assets.contains_key(&wiper_id)); + // The InfectedDevice<->C2Server link is bidirectional + // (sendTo/receiveFrom are opposite fieldnames of the same + // association) - `Model::add_associated_assets` restores both + // sides as soon as either is re-added, so depending on `snapshot`'s + // `HashMap` iteration order, *either* `(infected_device, "sendTo", + // c2_server)` *or* `(c2_server, "receiveFrom", infected_device)` + // ends up the one explicitly recorded here - whichever asset's + // turn came first in the reconciliation loop. Assert on the + // resulting model state instead of which side got the credit. + assert!( + new_associations.contains(&(infected_device, "sendTo".to_string(), c2_server)) + || new_associations.contains(&( + c2_server, + "receiveFrom".to_string(), + infected_device + )) + ); + + assert!( + model.get_asset_by_id(wiper_id).is_none(), + "Wiper should be gone after reconciling" + ); + let vulnerable_device = model.get_asset_by_name("VulnerableDevice").unwrap().id; + assert_eq!( + model + .get_asset_by_id(infected_device) + .unwrap() + .associated_assets + .get("sendTo"), + Some(&HashSet::from([c2_server, vulnerable_device])) + ); + } + + #[test] + fn reset_model_effects_restores_graph_to_pristine_snapshot() { + let (mut graph, mut model) = wiper_attack_graph(); + let snapshot = capture_model_snapshot(&model); + let pristine_names: HashSet = graph.full_name_to_node.keys().cloned().collect(); + + let infect_id = graph.full_name_to_node["InfectedDevice:infect"]; + let mut r = rng(); + execute_model_effects(&mut graph, &mut model, infect_id, &mut r).unwrap(); + let grown_names: HashSet = graph.full_name_to_node.keys().cloned().collect(); + assert_ne!( + grown_names, pristine_names, + "infect's model effect should have grown the graph" + ); + + reset_model_effects(&mut graph, &mut model, &snapshot).unwrap(); + let restored_names: HashSet = graph.full_name_to_node.keys().cloned().collect(); + assert_eq!(restored_names, pristine_names); + } +} diff --git a/core/malsim-core/src/necessity.rs b/core/malsim-core/src/necessity.rs new file mode 100644 index 00000000..034156c5 --- /dev/null +++ b/core/malsim-core/src/necessity.rs @@ -0,0 +1,308 @@ +//! Rust port of `python/malsim/mal_simulator/graph_processing.py`'s +//! necessity propagation - see `PORTING_NOTES.md` §5 Phase A3. +//! +//! Only necessity lives here; the viability/pruning half of the same +//! Python file (`calculate_viability`/`evaluate_viability`/ +//! `prune_unviable_and_unnecessary_nodes`) is in `crate::viability` - +//! originally left unported as dead code at A3, ported at B7 so the Python +//! file could be deleted without losing its test coverage (see +//! `PORTING_NOTES.md` §10). +//! +//! Rust-native tests (backfilled at Phase A4, per `PORTING_NOTES.md` §10's +//! A3 entry): every case needs a real `AttackGraphNode` with a specific +//! `step_type`, which requires a real +//! `maltoolbox_language::graph::LanguageGraph` to mint (`AttackStepId` is +//! a slotmap key, not fakeable) - `maltoolbox-language` was added as a +//! test-only (`dev-dependencies`) crate dependency when A4 hit the same +//! wall for its own traversal-predicate tests. See `crate::test_fixtures` +//! and `PORTING_NOTES.md` §10 for the discussion. + +use crate::types::{NodeDenseMap, NodeSet}; +use std::fmt; + +use maltoolbox_attackgraph::{AttackGraph, AttackGraphNode, AttackGraphNodeId}; +use maltoolbox_language::graph::attack_step::AttackStepType; + +#[derive(Debug, Clone, PartialEq, Eq)] +pub enum NecessityError { + /// Mirrors Python's `assert isinstance(node.existence_status, bool)` + /// for `exist`/`notExist` nodes. + MissingExistenceStatus(AttackGraphNodeId), + /// A parent's necessity was read before it was computed - mirrors + /// Python's `necessity_per_node[parent]` `KeyError` (the dict must be + /// pre-seeded for every node before `evaluate_necessity` runs; see + /// `calculate_necessity`). + MissingNecessity(AttackGraphNodeId), +} + +impl fmt::Display for NecessityError { + fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { + match self { + NecessityError::MissingExistenceStatus(id) => { + write!(f, "existence status not defined for node {id:?}") + } + NecessityError::MissingNecessity(id) => { + write!(f, "necessity not yet computed for parent node {id:?}") + } + } + } +} + +impl std::error::Error for NecessityError {} + +/// Port of `evaluate_necessity`. Takes `node_id` alongside `node` since +/// `AttackGraphNode` doesn't carry its own slotmap key (see +/// `maltoolbox_attackgraph::ids::AttackGraphNodeId`'s doc comment) - +/// needed both to check self-membership in `enabled_defenses` and for +/// error context. +pub fn evaluate_necessity( + node_id: AttackGraphNodeId, + node: &AttackGraphNode, + necessity_per_node: &NodeDenseMap, + enabled_defenses: &NodeSet, +) -> Result { + let parent_necessity = |id: &AttackGraphNodeId| -> Result { + necessity_per_node + .get(*id) + .copied() + .ok_or(NecessityError::MissingNecessity(*id)) + }; + + match node.step_type { + AttackStepType::Exist => node + .existence_status + .map(|exists| !exists) + .ok_or(NecessityError::MissingExistenceStatus(node_id)), + AttackStepType::NotExist => node + .existence_status + .ok_or(NecessityError::MissingExistenceStatus(node_id)), + AttackStepType::Defense => Ok(enabled_defenses.contains(&node_id)), + // Python: `all(necessity_per_node[p] for p in node.parents) or not + // node.parents` - `all(())` is already `True`, so the `or not + // node.parents` is redundant for "or" (unlike "and" below); this + // loop mirrors that without the redundant check. + AttackStepType::Or => { + for parent in &node.parents { + if !parent_necessity(parent)? { + return Ok(false); + } + } + Ok(true) + } + // Python: `any(necessity_per_node[p] for p in node.parents) or not + // node.parents` - `any(())` is `False`, so the empty-parents case + // genuinely needs the explicit `True` here (not redundant, unlike + // "or"). + AttackStepType::And => { + if node.parents.is_empty() { + return Ok(true); + } + for parent in &node.parents { + if parent_necessity(parent)? { + return Ok(true); + } + } + Ok(false) + } + } +} + +/// Port of `_propagate_necessity_from_node`. +/// +/// Implemented with an explicit stack of child iterators instead of +/// recursion: the visiting order (depth-first, each child's subtree fully +/// processed before the next sibling is evaluated) and the map updates are +/// identical to the recursive formulation, and the returned set is the +/// union of all changed nodes. +pub fn propagate_necessity_from_node( + node_id: AttackGraphNodeId, + graph: &AttackGraph, + necessity_per_node: &mut NodeDenseMap, +) -> Result { + let mut changed_nodes = NodeSet::default(); + // Python passes `frozenset()` (not the real `enabled_defenses`) + // into the inner `evaluate_necessity` call - mirrored exactly, + // not a bug: a propagated child is never itself re-evaluated as a + // `defense` node through this path (only `or`/`and` nodes do, + // since propagation starts from `exist`/`notExist`/`defense` + // nodes and walks downstream `or`/`and` children, whose necessity + // doesn't consult `enabled_defenses` at all). + let no_defenses = NodeSet::default(); + let mut stack = vec![graph.nodes[node_id].children.iter()]; + while let Some(children) = stack.last_mut() { + let Some(&child_id) = children.next() else { + stack.pop(); + continue; + }; + let child = &graph.nodes[child_id]; + let is_necessary = evaluate_necessity(child_id, child, necessity_per_node, &no_defenses)?; + if necessity_per_node.get(child_id).copied() != Some(is_necessary) { + necessity_per_node.insert(child_id, is_necessary); + changed_nodes.insert(child_id); + stack.push(child.children.iter()); + } + } + Ok(changed_nodes) +} + +/// Port of `calculate_necessity`. +pub fn calculate_necessity( + graph: &AttackGraph, + enabled_defenses: &NodeSet, +) -> Result, NecessityError> { + let mut necessity_per_node = NodeDenseMap::with_capacity(graph.nodes.capacity()); + for id in graph.nodes.keys() { + necessity_per_node.insert(id, true); + } + + for (node_id, node) in &graph.nodes { + if matches!( + node.step_type, + AttackStepType::Exist | AttackStepType::NotExist | AttackStepType::Defense + ) { + let is_necessary = + evaluate_necessity(node_id, node, &necessity_per_node, enabled_defenses)?; + necessity_per_node.insert(node_id, is_necessary); + if !is_necessary { + propagate_necessity_from_node(node_id, graph, &mut necessity_per_node)?; + } + } + } + Ok(necessity_per_node) +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::test_fixtures::{add_dummy_node, dummy_graph}; + + // Port of `tests/test_graph_processing.py::test_necessity_necessary`. + #[test] + fn calculate_necessity_necessary_nodes() { + let mut graph = dummy_graph(); + + // exists, existence_status = False -> necessary + let exist_node = add_dummy_node(&mut graph, "DummyExistAttackStep"); + graph.nodes[exist_node].existence_status = Some(false); + + // notExists, existence_status = True -> necessary + let not_exist_node = add_dummy_node(&mut graph, "DummyNotExistAttackStep"); + graph.nodes[not_exist_node].existence_status = Some(true); + + // Defense status on -> necessary + let enabled_defense_step = add_dummy_node(&mut graph, "DummyDefenseAttackStep"); + + // or-node with necessary parents -> necessary + let or_node = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let or_node_parent = add_dummy_node(&mut graph, "DummyOrAttackStep"); + graph.nodes[or_node].parents.insert(or_node_parent); + graph.nodes[or_node_parent].children.insert(or_node); + + // and-node with at least one necessary parent -> necessary + let and_node = add_dummy_node(&mut graph, "DummyAndAttackStep"); + let and_node_parent1 = add_dummy_node(&mut graph, "DummyAndAttackStep"); + let and_node_parent2 = add_dummy_node(&mut graph, "DummyAndAttackStep"); + graph.nodes[and_node].parents = [and_node_parent1, and_node_parent2].into_iter().collect(); + graph.nodes[and_node_parent1].children.insert(and_node); + graph.nodes[and_node_parent2].children.insert(and_node); + + let enabled_defenses: NodeSet = [enabled_defense_step].into_iter().collect(); + let necessity_per_node = calculate_necessity(&graph, &enabled_defenses).unwrap(); + + assert!(necessity_per_node[exist_node]); + assert!(necessity_per_node[not_exist_node]); + assert!(necessity_per_node[enabled_defense_step]); + assert!(necessity_per_node[or_node]); + assert!(necessity_per_node[and_node]); + } + + // Port of `tests/test_graph_processing.py::test_necessity_unnecessary`. + #[test] + fn calculate_necessity_unnecessary_nodes() { + let mut graph = dummy_graph(); + + // exists, existence_status = True -> unnecessary + let exist_node = add_dummy_node(&mut graph, "DummyExistAttackStep"); + graph.nodes[exist_node].existence_status = Some(true); + + // notExists, existence_status = False -> unnecessary + let not_exist_node = add_dummy_node(&mut graph, "DummyNotExistAttackStep"); + graph.nodes[not_exist_node].existence_status = Some(false); + + // Defense status off -> unnecessary + let disabled_defense_step = add_dummy_node(&mut graph, "DummyDefenseAttackStep"); + + // or-node with unnecessary parent -> unnecessary + let or_node = add_dummy_node(&mut graph, "DummyOrAttackStep"); + graph.nodes[or_node].parents.insert(disabled_defense_step); + graph.nodes[disabled_defense_step].children.insert(or_node); + + // and-node with only unnecessary parents -> unnecessary + let and_node = add_dummy_node(&mut graph, "DummyAndAttackStep"); + graph.nodes[and_node].parents.insert(disabled_defense_step); + graph.nodes[disabled_defense_step].children.insert(and_node); + + let enabled_defenses = NodeSet::default(); + let necessity_per_node = calculate_necessity(&graph, &enabled_defenses).unwrap(); + + assert!(!necessity_per_node[exist_node]); + assert!(!necessity_per_node[not_exist_node]); + assert!(!necessity_per_node[disabled_defense_step]); + assert!(!necessity_per_node[or_node]); + assert!(!necessity_per_node[and_node]); + } + + // Port of + // `tests/test_graph_processing.py::test_analyzers_apriori_propagate_necessity`. + #[test] + fn propagate_necessity_from_node_updates_downstream_or_and_and_nodes() { + let mut graph = dummy_graph(); + + let np1 = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let np2 = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let unp1 = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let unp2 = add_dummy_node(&mut graph, "DummyOrAttackStep"); + + let or_1unp = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let or_2np = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let and_1np = add_dummy_node(&mut graph, "DummyAndAttackStep"); + let and_2unp = add_dummy_node(&mut graph, "DummyAndAttackStep"); + + graph.nodes[or_1unp].parents = [np1, unp1].into_iter().collect(); + graph.nodes[or_2np].parents = [np1, np2].into_iter().collect(); + graph.nodes[and_1np].parents = [np1, unp1].into_iter().collect(); + graph.nodes[and_2unp].parents = [unp1, unp2].into_iter().collect(); + + graph.nodes[np1].children = [or_1unp, or_2np, and_1np].into_iter().collect(); + graph.nodes[np2].children = [or_2np].into_iter().collect(); + graph.nodes[unp1].children = [or_1unp, and_1np, and_2unp].into_iter().collect(); + graph.nodes[unp2].children = [and_2unp].into_iter().collect(); + + let enabled_defenses = NodeSet::default(); + let mut necessity_per_node = calculate_necessity(&graph, &enabled_defenses).unwrap(); + // Force unp1/unp2 unnecessary, then re-propagate from every + // top-level parent - mirrors the Python test exercising + // `_propagate_necessity_from_node` directly, independent of + // `calculate_necessity`'s own top-level loop (none of these nodes + // are `exist`/`notExist`/`defense`, so that loop never visits + // them). + necessity_per_node.insert(unp1, false); + necessity_per_node.insert(unp2, false); + + let mut changed_nodes = NodeSet::default(); + for &parent in &[np1, np2, unp1, unp2] { + changed_nodes.extend( + propagate_necessity_from_node(parent, &graph, &mut necessity_per_node).unwrap(), + ); + } + + assert_eq!(changed_nodes, [or_1unp, and_2unp].into_iter().collect()); + + for node in [np1, np2, or_2np, and_1np] { + assert!(necessity_per_node[node]); + } + for node in [unp1, unp2, or_1unp, and_2unp] { + assert!(!necessity_per_node[node]); + } + } +} diff --git a/core/malsim-core/src/observability.rs b/core/malsim-core/src/observability.rs new file mode 100644 index 00000000..14e5d89d --- /dev/null +++ b/core/malsim-core/src/observability.rs @@ -0,0 +1,157 @@ +//! Rust port of `python/malsim/mal_simulator/observability.py` - see +//! `PORTING_NOTES.md` §5 Phase A6. +//! +//! `node_is_observable` takes the already-flattened `observable_steps` id +//! set instead of a `NodePropertyRule[bool]`, same shape as A5's +//! `node_is_actionable_flat` (`attack_surface.rs`/`defense_surface.rs`): +//! `None` means "no rule configured" (every node observable, mirroring +//! `observed_nodes`'s own `if observable_steps_rule: ... else: +//! compromised_nodes` branch - no filtering at all), `Some(set)` means +//! exactly the already-true-valued ids are observable (mirroring a +//! flattened `NodePropertyRule.per_node()` result). A third private copy +//! of the same four-line idiom, for the same reason A5 kept two +//! independent copies rather than sharing one in `graph_utils.rs`: the +//! real `node_is_observable` stays Python per §2.4, operating on the real +//! `NodePropertyRule` directly, so a same-shaped-but-different Rust +//! function of the same name here would be confusing to a reader +//! grepping for its ported counterpart and not finding one. + +use crate::types::{NodeMap, NodeSet}; + +use maltoolbox_attackgraph::{AttackGraph, AttackGraphNodeId}; +use rand::Rng; + +use crate::false_alerts::{generate_false_negatives, generate_false_positives}; + +/// Port of `node_is_observable`, operating on the already-flattened id-set +/// instead of a `NodePropertyRule` - see module docs. +fn node_is_observable_flat(observable_steps: Option<&NodeSet>, node_id: AttackGraphNodeId) -> bool { + match observable_steps { + Some(steps) => steps.contains(&node_id), + None => true, + } +} + +/// Port of `observed_nodes`. Takes `graph` (rather than the whole +/// `MalSimulatorState` the Python function reads `sim_state.attack_graph` +/// off) since that's the only field this function actually needs - +/// `generate_false_positives` only reads `attack_graph.attack_steps`. +pub fn observed_nodes( + observable_steps: Option<&NodeSet>, + false_positive_rates: Option<&NodeMap>, + false_negative_rates: Option<&NodeMap>, + graph: &AttackGraph, + compromised_nodes: &NodeSet, + rng: &mut impl Rng, +) -> NodeSet { + let observable_steps_set: NodeSet = if observable_steps.is_some() { + compromised_nodes + .iter() + .copied() + .filter(|&id| node_is_observable_flat(observable_steps, id)) + .collect() + } else { + compromised_nodes.clone() + }; + + let false_negatives = + generate_false_negatives(graph, false_negative_rates, compromised_nodes, rng); + let false_positives = generate_false_positives( + false_positive_rates, + graph.attack_steps.iter().copied(), + rng, + ); + + observable_steps_set + .difference(&false_negatives) + .copied() + .collect::() + .union(&false_positives) + .copied() + .collect() +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::test_fixtures::{add_dummy_node, dummy_graph}; + use rand::rngs::StdRng; + use rand::SeedableRng; + + #[test] + fn node_is_observable_flat_true_for_everyone_without_rule() { + let mut graph = dummy_graph(); + let n = add_dummy_node(&mut graph, "DummyOrAttackStep"); + assert!(node_is_observable_flat(None, n)); + } + + #[test] + fn node_is_observable_flat_respects_flattened_set() { + let mut graph = dummy_graph(); + let observable = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let not_observable = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let set: NodeSet = [observable].into_iter().collect(); + assert!(node_is_observable_flat(Some(&set), observable)); + assert!(!node_is_observable_flat(Some(&set), not_observable)); + } + + #[test] + fn observed_nodes_without_any_rule_sees_every_compromised_node() { + let mut graph = dummy_graph(); + let n1 = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let n2 = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let compromised: NodeSet = [n1, n2].into_iter().collect(); + let mut rng = StdRng::seed_from_u64(1); + + let result = observed_nodes(None, None, None, &graph, &compromised, &mut rng); + assert_eq!(result, compromised); + } + + #[test] + fn observed_nodes_filters_by_observable_steps() { + let mut graph = dummy_graph(); + let observable = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let not_observable = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let compromised: NodeSet = [observable, not_observable].into_iter().collect(); + let observable_set: NodeSet = [observable].into_iter().collect(); + let mut rng = StdRng::seed_from_u64(2); + + let result = observed_nodes( + Some(&observable_set), + None, + None, + &graph, + &compromised, + &mut rng, + ); + assert_eq!(result, [observable].into_iter().collect()); + } + + #[test] + fn observed_nodes_false_negative_rate_one_hides_compromised_node() { + let mut graph = dummy_graph(); + let n = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let compromised: NodeSet = [n].into_iter().collect(); + let fn_rates: NodeMap<_> = [(n, 1.0)].into_iter().collect(); + let mut rng = StdRng::seed_from_u64(3); + + let result = observed_nodes(None, None, Some(&fn_rates), &graph, &compromised, &mut rng); + assert!(result.is_empty()); + } + + #[test] + fn observed_nodes_false_positive_rate_one_adds_uncompromised_attack_step() { + let mut graph = dummy_graph(); + let compromised_node = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let other_attack_step = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let compromised: NodeSet = [compromised_node].into_iter().collect(); + let fp_rates: NodeMap<_> = graph.attack_steps.iter().map(|&id| (id, 1.0)).collect(); + let mut rng = StdRng::seed_from_u64(4); + + let result = observed_nodes(None, Some(&fp_rates), None, &graph, &compromised, &mut rng); + // Every attack step (compromised or not) becomes a false positive + // at rate 1.0, unioned with the (unfiltered) observable set. + assert!(result.contains(&compromised_node)); + assert!(result.contains(&other_attack_step)); + } +} diff --git a/core/malsim-core/src/policies/heuristic_agent.rs b/core/malsim-core/src/policies/heuristic_agent.rs new file mode 100644 index 00000000..b52d2eb6 --- /dev/null +++ b/core/malsim-core/src/policies/heuristic_agent.rs @@ -0,0 +1,130 @@ +//! Port of `defenders/heuristic_agent.py`. + +use crate::types::NodeSet; + +use maltoolbox_attackgraph::AttackGraphNodeId; +use serde_json::{Map, Value}; + +use crate::agent_state::{AgentState, DefenderState}; +use crate::policies::{DecisionAgent, PolicyError}; +use crate::rewards::node_reward; + +/// The loop both defenders share: the cheapest defense on the action +/// surface (ties: lowest node id) whose node satisfies `worth_enabling`, +/// given the nodes observed as compromised so far. +fn cheapest_defense( + policy: &'static str, + compromised_nodes: &mut NodeSet, + agent_state: &AgentState<'_>, + worth_enabling: impl Fn(&DefenderState<'_>, AttackGraphNodeId, &NodeSet) -> bool, +) -> Result, PolicyError> { + let AgentState::Defender(defender_state) = agent_state else { + return Err(PolicyError::WrongAgentType { + policy, + expected: "defender", + }); + }; + compromised_nodes.extend(defender_state.step_observed_nodes.iter().copied()); + + let mut selected_node_cost = f64::INFINITY; + let mut selected_node = None; + + // To make it deterministic + for node in agent_state.sorted_action_surface() { + if compromised_nodes.contains(&node) { + continue; + } + + let node_cost = node_reward( + &defender_state.attack_graph.nodes[node], + defender_state.settings.rewards.as_ref(), + defender_state.model, + ); + + if node_cost < selected_node_cost && worth_enabling(defender_state, node, compromised_nodes) + { + selected_node = Some(node); + selected_node_cost = node_cost; + } + } + + Ok(selected_node) +} + +/// A defender that defends compromised assets using notPresent. +/// +/// Strategy: enable the cheapest defense node that has compromised child +/// nodes. +#[derive(Debug, Clone, Default)] +pub struct DefendCompromisedDefender { + compromised_nodes: NodeSet, +} + +impl DefendCompromisedDefender { + /// `agent_config`'s `seed`/`randomize` are accepted but, as in Python, + /// not used. + pub fn new(_agent_config: &Map) -> Self { + Self::default() + } +} + +impl DecisionAgent for DefendCompromisedDefender { + /// Return an action that disables a compromised node. + fn get_next_action( + &mut self, + agent_state: &AgentState<'_>, + ) -> Result, PolicyError> { + cheapest_defense( + "DefendCompromisedDefender", + &mut self.compromised_nodes, + agent_state, + |defender_state, node, compromised_nodes| { + defender_state.attack_graph.nodes[node] + .children + .iter() + .any(|child_node| compromised_nodes.contains(child_node)) + }, + ) + } +} + +/// A defender that defends assets about to be compromised. +/// +/// Strategy: enable the cheapest defense node that has a non compromised +/// child that has a compromised parent. +#[derive(Debug, Clone, Default)] +pub struct DefendFutureCompromisedDefender { + compromised_nodes: NodeSet, +} + +impl DefendFutureCompromisedDefender { + /// `agent_config`'s `seed`/`randomize` are accepted but, as in Python, + /// not used. + pub fn new(_agent_config: &Map) -> Self { + Self::default() + } +} + +impl DecisionAgent for DefendFutureCompromisedDefender { + /// Return an action that disables a node that could be compromised + /// next. + fn get_next_action( + &mut self, + agent_state: &AgentState<'_>, + ) -> Result, PolicyError> { + cheapest_defense( + "DefendFutureCompromisedDefender", + &mut self.compromised_nodes, + agent_state, + |defender_state, node, compromised_nodes| { + let graph = defender_state.attack_graph; + graph.nodes[node].children.iter().any(|&child_node| { + !compromised_nodes.contains(&child_node) + && graph.nodes.get(child_node).is_some_and(|child| { + child.parents.iter().any(|p| compromised_nodes.contains(p)) + }) + }) + }, + ) + } +} diff --git a/core/malsim-core/src/policies/keyboard_input.rs b/core/malsim-core/src/policies/keyboard_input.rs new file mode 100644 index 00000000..c7bc256b --- /dev/null +++ b/core/malsim-core/src/policies/keyboard_input.rs @@ -0,0 +1,101 @@ +//! Port of `keyboard_input.py`. + +use std::io::{self, BufRead, Write}; + +use maltoolbox_attackgraph::AttackGraphNodeId; + +use crate::agent_state::AgentState; +use crate::policies::{DecisionAgent, PolicyError}; + +/// An agent that makes decisions by asking user for keyboard input. +/// +/// Generic over its input/output so it can be driven by something other +/// than a terminal; `KeyboardAgent::new()` uses stdin/stdout. +pub struct KeyboardAgent, W = io::Stdout> { + input: R, + output: W, +} + +impl KeyboardAgent { + pub fn new() -> Self { + KeyboardAgent { + input: io::BufReader::new(io::stdin()), + output: io::stdout(), + } + } +} + +impl Default for KeyboardAgent { + fn default() -> Self { + Self::new() + } +} + +impl KeyboardAgent { + pub fn with_io(input: R, output: W) -> Self { + KeyboardAgent { input, output } + } +} + +/// What the user typed: `None` for an invalid entry, `Some(None)` for an +/// empty line (wait), `Some(Some(index))` for a listed action. +fn parse_action(user_input: &str, num_actions: usize) -> Option> { + if user_input.is_empty() { + return Some(None); + } + match user_input.parse::() { + Ok(index) if index < num_actions => Some(Some(index)), + _ => None, + } +} + +impl DecisionAgent for KeyboardAgent { + /// Compute action from action_surface. + fn get_next_action( + &mut self, + agent_state: &AgentState<'_>, + ) -> Result, PolicyError> { + let index_to_node = agent_state.sorted_action_surface(); + if index_to_node.is_empty() { + writeln!(self.output, "No actions to pick for defender")?; + return Ok(None); + } + let full_name = |node| { + agent_state + .attack_graph() + .full_name_of(node, Some(agent_state.model())) + }; + + let index = loop { + writeln!(self.output, "Available actions:")?; + for (i, &node) in index_to_node.iter().enumerate() { + writeln!(self.output, "{i}. {}", full_name(node))?; + } + writeln!(self.output, "Enter action or leave empty to wait:")?; + write!(self.output, "> ")?; + self.output.flush()?; + + let mut user_input = String::new(); + if self.input.read_line(&mut user_input)? == 0 { + // Python's `input()` raises `EOFError` here. + return Err(io::Error::from(io::ErrorKind::UnexpectedEof).into()); + } + match parse_action(user_input.trim(), index_to_node.len()) { + Some(index) => break index, + None => writeln!(self.output, "Invalid action.")?, + } + }; + + match index { + Some(index) => { + let node = index_to_node[index]; + writeln!(self.output, "Selected action: {}", full_name(node))?; + Ok(Some(node)) + } + None => { + writeln!(self.output, "wait")?; + Ok(None) + } + } + } +} diff --git a/core/malsim-core/src/policies/mod.rs b/core/malsim-core/src/policies/mod.rs new file mode 100644 index 00000000..3e809ed0 --- /dev/null +++ b/core/malsim-core/src/policies/mod.rs @@ -0,0 +1,186 @@ +//! Port of `python/malsim/policies`: the `DecisionAgent` interface and the +//! built-in heuristic agents a scenario's `policy` field can name. +//! +//! Wherever a Python policy iterates an action surface (a `set`), the Rust +//! port iterates it in node-id order: `HashSet` order is random per +//! process, so anything else would make seeded runs irreproducible. See +//! `PORTING_NOTES.md` §12. + +use std::fmt; + +use maltoolbox_attackgraph::AttackGraphNodeId; +use rand::rngs::StdRng; +use rand::SeedableRng; +use serde_json::{Map, Value}; + +use crate::agent_state::{AgentState, AgentStateError}; + +pub mod heuristic_agent; +pub mod keyboard_input; +pub mod passive_agent; +pub mod path_finding; +pub mod random_agent; +pub mod searchers; +pub mod shortest_path; +pub mod ttc_soft_min; + +pub use heuristic_agent::{DefendCompromisedDefender, DefendFutureCompromisedDefender}; +pub use keyboard_input::KeyboardAgent; +pub use passive_agent::PassiveAgent; +pub use path_finding::get_shortest_path_to; +pub use random_agent::RandomAgent; +pub use searchers::{BreadthFirstAttacker, DepthFirstAttacker}; +pub use shortest_path::ShortestPathAttacker; +pub use ttc_soft_min::TTCSoftMinAttacker; + +#[derive(Debug)] +pub enum PolicyError { + /// Python's `LookupError` for a name outside `policy_name_to_class`. + UnknownPolicy(String), + /// A value in the agent's `config` has the wrong type or is not one of + /// the allowed values. + InvalidConfig { + field: &'static str, + message: String, + }, + /// An attacker-only policy was handed a defender state, or the reverse. + WrongAgentType { + policy: &'static str, + expected: &'static str, + }, + /// `ShortestPathAttacker`'s assert that the attacker has goals. + NoGoals, + /// `TTCSoftMinAttacker` could not turn its TTCs into sampling weights. + InvalidWeights(String), + AgentState(AgentStateError), + /// `KeyboardAgent` could not read from or write to its terminal. + Io(std::io::Error), +} + +impl fmt::Display for PolicyError { + fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { + match self { + PolicyError::UnknownPolicy(name) => write!( + f, + "Policy class '{name}' not supported. Must be one of: {POLICY_NAMES:?}" + ), + PolicyError::InvalidConfig { field, message } => { + write!(f, "invalid agent config '{field}': {message}") + } + PolicyError::WrongAgentType { policy, expected } => { + write!(f, "{policy} can only be used by {expected} agents") + } + PolicyError::NoGoals => { + write!(f, "Attacker needs goal set for shortest path calculation") + } + PolicyError::InvalidWeights(message) => { + write!(f, "could not sample from TTC weights: {message}") + } + PolicyError::AgentState(e) => write!(f, "{e}"), + PolicyError::Io(e) => write!(f, "{e}"), + } + } +} + +impl std::error::Error for PolicyError { + fn source(&self) -> Option<&(dyn std::error::Error + 'static)> { + match self { + PolicyError::AgentState(e) => Some(e), + PolicyError::Io(e) => Some(e), + _ => None, + } + } +} + +impl From for PolicyError { + fn from(e: AgentStateError) -> Self { + PolicyError::AgentState(e) + } +} + +impl From for PolicyError { + fn from(e: std::io::Error) -> Self { + PolicyError::Io(e) + } +} + +/// Port of `DecisionAgent`: a heuristic agent. +pub trait DecisionAgent { + /// Select the next action the agent will work with: the selected node, + /// or `None` if there are no actions to select from (or the agent + /// chooses to wait). + fn get_next_action( + &mut self, + agent_state: &AgentState<'_>, + ) -> Result, PolicyError>; +} + +/// The keys of Python's `policy_name_to_class`. +pub const POLICY_NAMES: [&str; 9] = [ + "KeyboardAgent", + "PassiveAgent", + "DepthFirstAttacker", + "BreadthFirstAttacker", + "TTCSoftMinAttacker", + "ShortestPathAttacker", + "DefendCompromisedDefender", + "DefendFutureCompromisedDefender", + "RandomAgent", +]; + +/// Port of `policy_name_to_class[policy_name](agent_config)`: instantiates +/// the built-in policy a scenario's `policy` field names, with the agent's +/// `config`. +pub fn policy_from_name( + policy_name: &str, + agent_config: &Map, +) -> Result, PolicyError> { + Ok(match policy_name { + "KeyboardAgent" => Box::new(KeyboardAgent::new()), + "PassiveAgent" => Box::new(PassiveAgent), + "DepthFirstAttacker" => Box::new(DepthFirstAttacker::new(agent_config)?), + "BreadthFirstAttacker" => Box::new(BreadthFirstAttacker::new(agent_config)?), + "TTCSoftMinAttacker" => Box::new(TTCSoftMinAttacker::new(agent_config)?), + "ShortestPathAttacker" => Box::new(ShortestPathAttacker::new(agent_config)?), + "DefendCompromisedDefender" => Box::new(DefendCompromisedDefender::new(agent_config)), + "DefendFutureCompromisedDefender" => { + Box::new(DefendFutureCompromisedDefender::new(agent_config)) + } + "RandomAgent" => Box::new(RandomAgent::new(agent_config)?), + other => return Err(PolicyError::UnknownPolicy(other.to_string())), + }) +} + +/// `random.Random(agent_config.get('seed'))`: seeded when `seed` is an +/// integer, from OS entropy when it is absent or `null`. +pub(crate) fn rng_from_config(agent_config: &Map) -> Result { + match agent_config.get("seed") { + None | Some(Value::Null) => Ok(StdRng::from_rng(&mut rand::rng())), + Some(seed) => match seed.as_i64() { + // Negative seeds are legal in Python; reinterpret the bits. + Some(seed) => Ok(StdRng::seed_from_u64(seed as u64)), + None => Err(PolicyError::InvalidConfig { + field: "seed", + message: format!("Seed must be an integer or None, got {seed}"), + }), + }, + } +} + +/// `agent_config.get(field, default)` for a float setting. +pub(crate) fn f64_from_config( + agent_config: &Map, + field: &'static str, + default: f64, +) -> Result { + match agent_config.get(field) { + None => Ok(default), + Some(value) => value.as_f64().ok_or_else(|| PolicyError::InvalidConfig { + field, + message: format!("expected a number, got {value}"), + }), + } +} + +#[cfg(test)] +mod tests; diff --git a/core/malsim-core/src/policies/passive_agent.rs b/core/malsim-core/src/policies/passive_agent.rs new file mode 100644 index 00000000..9eb60fe5 --- /dev/null +++ b/core/malsim-core/src/policies/passive_agent.rs @@ -0,0 +1,20 @@ +//! Port of `passive_agent.py`: a passive agent that always chooses to do +//! nothing. + +use maltoolbox_attackgraph::AttackGraphNodeId; + +use crate::agent_state::AgentState; +use crate::policies::{DecisionAgent, PolicyError}; + +#[derive(Debug, Clone, Copy, Default)] +pub struct PassiveAgent; + +impl DecisionAgent for PassiveAgent { + fn get_next_action( + &mut self, + _agent_state: &AgentState<'_>, + ) -> Result, PolicyError> { + // A passive agent never does anything + Ok(None) + } +} diff --git a/core/malsim-core/src/policies/path_finding.rs b/core/malsim-core/src/policies/path_finding.rs new file mode 100644 index 00000000..7118e23f --- /dev/null +++ b/core/malsim-core/src/policies/path_finding.rs @@ -0,0 +1,183 @@ +//! Port of `utils/path_finding.py`. +//! +//! Note: Experimental, not proven correct +//! +//! Python converts the attack graph to a networkx graph and runs one +//! shortest-path search per compromised node. Here a single Dijkstra +//! search runs backwards from the goal, which yields the same cheapest +//! path for every start at once. See `PORTING_NOTES.md` §12 for this and +//! for the unreachable AND-step validation that is not ported. + +use crate::types::{NodeMap, NodeSet}; +use std::cmp::Ordering; +use std::collections::BinaryHeap; + +use maltoolbox_attackgraph::{AttackGraph, AttackGraphNodeId}; +use maltoolbox_language::graph::attack_step::AttackStepType; + +use crate::agent_state::{sorted_by_id, AgentStateError, AttackerState}; +use crate::policies::PolicyError; + +/// A node on the search frontier, ordered so that `BinaryHeap` pops the +/// lowest cost first and breaks ties on the lowest node id. +struct Frontier { + cost: f64, + id: i64, + node: AttackGraphNodeId, +} + +impl PartialEq for Frontier { + fn eq(&self, other: &Self) -> bool { + self.cmp(other) == Ordering::Equal + } +} + +impl Eq for Frontier {} + +impl PartialOrd for Frontier { + fn partial_cmp(&self, other: &Self) -> Option { + Some(self.cmp(other)) + } +} + +impl Ord for Frontier { + fn cmp(&self, other: &Self) -> Ordering { + other + .cost + .total_cmp(&self.cost) + .then_with(|| other.id.cmp(&self.id)) + } +} + +/// `ttc_values[node]`, with Python's `KeyError` as an error. +fn ttc_value( + attack_graph: &AttackGraph, + ttc_values: &NodeMap, + node: AttackGraphNodeId, +) -> Result { + ttc_values.get(&node).copied().ok_or_else(|| { + AgentStateError::MissingTtcValue(attack_graph.full_name_of(node, None)).into() + }) +} + +/// For every node that can reach `goal_node`: the cost of its cheapest +/// path there (the TTCs of every node on the path except the node itself) +/// and the next node on that path. An edge parent -> child weighs the +/// child's TTC, as in `attack_graph_to_nx_graph`. +fn paths_to_goal( + attack_graph: &AttackGraph, + goal_node: AttackGraphNodeId, + ttc_values: &NodeMap, +) -> Result)>, PolicyError> { + let mut best: NodeMap<(f64, Option)> = NodeMap::default(); + let mut done: NodeSet = NodeSet::default(); + let mut frontier = BinaryHeap::new(); + + let Some(goal) = attack_graph.nodes.get(goal_node) else { + // `nx.NodeNotFound` + return Ok(best); + }; + best.insert(goal_node, (0.0, None)); + frontier.push(Frontier { + cost: 0.0, + id: goal.id, + node: goal_node, + }); + + while let Some(Frontier { cost, node, .. }) = frontier.pop() { + if !done.insert(node) { + continue; + } + let parents = sorted_by_id( + attack_graph, + attack_graph.nodes[node].parents.iter().copied(), + ); + if parents.is_empty() { + continue; + } + let parent_cost = cost + ttc_value(attack_graph, ttc_values, node)?; + for parent in parents { + let improves = best + .get(&parent) + .is_none_or(|&(known, _)| parent_cost < known); + if improves && !done.contains(&parent) { + best.insert(parent, (parent_cost, Some(node))); + frontier.push(Frontier { + cost: parent_cost, + id: attack_graph.nodes[parent].id, + node: parent, + }); + } + } + } + Ok(best) +} + +/// Find shortest valid attack path from compromised nodes to goal: the +/// steps still to perform, in order, ending with `goal_node`. Empty when +/// the goal can't be reached (or is already compromised). +pub fn get_shortest_path_to( + attack_graph: &AttackGraph, + compromised_nodes: &[AttackGraphNodeId], + goal_node: AttackGraphNodeId, + ttc_values: &NodeMap, +) -> Result, PolicyError> { + let best = paths_to_goal(attack_graph, goal_node, ttc_values)?; + + // The cheapest path over all starts; the cost of a path includes its + // start node (`sum(ttc_values[n] for n in path_of_nodes)`). + let mut cheapest: Option<(f64, AttackGraphNodeId)> = None; + for start in sorted_by_id(attack_graph, compromised_nodes.iter().copied()) { + let Some(&(cost_after_start, _)) = best.get(&start) else { + continue; + }; + let path_cost = cost_after_start + ttc_value(attack_graph, ttc_values, start)?; + if cheapest.is_none_or(|(cost, _)| path_cost < cost) { + cheapest = Some((path_cost, start)); + } + } + let Some((_, start)) = cheapest else { + return Ok(Vec::new()); + }; + + let compromised_nodes: NodeSet = compromised_nodes.iter().copied().collect(); + let mut planned_path = Vec::new(); + let mut step = Some(start); + while let Some(node) = step { + if !compromised_nodes.contains(&node) { + planned_path.push(node); + } + step = best[&node].1; + } + Ok(planned_path) +} + +/// Return shortest path for each of the attackers goals, in node-id order +/// of the goals. +pub fn get_shortest_paths_for_attacker( + attacker_state: &AttackerState<'_>, +) -> Result)>, PolicyError> { + let attack_graph = attacker_state.attack_graph; + let ttc_values = attack_graph + .nodes + .iter() + .filter(|(_, n)| matches!(n.step_type, AttackStepType::Or | AttackStepType::And)) + .map(|(key, _)| Ok((key, attacker_state.node_ttc_value(key)?))) + .collect::, PolicyError>>()?; + + let goals = &attacker_state.runtime.goals; + if goals.is_empty() { + return Err(PolicyError::NoGoals); + } + let performed_nodes = sorted_by_id( + attack_graph, + attacker_state.runtime.performed_nodes.iter().copied(), + ); + sorted_by_id(attack_graph, goals.iter().copied()) + .into_iter() + .map(|goal| { + let path = get_shortest_path_to(attack_graph, &performed_nodes, goal, &ttc_values)?; + Ok((goal, path)) + }) + .collect() +} diff --git a/core/malsim-core/src/policies/random_agent.rs b/core/malsim-core/src/policies/random_agent.rs new file mode 100644 index 00000000..b0ed7518 --- /dev/null +++ b/core/malsim-core/src/policies/random_agent.rs @@ -0,0 +1,40 @@ +//! Port of `random_agent.py`. + +use maltoolbox_attackgraph::AttackGraphNodeId; +use rand::rngs::StdRng; +use rand::seq::IndexedRandom; +use rand::RngExt; +use serde_json::{Map, Value}; + +use crate::agent_state::AgentState; +use crate::policies::{f64_from_config, rng_from_config, DecisionAgent, PolicyError}; + +/// An agent that selects random actions. +#[derive(Debug)] +pub struct RandomAgent { + rng: StdRng, + wait_prob: f64, +} + +impl RandomAgent { + pub fn new(agent_config: &Map) -> Result { + Ok(RandomAgent { + rng: rng_from_config(agent_config)?, + wait_prob: f64_from_config(agent_config, "wait_prob", 0.0)?, + }) + } +} + +impl DecisionAgent for RandomAgent { + /// Return a random node from the action surface. + fn get_next_action( + &mut self, + agent_state: &AgentState<'_>, + ) -> Result, PolicyError> { + let possible_choices = agent_state.sorted_action_surface(); + if possible_choices.is_empty() || self.rng.random::() < self.wait_prob { + return Ok(None); + } + Ok(possible_choices.choose(&mut self.rng).copied()) + } +} diff --git a/core/malsim-core/src/policies/searchers.rs b/core/malsim-core/src/policies/searchers.rs new file mode 100644 index 00000000..e70a81db --- /dev/null +++ b/core/malsim-core/src/policies/searchers.rs @@ -0,0 +1,210 @@ +//! Port of `attackers/searchers.py`: breadth-first and depth-first agents. + +use crate::types::NodeSet; +use std::collections::VecDeque; + +use maltoolbox_attackgraph::{AttackGraph, AttackGraphNodeId}; +use rand::rngs::StdRng; +use rand::seq::SliceRandom; +use serde_json::{Map, Value}; + +use crate::agent_state::{sorted_by_id, AgentState}; +use crate::policies::{rng_from_config, DecisionAgent, PolicyError}; + +/// How new targets are ordered before they are queued. +#[derive(Debug, Clone, Copy, PartialEq, Eq, Default)] +pub enum ActionOrdering { + /// Randomize the order of new targets at each step. + Random, + /// Sort by node id. + Sorted, + /// Python relies on the action surface set's built-in order here. A + /// Rust `HashSet` has no stable one, so this sorts by node id. + #[default] + Nothing, + /// Python shuffles without sorting first. Without a stable set order + /// that is no different from `Random`, which is what this does. + ListRandom, +} + +impl ActionOrdering { + /// Parses the Python enum value (`ActionOrdering(value)`). + pub fn from_value(value: &str) -> Option { + match value { + "random" => Some(ActionOrdering::Random), + "sorted" => Some(ActionOrdering::Sorted), + "nothing" => Some(ActionOrdering::Nothing), + "list_random" => Some(ActionOrdering::ListRandom), + _ => None, + } + } +} + +/// Port of `parse_agent_config`'s `action_ordering` half (the seed is +/// handled by `rng_from_config`). +fn parse_action_ordering(agent_config: &Map) -> Result { + match agent_config.get("action_ordering") { + None => Ok(ActionOrdering::Nothing), + Some(value) => value + .as_str() + .and_then(ActionOrdering::from_value) + .ok_or_else(|| PolicyError::InvalidConfig { + field: "action_ordering", + message: format!( + "Invalid action_ordering: {value}. Valid options are: \ + [\"random\", \"sorted\", \"nothing\", \"list_random\"]" + ), + }), + } +} + +/// Where `extend_method` puts new targets: the two searchers differ only +/// in this. +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +enum SearchOrder { + BreadthFirst, + DepthFirst, +} + +/// The state both searchers share (Python: `DepthFirstAttacker` subclasses +/// `BreadthFirstAttacker`). +#[derive(Debug)] +struct Searcher { + search_order: SearchOrder, + action_ordering: ActionOrdering, + rng: StdRng, + targets: VecDeque, + current_target: Option, + /// `_prev_state.action_surface`; `None` before the first call. + prev_action_surface: Option, +} + +impl Searcher { + fn new( + search_order: SearchOrder, + agent_config: &Map, + ) -> Result { + Ok(Searcher { + search_order, + action_ordering: parse_action_ordering(agent_config)?, + rng: rng_from_config(agent_config)?, + targets: VecDeque::new(), + current_target: None, + prev_action_surface: None, + }) + } + + /// Port of `order_func`. + fn order( + &mut self, + attack_graph: &AttackGraph, + nodes: impl IntoIterator, + ) -> Vec { + // sort to ensure deterministic order before shuffling + let mut nodes = sorted_by_id(attack_graph, nodes); + match self.action_ordering { + ActionOrdering::Random | ActionOrdering::ListRandom => nodes.shuffle(&mut self.rng), + ActionOrdering::Sorted | ActionOrdering::Nothing => {} + } + nodes + } + + /// Port of `extend_method`: extend the deque of targets with new nodes + /// according to the policy of the agent (BFS or DFS). + fn extend_method(&mut self, new_nodes: Vec) { + match self.search_order { + // `targets.extendleft(new_nodes)` + SearchOrder::BreadthFirst => { + for node in new_nodes { + self.targets.push_front(node); + } + } + // `targets.extend(new_nodes)` + SearchOrder::DepthFirst => self.targets.extend(new_nodes), + } + } + + /// Port of `_update_targets`. + fn update_targets(&mut self, new_targets: Vec, disabled_nodes: &NodeSet) { + if let Some(current_target) = self.current_target { + if !disabled_nodes.contains(¤t_target) { + // If the current target was not compromised, e.g. due to + // TTCs, it remains in action surface and should be added as + // a target. + self.targets.push_back(current_target); + } + } + + // Enabled defenses may remove previously possible attack steps. + if !disabled_nodes.is_empty() { + self.targets.retain(|n| !disabled_nodes.contains(n)); + } + + // Use the extend method to add new targets + self.extend_method(new_targets); + } + + /// Receive the next action according to agent policy (bfs/dfs). + fn get_next_action(&mut self, agent_state: &AgentState<'_>) -> Option { + let action_surface = agent_state.action_surface(); + let (new_nodes, disabled_nodes): (Vec<_>, NodeSet) = match &self.prev_action_surface { + Some(prev) => ( + action_surface.difference(prev).copied().collect(), + prev.difference(action_surface).copied().collect(), + ), + None => (action_surface.iter().copied().collect(), NodeSet::default()), + }; + + let new_targets = self.order(agent_state.attack_graph(), new_nodes); + self.update_targets(new_targets, &disabled_nodes); + + // `_select_next_target` + self.current_target = self.targets.pop_back(); + self.prev_action_surface = Some(action_surface.clone()); + self.current_target + } +} + +/// A Breadth-First agent, with possible randomization at each level. +#[derive(Debug)] +pub struct BreadthFirstAttacker(Searcher); + +impl BreadthFirstAttacker { + /// A human-friendly name for the agent. + pub const NAME: &'static str = "Breadth First Attacker"; + + pub fn new(agent_config: &Map) -> Result { + Searcher::new(SearchOrder::BreadthFirst, agent_config).map(BreadthFirstAttacker) + } +} + +impl DecisionAgent for BreadthFirstAttacker { + fn get_next_action( + &mut self, + agent_state: &AgentState<'_>, + ) -> Result, PolicyError> { + Ok(self.0.get_next_action(agent_state)) + } +} + +/// A Depth-First agent, with possible randomization at each level. +#[derive(Debug)] +pub struct DepthFirstAttacker(Searcher); + +impl DepthFirstAttacker { + /// A human-friendly name for the agent. + pub const NAME: &'static str = "Depth First Attacker"; + + pub fn new(agent_config: &Map) -> Result { + Searcher::new(SearchOrder::DepthFirst, agent_config).map(DepthFirstAttacker) + } +} + +impl DecisionAgent for DepthFirstAttacker { + fn get_next_action( + &mut self, + agent_state: &AgentState<'_>, + ) -> Result, PolicyError> { + Ok(self.0.get_next_action(agent_state)) + } +} diff --git a/core/malsim-core/src/policies/shortest_path.rs b/core/malsim-core/src/policies/shortest_path.rs new file mode 100644 index 00000000..88ad83e7 --- /dev/null +++ b/core/malsim-core/src/policies/shortest_path.rs @@ -0,0 +1,50 @@ +//! Port of `attackers/shortest_path.py`. + +use maltoolbox_attackgraph::AttackGraphNodeId; +use rand::rngs::StdRng; +use rand::seq::IndexedRandom; +use serde_json::{Map, Value}; + +use crate::agent_state::AgentState; +use crate::policies::path_finding::get_shortest_paths_for_attacker; +use crate::policies::{rng_from_config, DecisionAgent, PolicyError}; + +/// An agent that finds shortest path in respect to TTC. +/// Requires that the agent has goal set in agent state, +/// and that TTCMode is PRE_SAMPLE or EXPECTED_VALUE. +/// +/// Note: Experimental, not proven correct +#[derive(Debug)] +pub struct ShortestPathAttacker { + rng: StdRng, +} + +impl ShortestPathAttacker { + pub fn new(agent_config: &Map) -> Result { + println!("Note: ShortestPathAttacker is in experimental mode"); + Ok(ShortestPathAttacker { + rng: rng_from_config(agent_config)?, + }) + } +} + +impl DecisionAgent for ShortestPathAttacker { + /// The first step of the shortest path to a randomly chosen goal. + fn get_next_action( + &mut self, + agent_state: &AgentState<'_>, + ) -> Result, PolicyError> { + let AgentState::Attacker(attacker_state) = agent_state else { + return Err(PolicyError::WrongAgentType { + policy: "ShortestPathAttacker", + expected: "attacker", + }); + }; + + let shortest_paths_per_goal = get_shortest_paths_for_attacker(attacker_state)?; + let shortest_path_random_goal = shortest_paths_per_goal + .choose(&mut self.rng) + .map(|(_goal, path)| path); + Ok(shortest_path_random_goal.and_then(|path| path.first().copied())) + } +} diff --git a/core/malsim-core/src/policies/tests.rs b/core/malsim-core/src/policies/tests.rs new file mode 100644 index 00000000..91ca63ab --- /dev/null +++ b/core/malsim-core/src/policies/tests.rs @@ -0,0 +1,552 @@ +//! Rust ports of `tests/agents/test_searchers.py` and the hand-built-graph +//! tests of `tests/agents/test_agents.py`. The scenario-file tests of that +//! directory are in `core/malsim-core/tests/run_simulation.rs`. + +use std::cell::RefCell; +use std::collections::{BTreeSet, HashMap}; +use std::rc::Rc; + +use maltoolbox_attackgraph::{AttackGraph, AttackGraphNodeId}; +use maltoolbox_model::Model; +use rand::rngs::StdRng; +use rand::SeedableRng; +use serde_json::{json, Map, Value}; + +use super::*; +use crate::agent_state::AgentState; +use crate::scenario::agent_settings::{ + AgentSettings, AttackerSettings, DefenderSettings, EntryPoints, +}; +use crate::scenario::flatten::{ + flatten_attacker_settings, flatten_defender_settings, get_entry_points, +}; +use crate::scenario::node_property_rule::NodePropertyRule; +use crate::settings::{FlatAgentSettings, MalSimulatorSettings, RewardMode}; +use crate::simulator::{Simulator, StepOutcome}; +use crate::test_fixtures::dummy_graph_and_model; + +/// A `MalSimulator` stand-in for a hand-built graph: a `Simulator` plus +/// the agent settings and last step outcome agent states are built from. +struct Sim { + graph: Rc>, + model: Model, + settings: Vec>, + sim: Simulator, + outcome: StepOutcome, +} + +impl Sim { + fn new( + graph: AttackGraph, + model: Model, + settings: Vec>, + ) -> Self { + let graph = Rc::new(RefCell::new(graph)); + let mut sim = Sim { + sim: Simulator::new(graph.clone()), + graph, + model, + settings, + outcome: StepOutcome { + attackers: Default::default(), + defenders: Default::default(), + step_enabled_defenses: Default::default(), + }, + }; + sim.reset(); + sim + } + + fn reset(&mut self) { + let graph = self.graph.borrow(); + let mut rng = StdRng::seed_from_u64(0); + let agents = self + .settings + .iter() + .map(|settings| { + let flat = match settings { + AgentSettings::Attacker(a) => { + FlatAgentSettings::Attacker(flatten_attacker_settings( + a, + &graph, + &self.model, + &get_entry_points(a, &mut rng), + )) + } + AgentSettings::Defender(d) => FlatAgentSettings::Defender( + flatten_defender_settings(d, &graph, &self.model), + ), + }; + (settings.name().to_string(), flat) + }) + .collect(); + drop(graph); + let state = self + .sim + .reset(&MalSimulatorSettings::default(), agents, 0) + .unwrap(); + self.outcome = StepOutcome::from_reset(state); + } + + fn next_action( + &self, + name: &str, + agent: &mut dyn DecisionAgent, + ) -> Result, PolicyError> { + let graph = self.graph.borrow(); + let settings = self.settings.iter().find(|s| s.name() == name).unwrap(); + let agent_state = AgentState::new( + settings, + &graph, + &self.model, + self.sim.state().unwrap(), + &self.outcome, + ) + .unwrap(); + agent.get_next_action(&agent_state) + } + + /// Steps `name` on `action` and returns the ids of the nodes that + /// step compromised. + fn step(&mut self, name: &str, action: AttackGraphNodeId) -> Vec { + self.outcome = self + .sim + .step(&HashMap::from([(name.to_string(), vec![action])])) + .unwrap(); + let graph = self.graph.borrow(); + let mut ids: Vec = self.outcome.attackers[name] + .step_performed_nodes + .iter() + .map(|&n| graph.nodes[n].id) + .collect(); + ids.sort_unstable(); + ids + } + + fn id(&self, node: AttackGraphNodeId) -> i64 { + self.graph.borrow().nodes[node].id + } +} + +/// Adds a node of the `DummyAsset` attack step `step_name`, backed by a +/// new model asset named `asset`. +fn add_node(graph: &mut AttackGraph, model: &mut Model, step_name: &str) -> AttackGraphNodeId { + let asset_id = graph.lang_graph.asset_id("DummyAsset").unwrap(); + let step_id = graph.lang_graph.assets[asset_id].attack_steps[step_name]; + let name = format!("asset{}", graph.nodes.len()); + let model_asset = model + .add_asset("DummyAsset", Some(name), None, None, None, false) + .unwrap(); + graph + .add_node( + step_id, + Some(model), + None, + Some(model_asset), + None, + None, + None, + ) + .unwrap() +} + +/// Port of `tests/conftest.py::connect_nodes`. +fn connect_nodes(graph: &mut AttackGraph, parent: AttackGraphNodeId, child: AttackGraphNodeId) { + graph.nodes[parent].children.insert(child); + graph.nodes[child].parents.insert(parent); +} + +fn attacker(name: &str, entry_point: AttackGraphNodeId) -> AgentSettings { + AgentSettings::Attacker(AttackerSettings { + name: name.to_string(), + entry_points: EntryPoints::Single(BTreeSet::from([entry_point])), + policy: None, + actionable_steps: None, + rewards: None, + config: Map::new(), + reward_mode: RewardMode::Cumulative, + goals: BTreeSet::new(), + ttc_dists: None, + }) +} + +fn defender(name: &str, rewards: Option) -> AgentSettings { + AgentSettings::Defender(DefenderSettings { + name: name.to_string(), + policy: None, + observable_steps: None, + actionable_steps: None, + rewards: rewards.map(|r| NodePropertyRule::from_value(&r).unwrap()), + false_positive_rates: None, + false_negative_rates: None, + config: Map::new(), + reward_mode: RewardMode::Cumulative, + }) +} + +fn config(value: Value) -> Map { + value.as_object().unwrap().clone() +} + +/// `num_nodes` OR nodes connected by `edges` (pairs of node indexes), with +/// an attacker named `name` entering at node 0. +fn or_graph_sim(name: &str, num_nodes: usize, edges: &[(usize, usize)]) -> Sim { + let (mut graph, mut model) = dummy_graph_and_model(); + let nodes: Vec<_> = (0..num_nodes) + .map(|_| add_node(&mut graph, &mut model, "DummyOrAttackStep")) + .collect(); + for &(parent, child) in edges { + connect_nodes(&mut graph, nodes[parent], nodes[child]); + } + let settings = vec![attacker(name, nodes[0])]; + Sim::new(graph, model, settings) +} + +/// ```text +/// node0 ______________ +/// / \ \ +/// node1 node2 node7 +/// / \ / \ +/// node3 node4 node5 node6 +/// ``` +const COMPLICATED: [(usize, usize); 7] = [(0, 1), (0, 2), (0, 7), (1, 3), (1, 4), (2, 5), (2, 6)]; + +/// Lets `agent` pick `steps` actions, stepping on each, and returns the +/// compromised node ids in order. +fn traversal_order( + sim: &mut Sim, + name: &str, + agent: &mut dyn DecisionAgent, + steps: usize, +) -> Vec { + let mut actual_order = Vec::new(); + for _ in 0..steps { + let action_node = sim.next_action(name, agent).unwrap().expect("an action"); + let performed = sim.step(name, action_node); + assert_eq!(performed.len(), 1); + actual_order.push(performed[0]); + } + actual_order +} + +#[test] +fn breadth_first_traversal_simple() { + // node0 -> node1 -> node2 -> node3 + let mut sim = or_graph_sim("bfs", 4, &[(0, 1), (1, 2), (2, 3)]); + let mut attacker_ai = + BreadthFirstAttacker::new(&config(json!({"seed": 42, "randomize": false}))).unwrap(); + + assert_eq!( + traversal_order(&mut sim, "bfs", &mut attacker_ai, 3), + [1, 2, 3] + ); + // Nothing left to do. + assert_eq!(sim.next_action("bfs", &mut attacker_ai).unwrap(), None); +} + +#[test] +fn breadth_first_traversal_complicated() { + for action_ordering in ["nothing", "sorted", "random", "list_random"] { + let mut sim = or_graph_sim("bfs", 8, &COMPLICATED); + let mut attacker_ai = BreadthFirstAttacker::new(&config( + json!({"seed": 42, "action_ordering": action_ordering}), + )) + .unwrap(); + + let mut actual_order = traversal_order(&mut sim, "bfs", &mut attacker_ai, 7); + if matches!(action_ordering, "nothing" | "sorted") { + assert_eq!(actual_order, [1, 2, 7, 3, 4, 5, 6]); + } + // Each level is exhausted before the next one starts. + actual_order[..3].sort_unstable(); + actual_order[3..].sort_unstable(); + assert_eq!(actual_order, [1, 2, 7, 3, 4, 5, 6], "{action_ordering}"); + } +} + +#[test] +fn depth_first_traversal_complicated() { + for action_ordering in ["nothing", "sorted", "random", "list_random"] { + let mut sim = or_graph_sim("dfs", 8, &COMPLICATED); + let mut attacker_ai = DepthFirstAttacker::new(&config( + json!({"seed": 42, "action_ordering": action_ordering}), + )) + .unwrap(); + + let actual_order = traversal_order(&mut sim, "dfs", &mut attacker_ai, 7); + + // All children of 1 must come directly after it + let index1 = actual_order.iter().position(|&id| id == 1).unwrap(); + let mut children = actual_order[index1 + 1..index1 + 3].to_vec(); + children.sort_unstable(); + assert_eq!(children, [3, 4], "{action_ordering}"); + + // All children of 2 must come directly after it + let index2 = actual_order.iter().position(|&id| id == 2).unwrap(); + let mut children = actual_order[index2 + 1..index2 + 3].to_vec(); + children.sort_unstable(); + assert_eq!(children, [5, 6], "{action_ordering}"); + } +} + +#[test] +fn searcher_seeded_random_ordering_is_reproducible() { + let order = || { + let mut sim = or_graph_sim("bfs", 8, &COMPLICATED); + let mut attacker_ai = + BreadthFirstAttacker::new(&config(json!({"seed": 7, "action_ordering": "random"}))) + .unwrap(); + traversal_order(&mut sim, "bfs", &mut attacker_ai, 7) + }; + assert_eq!(order(), order()); +} + +#[test] +fn searcher_rejects_invalid_config() { + assert!(matches!( + BreadthFirstAttacker::new(&config(json!({"action_ordering": "alphabetical"}))), + Err(PolicyError::InvalidConfig { + field: "action_ordering", + .. + }) + )); + assert!(matches!( + DepthFirstAttacker::new(&config(json!({"seed": "one"}))), + Err(PolicyError::InvalidConfig { field: "seed", .. }) + )); +} + +/// ```text +/// node0 -------+---------| +/// | node1 | node2 | +/// | / \ | / \ | +/// node3 node4 node5 +/// ``` +/// node1/node2 are defenses, the rest AND steps; the attacker enters at +/// node4. `rewards` are the defender's. +fn defend_compromised_sim(rewards: Option) -> (Sim, Vec) { + let (mut graph, mut model) = dummy_graph_and_model(); + let steps = ["And", "Defense", "Defense", "And", "And", "And"]; + let nodes: Vec<_> = steps + .iter() + .map(|step| add_node(&mut graph, &mut model, &format!("Dummy{step}AttackStep"))) + .collect(); + for (parent, child) in [(0, 3), (0, 4), (0, 5), (1, 3), (1, 4), (2, 4), (2, 5)] { + connect_nodes(&mut graph, nodes[parent], nodes[child]); + } + let settings = vec![attacker("bfs", nodes[4]), defender("def_comp", rewards)]; + (Sim::new(graph, model, settings), nodes) +} + +fn defense_costs(node1: f64, node2: f64) -> Option { + Some(json!({"by_asset_name": { + "asset1": {"DummyDefenseAttackStep": node1}, + "asset2": {"DummyDefenseAttackStep": node2}, + }})) +} + +#[test] +fn defend_compromised_defender() { + let agent_config = config(json!({"seed": 42, "randomize": false})); + + // Both defenses have the compromised node4 as a child: without + // rewards they cost the same and the lowest node id wins. + let (sim, nodes) = defend_compromised_sim(None); + let mut defender_ai = DefendCompromisedDefender::new(&agent_config); + let action_node = sim.next_action("def_comp", &mut defender_ai).unwrap(); + assert_eq!(action_node, Some(nodes[1])); + assert_eq!(sim.id(nodes[1]), 1); + + // Should pick cheapest one + let (sim, nodes) = defend_compromised_sim(defense_costs(100.0, 10.0)); + let mut defender_ai = DefendCompromisedDefender::new(&agent_config); + let action_node = sim.next_action("def_comp", &mut defender_ai).unwrap(); + assert_eq!(action_node, Some(nodes[2])); + + let (sim, nodes) = defend_compromised_sim(defense_costs(10.0, 100.0)); + let mut defender_ai = DefendCompromisedDefender::new(&agent_config); + let action_node = sim.next_action("def_comp", &mut defender_ai).unwrap(); + assert_eq!(action_node, Some(nodes[1])); +} + +#[test] +fn defend_future_compromised_defender() { + // node0 -------+-----------| + // | node1 | node2 | + // | / \ | / \ | + // node3 node4 | node5 + // | | + // \ / + // node 6 + let (mut graph, mut model) = dummy_graph_and_model(); + let steps = ["And", "Defense", "Defense", "And", "And", "And", "And"]; + let nodes: Vec<_> = steps + .iter() + .map(|step| add_node(&mut graph, &mut model, &format!("Dummy{step}AttackStep"))) + .collect(); + for (parent, child) in [ + (0, 3), + (0, 4), + (0, 5), + (1, 3), + (1, 4), + (2, 5), + (2, 6), + (4, 6), + ] { + connect_nodes(&mut graph, nodes[parent], nodes[child]); + } + let settings = vec![attacker("bfs", nodes[4]), defender("def_future_comp", None)]; + let sim = Sim::new(graph, model, settings); + + let mut defender_ai = + DefendFutureCompromisedDefender::new(&config(json!({"seed": 42, "randomize": false}))); + + // Should pick node 2 either way: only its child node6 is not + // compromised but has a compromised parent (node4). + let action_node = sim + .next_action("def_future_comp", &mut defender_ai) + .unwrap(); + assert_eq!(action_node, Some(nodes[2])); +} + +#[test] +fn defender_policies_reject_attacker_states() { + let (sim, _) = defend_compromised_sim(None); + let agent_config = Map::new(); + assert!(matches!( + sim.next_action("bfs", &mut DefendCompromisedDefender::new(&agent_config)), + Err(PolicyError::WrongAgentType { + expected: "defender", + .. + }) + )); + assert!(matches!( + sim.next_action( + "def_comp", + &mut TTCSoftMinAttacker::new(&agent_config).unwrap() + ), + Err(PolicyError::WrongAgentType { + expected: "attacker", + .. + }) + )); + assert!(matches!( + sim.next_action( + "def_comp", + &mut ShortestPathAttacker::new(&agent_config).unwrap() + ), + Err(PolicyError::WrongAgentType { + expected: "attacker", + .. + }) + )); +} + +#[test] +fn random_agent() { + // node0 connected to all other nodes + let mut sim = or_graph_sim("random_attacker", 4, &[(0, 1), (0, 2), (0, 3)]); + let attacker_name = "random_attacker"; + + let mut attacker_ai = RandomAgent::new(&config(json!({"seed": 0}))).unwrap(); + let mut counter: HashMap, u32> = HashMap::new(); + for _ in 0..1_500 { + sim.reset(); + let action_node = sim.next_action(attacker_name, &mut attacker_ai).unwrap(); + *counter.entry(action_node.map(|n| sim.id(n))).or_default() += 1; + } + assert_eq!(counter.len(), 3); + assert!(!counter.contains_key(&None)); + for &count in counter.values() { + let prob = f64::from(count) / 1_500.0; + assert!((prob - 1.0 / 3.0).abs() < 0.04, "{counter:?}"); + } + + let mut attacker_ai = RandomAgent::new(&config(json!({"seed": 0, "wait_prob": 0.05}))).unwrap(); + let mut waits = 0; + for _ in 0..1_500 { + sim.reset(); + if sim + .next_action(attacker_name, &mut attacker_ai) + .unwrap() + .is_none() + { + waits += 1; + } + } + let wait_prob = f64::from(waits) / 1_500.0; + assert!((wait_prob - 0.05).abs() < 0.04, "{wait_prob}"); +} + +#[test] +fn passive_agent_never_acts() { + let sim = or_graph_sim("passive", 2, &[(0, 1)]); + assert_eq!(sim.next_action("passive", &mut PassiveAgent).unwrap(), None); +} + +/// A `KeyboardAgent` fed `input`, with what it printed. +fn keyboard_action( + sim: &Sim, + input: &str, +) -> (Result, PolicyError>, String) { + let mut output = Vec::new(); + let mut agent = KeyboardAgent::with_io(input.as_bytes(), &mut output); + let action = sim.next_action("keyboard", &mut agent); + (action, String::from_utf8(output).unwrap()) +} + +#[test] +fn keyboard_agent_reads_an_action_index() { + let sim = or_graph_sim("keyboard", 4, &[(0, 1), (0, 2), (0, 3)]); + let surface = { + let graph = sim.graph.borrow(); + crate::agent_state::sorted_by_id( + &graph, + sim.sim.state().unwrap().attackers["keyboard"] + .action_surface + .iter() + .copied(), + ) + }; + assert_eq!(surface.len(), 3); + + let (action, output) = keyboard_action(&sim, "1\n"); + assert_eq!(action.unwrap(), Some(surface[1])); + assert!(output.contains("Available actions:")); + assert!(output.contains("0. asset1:DummyOrAttackStep"), "{output}"); + assert!(output.contains("Selected action: asset2:DummyOrAttackStep")); + + // An empty line waits. + let (action, output) = keyboard_action(&sim, "\n"); + assert_eq!(action.unwrap(), None); + assert!(output.ends_with("wait\n")); + + // Invalid entries (not a number, out of range) are asked again. + let (action, output) = keyboard_action(&sim, "x\n3\n-1\n2\n"); + assert_eq!(action.unwrap(), Some(surface[2])); + assert_eq!(output.matches("Invalid action.").count(), 3); + + // Running out of input is an error, not a wait. + let (action, _) = keyboard_action(&sim, ""); + assert!(matches!(action, Err(PolicyError::Io(_)))); +} + +#[test] +fn keyboard_agent_without_actions_does_not_prompt() { + let sim = or_graph_sim("keyboard", 1, &[]); + let (action, output) = keyboard_action(&sim, ""); + assert_eq!(action.unwrap(), None); + assert_eq!(output, "No actions to pick for defender\n"); +} + +#[test] +fn policy_from_name_knows_every_python_policy() { + for name in POLICY_NAMES { + assert!(policy_from_name(name, &Map::new()).is_ok(), "{name}"); + } + assert!(matches!( + policy_from_name("NotARealPolicy", &Map::new()), + Err(PolicyError::UnknownPolicy(name)) if name == "NotARealPolicy" + )); +} diff --git a/core/malsim-core/src/policies/ttc_soft_min.rs b/core/malsim-core/src/policies/ttc_soft_min.rs new file mode 100644 index 00000000..da790048 --- /dev/null +++ b/core/malsim-core/src/policies/ttc_soft_min.rs @@ -0,0 +1,64 @@ +//! Port of `attackers/ttc_soft_min.py`: use softargmax to prioritize low +//! TTC attack steps when running attacker. + +use maltoolbox_attackgraph::AttackGraphNodeId; +use rand::distr::weighted::WeightedIndex; +use rand::distr::Distribution; +use rand::rngs::StdRng; +use serde_json::{Map, Value}; + +use crate::agent_state::AgentState; +use crate::policies::{f64_from_config, rng_from_config, DecisionAgent, PolicyError}; + +/// An agent that samples its next step with a probability that falls off +/// with the TTC the step has left. +#[derive(Debug)] +pub struct TTCSoftMinAttacker { + rng: StdRng, + /// Sharpness of the softargmax. + beta: f64, +} + +impl TTCSoftMinAttacker { + pub fn new(agent_config: &Map) -> Result { + Ok(TTCSoftMinAttacker { + rng: rng_from_config(agent_config)?, + beta: f64_from_config(agent_config, "beta", 1.0)?, + }) + } +} + +impl DecisionAgent for TTCSoftMinAttacker { + /// Sample node from the action surface based on ttc softargmax. + fn get_next_action( + &mut self, + agent_state: &AgentState<'_>, + ) -> Result, PolicyError> { + let AgentState::Attacker(attacker_state) = agent_state else { + return Err(PolicyError::WrongAgentType { + policy: "TTCSoftMinAttacker", + expected: "attacker", + }); + }; + + let possible_choices = agent_state.sorted_action_surface(); + if possible_choices.is_empty() { + return Ok(None); + } + + let weights = possible_choices + .iter() + .map(|&n| { + let num_attempts = attacker_state.runtime.num_attempts.get(&n).copied(); + let ttc_left = attacker_state.node_ttc_value(n)? - num_attempts.unwrap_or(0) as f64; + Ok((-self.beta * ttc_left).exp()) + }) + .collect::, PolicyError>>()?; + + // `WeightedIndex` normalizes the weights itself. + let index = WeightedIndex::new(&weights) + .map_err(|e| PolicyError::InvalidWeights(e.to_string()))? + .sample(&mut self.rng); + Ok(Some(possible_choices[index])) + } +} diff --git a/core/malsim-core/src/rewards.rs b/core/malsim-core/src/rewards.rs new file mode 100644 index 00000000..f6406b8d --- /dev/null +++ b/core/malsim-core/src/rewards.rs @@ -0,0 +1,168 @@ +//! Port of `rewards.py` (plus `graph_utils.py::node_reward`): per-step +//! rewards for attackers and defenders. +//! +//! Python builds one reward closure per agent at simulator construction +//! (`attacker_step_reward_fn`/`defender_step_reward_fn`) and +//! `MalSimulator.agent_reward(state)` calls it. Here the agent state view +//! already carries the settings the closure captured, so these are plain +//! functions of the state. + +use crate::types::NodeSet; +use std::fmt; + +use maltoolbox_attackgraph::AttackGraphNode; +use maltoolbox_model::Model; +use rand::Rng; + +use crate::agent_state::{sorted_by_id, AgentState, AttackerState, DefenderState}; +use crate::graph_state::TtcMode; +use crate::scenario::node_property_rule::NodePropertyRule; +use crate::settings::RewardMode; +use crate::ttc::{TtcDist, TtcDistError}; + +#[derive(Debug, Clone, PartialEq)] +pub enum RewardError { + /// Python's `ENABLED_DEFENSES_FUNCS[reward_mode]` `KeyError`: defenders + /// only support `CUMULATIVE` and `ONE_OFF`. + UnsupportedDefenderRewardMode(RewardMode), + TtcDist(TtcDistError), +} + +impl fmt::Display for RewardError { + fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { + match self { + RewardError::UnsupportedDefenderRewardMode(mode) => write!( + f, + "reward mode {} is not supported for defenders", + mode.name() + ), + RewardError::TtcDist(e) => write!(f, "{e}"), + } + } +} + +impl std::error::Error for RewardError { + fn source(&self) -> Option<&(dyn std::error::Error + 'static)> { + match self { + RewardError::TtcDist(e) => Some(e), + RewardError::UnsupportedDefenderRewardMode(_) => None, + } + } +} + +impl From for RewardError { + fn from(e: TtcDistError) -> Self { + RewardError::TtcDist(e) + } +} + +/// Port of `node_reward`: the node's reward from the agent's rule, `0.0` +/// without a (non-empty) rule or a matching entry. +pub fn node_reward( + node: &AttackGraphNode, + reward_rule: Option<&NodePropertyRule>, + model: &Model, +) -> f64 { + match reward_rule { + Some(rule) if !rule.is_empty() => rule.value(node, model).unwrap_or(0.0), + _ => 0.0, + } +} + +/// Port of `defender_step_reward`: the defender is penalized for enabled +/// defenses and compromised steps - all of them for `CUMULATIVE`, this +/// step's for `ONE_OFF`. +pub fn defender_step_reward(defender_state: &DefenderState<'_>) -> Result { + let runtime = defender_state.runtime; + let (enabled_defenses, compromised_nodes) = match defender_state.settings.reward_mode { + RewardMode::Cumulative => (&runtime.performed_nodes, &runtime.compromised_nodes), + RewardMode::OneOff => ( + defender_state.step_performed_nodes, + &defender_state.step_compromised_nodes, + ), + mode => return Err(RewardError::UnsupportedDefenderRewardMode(mode)), + }; + + let nodes: NodeSet = enabled_defenses.union(compromised_nodes).copied().collect(); + let graph = defender_state.attack_graph; + let step_reward: f64 = sorted_by_id(graph, nodes) + .into_iter() + .map(|n| { + node_reward( + &graph.nodes[n], + defender_state.settings.rewards.as_ref(), + defender_state.model, + ) + }) + .sum(); + Ok(-step_reward) +} + +/// Port of `attacker_step_reward`: the attacker is rewarded for compromised +/// nodes (all of them for `CUMULATIVE`, otherwise this step's) and +/// penalized per attempt when TTCs are enabled, or by the node's TTC for +/// the `EXPECTED_TTC`/`SAMPLE_TTC` reward modes when they are disabled. +pub fn attacker_step_reward( + attacker_state: &AttackerState<'_>, + rng: &mut impl Rng, +) -> Result { + let reward_mode = attacker_state.settings.reward_mode; + let performed_steps = match reward_mode { + RewardMode::Cumulative => &attacker_state.runtime.performed_nodes, + RewardMode::OneOff | RewardMode::ExpectedTtc | RewardMode::SampleTtc => { + attacker_state.step_performed_nodes + } + }; + let graph = attacker_state.attack_graph; + // Node-id order, so the float sum and the `SAMPLE_TTC` draws don't + // depend on `HashSet` order. + let performed_steps = sorted_by_id(graph, performed_steps.iter().copied()); + + let mut step_reward: f64 = performed_steps + .iter() + .map(|&n| { + node_reward( + &graph.nodes[n], + attacker_state.settings.rewards.as_ref(), + attacker_state.model, + ) + }) + .sum(); + + if attacker_state.sim_state.settings.ttc_mode != TtcMode::Disabled { + // If TTC Mode is not disabled, attacker is penalized for each attempt + step_reward -= attacker_state.step_attempted_nodes.len() as f64; + } else if matches!(reward_mode, RewardMode::ExpectedTtc | RewardMode::SampleTtc) { + // If TTC Mode is disabled but reward mode uses TTCs, penalize with TTCs + for &n in &performed_steps { + // `TTCDist.from_node(node) if node.ttc else 0` + let Some(ttc) = graph.nodes[n].ttc.as_ref().filter(|ttc| is_truthy(ttc)) else { + continue; + }; + let ttc_dist = TtcDist::from_dict(ttc)?; + step_reward -= match reward_mode { + RewardMode::ExpectedTtc => ttc_dist.expected_value(), + _ => ttc_dist.sample_value(rng), + }; + } + } + + Ok(step_reward) +} + +/// Port of `MalSimulator.agent_reward`. +pub fn agent_reward(agent_state: &AgentState<'_>, rng: &mut impl Rng) -> Result { + match agent_state { + AgentState::Attacker(attacker_state) => attacker_step_reward(attacker_state, rng), + AgentState::Defender(defender_state) => defender_step_reward(defender_state), + } +} + +/// Python truthiness of a node's `ttc` value (`None` and `{}` are falsy). +fn is_truthy(ttc: &serde_json::Value) -> bool { + match ttc { + serde_json::Value::Null => false, + serde_json::Value::Object(map) => !map.is_empty(), + _ => true, + } +} diff --git a/core/malsim-core/src/run_simulation.rs b/core/malsim-core/src/run_simulation.rs new file mode 100644 index 00000000..23ea3dd0 --- /dev/null +++ b/core/malsim-core/src/run_simulation.rs @@ -0,0 +1,221 @@ +//! Port of `run_simulation.py`: the reference simulation loop the CLI +//! uses. Every agent's policy picks one action per step until the +//! simulation is done. +//! +//! Python's `run_simulation(sim)` reads agent settings, policies and +//! rewards off the `MalSimulator`. The Rust `Simulator` holds none of +//! those, so the `Scenario` it was built from is passed alongside it. See +//! `PORTING_NOTES.md` §11. + +use std::collections::{BTreeMap, HashMap}; +use std::fmt; +use std::rc::Rc; + +use maltoolbox_attackgraph::AttackGraphNodeId; +use rand::rngs::StdRng; +use rand::{RngExt, SeedableRng}; + +use crate::agent_state::{AgentState, AgentStateError}; +use crate::policies::{policy_from_name, DecisionAgent, PolicyError}; +use crate::rewards::{agent_reward, RewardError}; +use crate::scenario::agent_settings::{AgentSettings, AgentSettingsError}; +use crate::scenario::Scenario; +use crate::simulator::{Simulator, SimulatorError, StepOutcome}; + +#[derive(Debug)] +pub enum RunSimulationError { + /// The simulator does not run on the scenario's attack graph. + GraphMismatch, + AgentSettings(AgentSettingsError), + AgentState(AgentStateError), + Policy(PolicyError), + Reward(RewardError), + Simulator(SimulatorError), +} + +impl fmt::Display for RunSimulationError { + fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { + match self { + RunSimulationError::GraphMismatch => write!( + f, + "the simulator was not built from the scenario's attack graph" + ), + RunSimulationError::AgentSettings(e) => write!(f, "{e}"), + RunSimulationError::AgentState(e) => write!(f, "{e}"), + RunSimulationError::Policy(e) => write!(f, "{e}"), + RunSimulationError::Reward(e) => write!(f, "{e}"), + RunSimulationError::Simulator(e) => write!(f, "{e}"), + } + } +} + +impl std::error::Error for RunSimulationError { + fn source(&self) -> Option<&(dyn std::error::Error + 'static)> { + match self { + RunSimulationError::GraphMismatch => None, + RunSimulationError::AgentSettings(e) => Some(e), + RunSimulationError::AgentState(e) => Some(e), + RunSimulationError::Policy(e) => Some(e), + RunSimulationError::Reward(e) => Some(e), + RunSimulationError::Simulator(e) => Some(e), + } + } +} + +impl From for RunSimulationError { + fn from(e: AgentSettingsError) -> Self { + RunSimulationError::AgentSettings(e) + } +} + +impl From for RunSimulationError { + fn from(e: AgentStateError) -> Self { + RunSimulationError::AgentState(e) + } +} + +impl From for RunSimulationError { + fn from(e: PolicyError) -> Self { + RunSimulationError::Policy(e) + } +} + +impl From for RunSimulationError { + fn from(e: RewardError) -> Self { + RunSimulationError::Reward(e) + } +} + +impl From for RunSimulationError { + fn from(e: SimulatorError) -> Self { + RunSimulationError::Simulator(e) + } +} + +/// The decision agents of a scenario, by agent name: Python's lazily +/// instantiated `AgentRuntimeMixin.agent`. An agent without a `policy` has +/// no entry. +pub type DecisionAgents = BTreeMap>; + +/// Instantiates the built-in policy each of the scenario's agents names. +pub fn decision_agents(scenario: &Scenario) -> Result { + let mut agents = DecisionAgents::new(); + for settings in &scenario.agent_settings { + let (policy, config) = match settings { + AgentSettings::Attacker(a) => (&a.policy, &a.config), + AgentSettings::Defender(d) => (&d.policy, &d.config), + }; + if let Some(policy) = policy { + agents.insert( + settings.name().to_string(), + policy_from_name(policy, config)?, + ); + } + } + Ok(agents) +} + +/// Run a simulation with the agents (and their built-in policies) of +/// `scenario`, on a `sim` built from `scenario.attack_graph`. +/// +/// `max_steps`: if set, stop after this many steps even if the simulation +/// isn't done. +/// +/// Returns the selected actions by each agent in each step. +pub fn run_simulation( + sim: &mut Simulator, + scenario: &Scenario, + max_steps: Option, +) -> Result>, RunSimulationError> { + let mut agents = decision_agents(scenario)?; + run_simulation_with_agents(sim, scenario, &mut agents, max_steps) +} + +/// `run_simulation` with caller-supplied decision agents, e.g. policies +/// that are not built in. Agents of the scenario without an entry in +/// `agents` wait. +pub fn run_simulation_with_agents( + sim: &mut Simulator, + scenario: &Scenario, + agents: &mut DecisionAgents, + max_steps: Option, +) -> Result>, RunSimulationError> { + if !Rc::ptr_eq(sim.graph(), &scenario.attack_graph) { + return Err(RunSimulationError::GraphMismatch); + } + let mut agent_actions: BTreeMap> = BTreeMap::new(); + let mut total_rewards: Vec = vec![0.0; scenario.agent_settings.len()]; + + println!("Starting CLI env simulator."); + // One generator for what Python's `MalSimulator.rng` is used for: + // sampling entry points, seeding the simulator and `SAMPLE_TTC` + // rewards. + let mut rng = match scenario.sim_settings.seed { + Some(seed) => StdRng::seed_from_u64(seed), + None => StdRng::from_rng(&mut rand::rng()), + }; + let flat_agents = scenario.flatten_agents(&mut rng)?; + let state = sim.reset(&scenario.sim_settings, flat_agents, rng.random())?; + let mut outcome = StepOutcome::from_reset(state); + + let mut iteration: u64 = 0; + while !sim.state().is_some_and(|state| state.done()) + && max_steps.is_none_or(|max_steps| iteration < max_steps) + { + println!("Iteration {iteration}"); + let mut actions: HashMap> = HashMap::new(); + + // Select actions for each agent + { + let attack_graph = scenario.attack_graph.borrow(); + let model = scenario.model.borrow(); + let state = sim.state().ok_or(SimulatorError::NotReset)?; + for settings in &scenario.agent_settings { + let agent_name = settings.name(); + let Some(decision_agent) = agents.get_mut(agent_name) else { + println!( + "Agent \"{agent_name}\" has no decision agent class \ + specified in scenario. Waiting." + ); + continue; + }; + + let agent_state = + AgentState::new(settings, &attack_graph, &model, state, &outcome)?; + if let Some(agent_action) = decision_agent.get_next_action(&agent_state)? { + actions.insert(agent_name.to_string(), vec![agent_action]); + + // Store agent action + agent_actions + .entry(agent_name.to_string()) + .or_default() + .push(agent_action); + } + } + } + + // Perform next step of simulation + outcome = sim.step(&actions)?; + { + let attack_graph = scenario.attack_graph.borrow(); + let model = scenario.model.borrow(); + let state = sim.state().ok_or(SimulatorError::NotReset)?; + for (settings, total_reward) in scenario.agent_settings.iter().zip(&mut total_rewards) { + let agent_state = + AgentState::new(settings, &attack_graph, &model, state, &outcome)?; + *total_reward += agent_reward(&agent_state, &mut rng)?; + } + } + iteration += 1; + println!("---"); + } + + println!("Simulation over after {iteration} steps."); + + // Print total rewards + for (settings, total_reward) in scenario.agent_settings.iter().zip(&total_rewards) { + println!("Total reward \"{}\" {total_reward:?}", settings.name()); + } + + Ok(agent_actions) +} diff --git a/core/malsim-core/src/scenario/agent_settings.rs b/core/malsim-core/src/scenario/agent_settings.rs new file mode 100644 index 00000000..c810ab66 --- /dev/null +++ b/core/malsim-core/src/scenario/agent_settings.rs @@ -0,0 +1,854 @@ +//! Rust port of `python/malsim/config/agent_settings.py`'s +//! `AttackerSettings`/`DefenderSettings` and of +//! `agent_settings_factories.py::agent_settings_from_dict` - see +//! `PORTING_NOTES.md` §7 C3. +//! +//! Like the Python generic `AttackerSettings[T]`, `AttackerSettings` is +//! parsed with `N = String` (entry points/goals as full names) and turned +//! into `N = AttackGraphNodeId` once a graph exists +//! (`convert_to_attack_graph_nodes`). The rules stay unresolved +//! `NodePropertyRule`s, as in Python; `scenario::flatten` resolves them. +//! +//! `policy` is carried as an opaque string and never instantiated (§2.6): +//! there are no Rust policies, and an embedding caller does its own action +//! selection. + +use std::collections::{BTreeMap, BTreeSet}; +use std::fmt; + +use maltoolbox_attackgraph::{AttackGraph, AttackGraphNodeId}; +use serde_json::{Map, Value}; + +use crate::scenario::node_property_rule::{NodePropertyRule, NodePropertyRuleError, StepValues}; +use crate::settings::RewardMode; +use crate::ttc::{ttc_dist_name, TtcDist}; + +#[derive(Debug, Clone, PartialEq, Eq)] +pub enum AgentSettingsError { + /// The agent's settings are not a mapping. + NotAMapping { agent: String }, + /// Python's `ValueError("Illegal keys in agent scenario settings: ...")`. + IllegalKeys { agent: String, keys: Vec }, + /// `type` is missing (Python: `KeyError: 'type'`). + MissingType { agent: String }, + /// `type` is not `attacker`/`defender` (Python: `AgentType(...)`'s + /// `ValueError`). + InvalidType { agent: String, value: String }, + /// A field has a value of the wrong shape. + InvalidField { + agent: String, + field: &'static str, + reason: String, + }, + /// A `NodePropertyRule` field could not be parsed. + Rule { + agent: String, + field: &'static str, + source: NodePropertyRuleError, + }, + /// An entry point or goal names no node in the attack graph (Python: + /// `full_name_or_node_to_node`'s lookup error). + UnknownNode { agent: String, message: String }, +} + +impl fmt::Display for AgentSettingsError { + fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { + match self { + AgentSettingsError::NotAMapping { agent } => { + write!(f, "Settings of agent '{agent}' must be a mapping") + } + AgentSettingsError::IllegalKeys { agent, keys } => write!( + f, + "Illegal keys in agent scenario settings of '{agent}': {keys:?}" + ), + AgentSettingsError::MissingType { agent } => { + write!(f, "Agent '{agent}' has no 'type'") + } + AgentSettingsError::InvalidType { agent, value } => { + write!(f, "'{value}' is not a valid AgentType (agent '{agent}')") + } + AgentSettingsError::InvalidField { + agent, + field, + reason, + } => { + write!(f, "Invalid '{field}' for agent '{agent}': {reason}") + } + AgentSettingsError::Rule { + agent, + field, + source, + } => { + write!(f, "Invalid '{field}' for agent '{agent}': {source}") + } + AgentSettingsError::UnknownNode { agent, message } => { + write!(f, "Agent '{agent}': {message}") + } + } + } +} + +impl std::error::Error for AgentSettingsError { + fn source(&self) -> Option<&(dyn std::error::Error + 'static)> { + match self { + AgentSettingsError::Rule { source, .. } => Some(source), + _ => None, + } + } +} + +/// Port of `AgentType`. +#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)] +pub enum AgentType { + Attacker, + Defender, +} + +impl AgentType { + /// Parses the Python enum *value* (`AgentType('attacker')`). + pub fn from_value(value: &str) -> Option { + match value { + "attacker" => Some(AgentType::Attacker), + "defender" => Some(AgentType::Defender), + _ => None, + } + } + + pub fn value(self) -> &'static str { + match self { + AgentType::Attacker => "attacker", + AgentType::Defender => "defender", + } + } +} + +/// `AttackerSettings.entry_points`'s two shapes: one set of entry points, +/// or several alternative sets of which one is sampled per reset (Python's +/// `Set[T]` vs `tuple[Set[T], ...]`). +#[derive(Debug, Clone, PartialEq, Eq)] +pub enum EntryPoints { + Single(BTreeSet), + Multiple(Vec>), +} + +impl Default for EntryPoints { + fn default() -> Self { + EntryPoints::Single(BTreeSet::new()) + } +} + +/// Port of `AttackerSettings`. +#[derive(Debug, Clone, PartialEq)] +pub struct AttackerSettings { + pub name: String, + pub entry_points: EntryPoints, + /// Name of the Python policy class; opaque in Rust (§2.6). + pub policy: Option, + pub actionable_steps: Option>, + pub rewards: Option>, + pub config: Map, + pub reward_mode: RewardMode, + /// Goals affect simulation termination but are optional. + pub goals: BTreeSet, + /// TTC distributions that override TTCs set in the language. + pub ttc_dists: Option>, +} + +/// Port of `DefenderSettings`. +#[derive(Debug, Clone, PartialEq)] +pub struct DefenderSettings { + pub name: String, + /// Name of the Python policy class; opaque in Rust (§2.6). + pub policy: Option, + pub observable_steps: Option>, + pub actionable_steps: Option>, + pub rewards: Option>, + pub false_positive_rates: Option>, + pub false_negative_rates: Option>, + pub config: Map, + pub reward_mode: RewardMode, +} + +/// `AttackerSettings[T] | DefenderSettings`. +#[derive(Debug, Clone, PartialEq)] +pub enum AgentSettings { + Attacker(AttackerSettings), + Defender(DefenderSettings), +} + +impl AgentSettings { + pub fn name(&self) -> &str { + match self { + AgentSettings::Attacker(a) => &a.name, + AgentSettings::Defender(d) => &d.name, + } + } + + pub fn agent_type(&self) -> AgentType { + match self { + AgentSettings::Attacker(_) => AgentType::Attacker, + AgentSettings::Defender(_) => AgentType::Defender, + } + } +} + +impl AttackerSettings { + /// Port of `convert_to_attack_graph_nodes`: resolves entry points and + /// goals from full names to node ids. + pub fn convert_to_attack_graph_nodes( + &self, + attack_graph: &AttackGraph, + ) -> Result, AgentSettingsError> { + let to_nodes = |names: &BTreeSet| { + names + .iter() + .map(|full_name| { + attack_graph.get_node_by_full_name(full_name).map_err(|e| { + AgentSettingsError::UnknownNode { + agent: self.name.clone(), + message: e.to_string(), + } + }) + }) + .collect::, _>>() + }; + + let entry_points = match &self.entry_points { + EntryPoints::Single(eps) => EntryPoints::Single(to_nodes(eps)?), + EntryPoints::Multiple(sets) => { + EntryPoints::Multiple(sets.iter().map(to_nodes).collect::, _>>()?) + } + }; + + Ok(AttackerSettings { + name: self.name.clone(), + entry_points, + policy: self.policy.clone(), + actionable_steps: self.actionable_steps.clone(), + rewards: self.rewards.clone(), + config: self.config.clone(), + reward_mode: self.reward_mode, + goals: to_nodes(&self.goals)?, + ttc_dists: self.ttc_dists.clone(), + }) + } +} + +fn name_list(names: &BTreeSet) -> Value { + Value::Array(names.iter().cloned().map(Value::String).collect()) +} + +impl EntryPoints { + /// Inverse of `load_entry_points`: a list of full names, or a list of + /// such lists for alternative sets. + pub fn to_value(&self) -> Value { + match self { + EntryPoints::Single(eps) => name_list(eps), + EntryPoints::Multiple(sets) => Value::Array(sets.iter().map(name_list).collect()), + } + } +} + +/// Inserts `rule` under `key` unless it is absent or empty (Python's +/// `if self.rule:` guard in `to_dict`). +fn insert_rule( + d: &mut Map, + key: &str, + rule: &Option>, +) { + if let Some(rule) = rule.as_ref().filter(|r| !r.is_empty()) { + d.insert(key.into(), rule.to_value()); + } +} + +/// Inserts the `ttc_overrides` rule in the form the loader reads: each +/// distribution as its predefined name (`TtcDist` -> `ttc_dist_name`), +/// not `TtcDist::to_dict`'s dict. A custom distribution has no name and +/// is written as its dict, so reloading fails loudly instead of the +/// override being dropped silently. +fn insert_ttc_rule(d: &mut Map, rule: &Option>) { + let Some(rule) = rule.as_ref().filter(|r| !r.is_empty()) else { + return; + }; + let by_asset = |by_asset: &Option>>| match by_asset { + None => Value::Null, + Some(by_asset) => Value::Object( + by_asset + .iter() + .map(|(asset, steps): (&String, &StepValues)| { + let steps = match steps { + StepValues::Listed(steps) => { + Value::Array(steps.iter().cloned().map(Value::String).collect()) + } + StepValues::Valued(values) => Value::Object( + values + .iter() + .map(|(step, dist)| { + let dist = ttc_dist_name(dist).map_or_else( + || dist.to_dict(), + |n| Value::String(n.into()), + ); + (step.clone(), dist) + }) + .collect(), + ), + }; + (asset.clone(), steps) + }) + .collect(), + ), + }; + d.insert( + "ttc_overrides".into(), + serde_json::json!({ + "by_asset_type": by_asset(&rule.by_asset_type), + "by_asset_name": by_asset(&rule.by_asset_name), + }), + ); +} + +/// Inserts `policy`, `config` and `reward_mode` the way both agent types +/// write them. Unlike Python's `to_dict`, `reward_mode` is always written, +/// so a non-default mode survives a save/load round trip. +fn insert_common( + d: &mut Map, + policy: &Option, + config: &Map, + reward_mode: RewardMode, +) { + if let Some(policy) = policy { + d.insert("policy".into(), Value::String(policy.clone())); + } + if !config.is_empty() { + d.insert("config".into(), Value::Object(config.clone())); + } + d.insert( + "reward_mode".into(), + Value::String(reward_mode.name().into()), + ); +} + +impl AttackerSettings { + /// Port of `AttackerSettings.to_dict`, plus `ttc_overrides` (which + /// Python drops) so the settings round-trip through + /// `agent_settings_from_dict`. + pub fn to_value(&self) -> Value { + let mut d = Map::new(); + d.insert("type".into(), AgentType::Attacker.value().into()); + d.insert("entry_points".into(), self.entry_points.to_value()); + if !self.goals.is_empty() { + d.insert("goals".into(), name_list(&self.goals)); + } + insert_common(&mut d, &self.policy, &self.config, self.reward_mode); + insert_rule(&mut d, "actionable_steps", &self.actionable_steps); + insert_rule(&mut d, "rewards", &self.rewards); + insert_ttc_rule(&mut d, &self.ttc_dists); + Value::Object(d) + } +} + +impl DefenderSettings { + /// Port of `DefenderSettings.to_dict`, plus `reward_mode`. + pub fn to_value(&self) -> Value { + let mut d = Map::new(); + d.insert("type".into(), AgentType::Defender.value().into()); + insert_common(&mut d, &self.policy, &self.config, self.reward_mode); + insert_rule(&mut d, "observable_steps", &self.observable_steps); + insert_rule(&mut d, "actionable_steps", &self.actionable_steps); + insert_rule(&mut d, "rewards", &self.rewards); + insert_rule(&mut d, "false_positive_rates", &self.false_positive_rates); + insert_rule(&mut d, "false_negative_rates", &self.false_negative_rates); + Value::Object(d) + } +} + +/// Port of `_validate_agent_dict`'s `allowed_keys`. +pub const ALLOWED_AGENT_KEYS: [&str; 13] = [ + "type", + "policy", + "agent_class", + "config", + "rewards", + "false_positive_rates", + "false_negative_rates", + "observable_steps", + "actionable_steps", + "entry_points", + "goals", + "reward_mode", + "ttc_overrides", +]; + +/// Port of `_validate_agent_dict`. Like Python, only illegal keys are +/// rejected: Python's missing-key check tests `illegal_keys` a second +/// time instead of `missing_keys`, so a missing `policy` is accepted (and +/// a missing `type` only fails later, when it's read). +fn validate_agent_dict<'a>( + name: &str, + d: &'a Value, +) -> Result<&'a Map, AgentSettingsError> { + let obj = d + .as_object() + .ok_or_else(|| AgentSettingsError::NotAMapping { + agent: name.to_string(), + })?; + let mut illegal_keys: Vec = obj + .keys() + .filter(|k| !ALLOWED_AGENT_KEYS.contains(&k.as_str())) + .cloned() + .collect(); + if !illegal_keys.is_empty() { + illegal_keys.sort(); + return Err(AgentSettingsError::IllegalKeys { + agent: name.to_string(), + keys: illegal_keys, + }); + } + Ok(obj) +} + +fn string_set( + name: &str, + field: &'static str, + items: &[Value], +) -> Result, AgentSettingsError> { + items + .iter() + .map(|item| { + item.as_str() + .map(str::to_string) + .ok_or_else(|| AgentSettingsError::InvalidField { + agent: name.to_string(), + field, + reason: format!("expected a full name string, got {item}"), + }) + }) + .collect() +} + +/// The members of a YAML `!!set`. PyYAML loads `!!set` as a Python `set`, +/// but `serde_yaml` drops the tag and yields the underlying mapping of +/// member -> `null`, so a mapping whose values are all `null` is read as a +/// set. Returns `None` for any other value. +fn yaml_set_members(value: &Value) -> Option> { + let map = value.as_object()?; + map.values() + .all(Value::is_null) + .then(|| map.keys().cloned().map(Value::String).collect()) +} + +/// Port of `_load_entry_points`: absent/`null` is an empty set, a list of +/// strings (or a YAML `!!set`) is one set, a list of lists is several +/// alternative sets. +fn load_entry_points( + name: &str, + d: Option<&Value>, +) -> Result, AgentSettingsError> { + let invalid = |reason: String| AgentSettingsError::InvalidField { + agent: name.to_string(), + field: "entry_points", + reason, + }; + let items = match d { + None | Some(Value::Null) => return Ok(EntryPoints::default()), + Some(Value::Array(items)) => items, + Some(other) if yaml_set_members(other).is_some() => { + let members = yaml_set_members(other).expect("checked is_some"); + return Ok(EntryPoints::Single(string_set( + name, + "entry_points", + &members, + )?)); + } + Some(other) => { + return Err(invalid(format!( + "entry_points must be a set or list of strings, got {other}" + ))) + } + }; + + // `all(...)` over an empty list is true, so `[]` is an empty single set. + if items.iter().all(Value::is_string) { + Ok(EntryPoints::Single(string_set( + name, + "entry_points", + items, + )?)) + } else if items.iter().all(Value::is_array) { + items + .iter() + .map(|set| { + string_set( + name, + "entry_points", + set.as_array().expect("checked is_array"), + ) + }) + .collect::, _>>() + .map(EntryPoints::Multiple) + } else { + Err(invalid( + "entry_points list must contain either all strings or all sets/lists of strings" + .to_string(), + )) + } +} + +fn load_goals(name: &str, d: Option<&Value>) -> Result, AgentSettingsError> { + match d { + None => Ok(BTreeSet::new()), + Some(Value::Array(items)) => string_set(name, "goals", items), + Some(other) if yaml_set_members(other).is_some() => string_set( + name, + "goals", + &yaml_set_members(other).expect("checked is_some"), + ), + Some(other) => Err(AgentSettingsError::InvalidField { + agent: name.to_string(), + field: "goals", + reason: format!("expected a list of full names, got {other}"), + }), + } +} + +fn load_rule( + name: &str, + d: &Map, + field: &'static str, +) -> Result>, AgentSettingsError> { + NodePropertyRule::from_optional_value(d.get(field)).map_err(|source| AgentSettingsError::Rule { + agent: name.to_string(), + field, + source, + }) +} + +/// `d.get('policy') or d.get('agent_class')` - a falsy (empty/null) +/// `policy` falls through to `agent_class`. +fn load_policy(name: &str, d: &Map) -> Result, AgentSettingsError> { + for field in ["policy", "agent_class"] { + match d.get(field) { + None | Some(Value::Null) => {} + Some(Value::String(s)) if s.is_empty() => {} + Some(Value::String(s)) => return Ok(Some(s.clone())), + Some(other) => { + return Err(AgentSettingsError::InvalidField { + agent: name.to_string(), + field, + reason: format!("expected a policy class name, got {other}"), + }) + } + } + } + Ok(None) +} + +/// `d.get('config', {})`; `null` is treated as empty. +fn load_config( + name: &str, + d: &Map, +) -> Result, AgentSettingsError> { + match d.get("config") { + None | Some(Value::Null) => Ok(Map::new()), + Some(Value::Object(config)) => Ok(config.clone()), + Some(other) => Err(AgentSettingsError::InvalidField { + agent: name.to_string(), + field: "config", + reason: format!("expected a mapping, got {other}"), + }), + } +} + +/// `RewardMode[d.get('reward_mode', )]`, where the default is +/// `EXPECTED_TTC` for attackers and `CUMULATIVE` for defenders. +fn load_reward_mode( + name: &str, + agent_type: AgentType, + d: &Map, +) -> Result { + let invalid = |reason: String| AgentSettingsError::InvalidField { + agent: name.to_string(), + field: "reward_mode", + reason, + }; + match d.get("reward_mode") { + None => Ok(match agent_type { + AgentType::Attacker => RewardMode::ExpectedTtc, + AgentType::Defender => RewardMode::Cumulative, + }), + Some(Value::String(s)) => { + RewardMode::from_name(s).ok_or_else(|| invalid(format!("unknown RewardMode '{s}'"))) + } + Some(other) => Err(invalid(format!("expected a RewardMode name, got {other}"))), + } +} + +/// Port of `agent_settings_from_dict`, minus policy-class resolution +/// (§2.6: `policy` is kept as its name). +pub fn agent_settings_from_dict( + name: &str, + d: &Value, +) -> Result, AgentSettingsError> { + let d = validate_agent_dict(name, d)?; + let agent_type = match d.get("type") { + None => { + return Err(AgentSettingsError::MissingType { + agent: name.to_string(), + }) + } + Some(v) => { + let value = v.as_str().unwrap_or_default(); + AgentType::from_value(value).ok_or_else(|| AgentSettingsError::InvalidType { + agent: name.to_string(), + value: v.to_string(), + })? + } + }; + + let policy = load_policy(name, d)?; + let config = load_config(name, d)?; + let reward_mode = load_reward_mode(name, agent_type, d)?; + + Ok(match agent_type { + AgentType::Attacker => AgentSettings::Attacker(AttackerSettings { + name: name.to_string(), + entry_points: load_entry_points(name, d.get("entry_points"))?, + goals: load_goals(name, d.get("goals"))?, + ttc_dists: load_rule(name, d, "ttc_overrides")?, + policy, + actionable_steps: load_rule(name, d, "actionable_steps")?, + rewards: load_rule(name, d, "rewards")?, + config, + reward_mode, + }), + AgentType::Defender => AgentSettings::Defender(DefenderSettings { + name: name.to_string(), + policy, + observable_steps: load_rule(name, d, "observable_steps")?, + actionable_steps: load_rule(name, d, "actionable_steps")?, + rewards: load_rule(name, d, "rewards")?, + false_positive_rates: load_rule(name, d, "false_positive_rates")?, + false_negative_rates: load_rule(name, d, "false_negative_rates")?, + config, + reward_mode, + }), + }) +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::test_fixtures::wiper_attack_graph; + use serde_json::json; + + fn attacker(settings: AgentSettings) -> AttackerSettings { + match settings { + AgentSettings::Attacker(a) => a, + AgentSettings::Defender(_) => panic!("expected an attacker"), + } + } + + fn defender(settings: AgentSettings) -> DefenderSettings { + match settings { + AgentSettings::Defender(d) => d, + AgentSettings::Attacker(_) => panic!("expected a defender"), + } + } + + fn names(items: &[&str]) -> BTreeSet { + items.iter().map(|s| s.to_string()).collect() + } + + #[test] + fn attacker_from_dict() { + let a = attacker( + agent_settings_from_dict( + "Attacker1", + &json!({ + "type": "attacker", + "agent_class": "BreadthFirstAttacker", + "entry_points": ["User:3:phishing", "Host:0:connect"], + "goals": ["Data:2:read"], + "actionable_steps": {"by_asset_type": {"Host": ["authenticate", "connect"]}}, + "rewards": {"by_asset_name": {"Host:0": {"access": 4}}}, + "ttc_overrides": {"by_asset_type": {"Host": {"connect": "HardAndUncertain"}}}, + "config": {"seed": 1}, + }), + ) + .unwrap(), + ); + assert_eq!(a.name, "Attacker1"); + assert_eq!(a.policy.as_deref(), Some("BreadthFirstAttacker")); + assert_eq!( + a.entry_points, + EntryPoints::Single(names(&["User:3:phishing", "Host:0:connect"])) + ); + assert_eq!(a.goals, names(&["Data:2:read"])); + assert!(a.actionable_steps.is_some()); + assert!(a.rewards.is_some()); + assert!(a.ttc_dists.is_some()); + assert_eq!(a.config["seed"], json!(1)); + // `agent_settings_from_dict`'s default for attackers, not the dataclass's ONE_OFF. + assert_eq!(a.reward_mode, RewardMode::ExpectedTtc); + } + + #[test] + fn defender_from_dict() { + let d = defender( + agent_settings_from_dict( + "Defender1", + &json!({ + "type": "defender", + "policy": "PassiveAgent", + "reward_mode": "ONE_OFF", + "observable_steps": {"by_asset_type": {"Host": ["access"]}}, + "false_positive_rates": {"by_asset_type": {"Host": {"connect": 0.5}}}, + "false_negative_rates": {"by_asset_type": {"Host": {"access": 0.5}}}, + }), + ) + .unwrap(), + ); + assert_eq!(d.policy.as_deref(), Some("PassiveAgent")); + assert_eq!(d.reward_mode, RewardMode::OneOff); + assert!(d.observable_steps.is_some()); + assert!(d.actionable_steps.is_none()); + assert!(d.false_positive_rates.is_some()); + assert!(d.false_negative_rates.is_some()); + assert!(d.config.is_empty()); + } + + #[test] + fn policy_takes_precedence_over_agent_class_and_is_opaque() { + let a = attacker( + agent_settings_from_dict( + "a", + &json!({"type": "attacker", "policy": "NotARealPolicy", "agent_class": "Other"}), + ) + .unwrap(), + ); + assert_eq!(a.policy.as_deref(), Some("NotARealPolicy")); + + let a = attacker(agent_settings_from_dict("a", &json!({"type": "attacker"})).unwrap()); + assert_eq!(a.policy, None); + } + + #[test] + fn entry_points_shapes() { + let load = |v: Value| { + attacker( + agent_settings_from_dict("a", &json!({"type": "attacker", "entry_points": v})) + .unwrap(), + ) + .entry_points + }; + assert_eq!(load(Value::Null), EntryPoints::Single(BTreeSet::new())); + assert_eq!(load(json!([])), EntryPoints::Single(BTreeSet::new())); + assert_eq!( + load(json!([["A:x", "B:y"], ["C:z"]])), + EntryPoints::Multiple(vec![names(&["A:x", "B:y"]), names(&["C:z"])]) + ); + assert!(matches!( + agent_settings_from_dict( + "a", + &json!({"type": "attacker", "entry_points": ["A:x", ["B:y"]]}) + ), + Err(AgentSettingsError::InvalidField { + field: "entry_points", + .. + }) + )); + // YAML `!!set` as serde_yaml hands it over (`detector_lang_scenario.yml`). + assert_eq!( + load(json!({"A:x": null, "B:y": null})), + EntryPoints::Single(names(&["A:x", "B:y"])) + ); + assert!(matches!( + agent_settings_from_dict( + "a", + &json!({"type": "attacker", "entry_points": {"A:x": 1}}) + ), + Err(AgentSettingsError::InvalidField { + field: "entry_points", + .. + }) + )); + assert!(matches!( + agent_settings_from_dict("a", &json!({"type": "attacker", "entry_points": "A:x"})), + Err(AgentSettingsError::InvalidField { + field: "entry_points", + .. + }) + )); + } + + #[test] + fn invalid_agent_dicts_are_rejected() { + assert!(matches!( + agent_settings_from_dict("a", &json!({"type": "attacker", "bogus": 1})), + Err(AgentSettingsError::IllegalKeys { keys, .. }) if keys == vec!["bogus".to_string()] + )); + assert!(matches!( + agent_settings_from_dict("a", &json!({"policy": "PassiveAgent"})), + Err(AgentSettingsError::MissingType { .. }) + )); + assert!(matches!( + agent_settings_from_dict("a", &json!({"type": "observer"})), + Err(AgentSettingsError::InvalidType { .. }) + )); + assert!(matches!( + agent_settings_from_dict( + "a", + &json!({"type": "attacker", "reward_mode": "SOMETIMES"}) + ), + Err(AgentSettingsError::InvalidField { + field: "reward_mode", + .. + }) + )); + assert!(matches!( + agent_settings_from_dict( + "a", + &json!({"type": "defender", "rewards": {"Host:0:access": 1}}) + ), + Err(AgentSettingsError::Rule { + field: "rewards", + .. + }) + )); + } + + #[test] + fn convert_to_attack_graph_nodes_resolves_full_names() { + let (graph, _model) = wiper_attack_graph(); + let a = attacker( + agent_settings_from_dict( + "a", + &json!({ + "type": "attacker", + "entry_points": [["InfectedDevice:infect"], ["VulnerableDevice:infect"]], + "goals": ["InfectedData:read"], + }), + ) + .unwrap(), + ); + let converted = a.convert_to_attack_graph_nodes(&graph).unwrap(); + let id = |n: &str| graph.get_node_by_full_name(n).unwrap(); + assert_eq!( + converted.entry_points, + EntryPoints::Multiple(vec![ + BTreeSet::from([id("InfectedDevice:infect")]), + BTreeSet::from([id("VulnerableDevice:infect")]), + ]) + ); + assert_eq!(converted.goals, BTreeSet::from([id("InfectedData:read")])); + + let bad = attacker( + agent_settings_from_dict("a", &json!({"type": "attacker", "goals": ["Nope:read"]})) + .unwrap(), + ); + assert!(matches!( + bad.convert_to_attack_graph_nodes(&graph), + Err(AgentSettingsError::UnknownNode { .. }) + )); + } +} diff --git a/core/malsim-core/src/scenario/flatten.rs b/core/malsim-core/src/scenario/flatten.rs new file mode 100644 index 00000000..f39750f9 --- /dev/null +++ b/core/malsim-core/src/scenario/flatten.rs @@ -0,0 +1,320 @@ +//! Resolves `AttackerSettings`/`DefenderSettings` against a concrete graph +//! into the flat, id-keyed inputs `Simulator::reset` consumes - the +//! Rust-only counterpart of `python/malsim/mal_simulator/native_settings.py` +//! (`flatten_attacker_settings`/`flatten_defender_settings`) plus +//! `attacker_state_factories.py::get_entry_points`. See `PORTING_NOTES.md` +//! §7 C3. +//! +//! Each function keeps the exact per-node semantics of the Python helper +//! it mirrors (`node_is_actionable`, `node_is_observable`, +//! `node_false_*_rate`), including the "empty rule behaves like no rule" +//! quirks those helpers get from testing `if rule:` (`NodePropertyRule. +//! __len__`). + +use crate::types::{NodeMap, NodeSet}; +use std::collections::BTreeSet; + +use maltoolbox_attackgraph::{AttackGraph, AttackGraphNodeId}; +use maltoolbox_model::Model; +use rand::{Rng, RngExt}; + +use crate::scenario::agent_settings::{AttackerSettings, DefenderSettings, EntryPoints}; +use crate::scenario::node_property_rule::NodePropertyRule; +use crate::settings::{FlatAttackerSettings, FlatDefenderSettings}; +use crate::ttc::TtcDist; + +/// Port of `get_entry_points`: the single entry-point set, or one set +/// sampled uniformly from the alternatives. An empty list of alternatives +/// (only constructible programmatically) yields no entry points. +pub fn get_entry_points( + attacker_settings: &AttackerSettings, + rng: &mut impl Rng, +) -> BTreeSet { + match &attacker_settings.entry_points { + EntryPoints::Single(entry_points) => entry_points.clone(), + EntryPoints::Multiple(options) if options.is_empty() => BTreeSet::new(), + EntryPoints::Multiple(options) => options[rng.random_range(0..options.len())].clone(), + } +} + +/// Port of `node_is_actionable`: an empty rule makes every node actionable. +fn node_is_actionable( + rule: &NodePropertyRule, + node: &maltoolbox_attackgraph::AttackGraphNode, + model: &Model, +) -> bool { + if rule.is_empty() { + return true; + } + rule.value(node, model).unwrap_or(false) +} + +/// Port of `_flatten_actionable_steps`. +fn flatten_actionable_steps( + rule: Option<&NodePropertyRule>, + graph: &AttackGraph, + model: &Model, +) -> Option { + let rule = rule?; + Some( + graph + .nodes + .iter() + .filter(|(_, node)| node_is_actionable(rule, node, model)) + .map(|(id, _)| id) + .collect(), + ) +} + +/// Port of `_flatten_observable_steps` (`node_is_observable` has no +/// empty-rule special case: an empty rule observes nothing). +fn flatten_observable_steps( + rule: Option<&NodePropertyRule>, + graph: &AttackGraph, + model: &Model, +) -> Option { + let rule = rule?; + Some( + graph + .nodes + .iter() + .filter(|(_, node)| rule.value(node, model).unwrap_or(false)) + .map(|(id, _)| id) + .collect(), + ) +} + +/// Port of `_flatten_rate_map` with `node_false_positive_rate`/ +/// `node_false_negative_rate` (identical bodies): every node gets an entry, +/// `0.0` where the rule gives nothing or is empty. +fn flatten_rate_map( + rule: Option<&NodePropertyRule>, + graph: &AttackGraph, + model: &Model, +) -> Option> { + let rule = rule?; + Some( + graph + .nodes + .iter() + .map(|(id, node)| { + let rate = if rule.is_empty() { + 0.0 + } else { + rule.value(node, model).unwrap_or(0.0) + }; + (id, rate) + }) + .collect(), + ) +} + +/// Port of `_flatten_ttc_dists`: `None` when there's no rule or it +/// matches no node. +fn flatten_ttc_dists( + rule: Option<&NodePropertyRule>, + graph: &AttackGraph, + model: &Model, +) -> Option> { + let per_node = rule?.per_node(graph, model); + (!per_node.is_empty()).then_some(per_node) +} + +/// Port of `flatten_attacker_settings`. `entry_points` is the +/// already-sampled single set (see `get_entry_points`). +pub fn flatten_attacker_settings( + attacker_settings: &AttackerSettings, + graph: &AttackGraph, + model: &Model, + entry_points: &BTreeSet, +) -> FlatAttackerSettings { + FlatAttackerSettings { + entry_points: entry_points.iter().copied().collect(), + goals: attacker_settings.goals.iter().copied().collect(), + actionable_steps: flatten_actionable_steps( + attacker_settings.actionable_steps.as_ref(), + graph, + model, + ), + ttc_dists: flatten_ttc_dists(attacker_settings.ttc_dists.as_ref(), graph, model), + } +} + +/// Port of `flatten_defender_settings`. +pub fn flatten_defender_settings( + defender_settings: &DefenderSettings, + graph: &AttackGraph, + model: &Model, +) -> FlatDefenderSettings { + FlatDefenderSettings { + actionable_steps: flatten_actionable_steps( + defender_settings.actionable_steps.as_ref(), + graph, + model, + ), + observable_steps: flatten_observable_steps( + defender_settings.observable_steps.as_ref(), + graph, + model, + ), + false_positive_rates: flatten_rate_map( + defender_settings.false_positive_rates.as_ref(), + graph, + model, + ), + false_negative_rates: flatten_rate_map( + defender_settings.false_negative_rates.as_ref(), + graph, + model, + ), + } +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::scenario::agent_settings::{agent_settings_from_dict, AgentSettings}; + use crate::test_fixtures::wiper_attack_graph; + use rand::rngs::StdRng; + use rand::SeedableRng; + use serde_json::{json, Value}; + + fn attacker(graph: &AttackGraph, d: Value) -> AttackerSettings { + match agent_settings_from_dict("a", &d).unwrap() { + AgentSettings::Attacker(a) => a.convert_to_attack_graph_nodes(graph).unwrap(), + AgentSettings::Defender(_) => panic!("expected an attacker"), + } + } + + fn defender(d: Value) -> DefenderSettings { + match agent_settings_from_dict("d", &d).unwrap() { + AgentSettings::Defender(d) => d, + AgentSettings::Attacker(_) => panic!("expected a defender"), + } + } + + fn ids(graph: &AttackGraph, names: &[&str]) -> NodeSet { + names + .iter() + .map(|n| graph.get_node_by_full_name(n).unwrap()) + .collect() + } + + #[test] + fn attacker_without_rules_flattens_to_none() { + let (graph, model) = wiper_attack_graph(); + let a = attacker( + &graph, + json!({"type": "attacker", "entry_points": ["InfectedDevice:infect"]}), + ); + let eps = get_entry_points(&a, &mut StdRng::seed_from_u64(0)); + let flat = flatten_attacker_settings(&a, &graph, &model, &eps); + assert_eq!(flat.entry_points, ids(&graph, &["InfectedDevice:infect"])); + assert!(flat.goals.is_empty()); + assert_eq!(flat.actionable_steps, None); + assert_eq!(flat.ttc_dists, None); + } + + #[test] + fn attacker_rules_resolve_to_id_maps() { + let (graph, model) = wiper_attack_graph(); + let a = attacker( + &graph, + json!({ + "type": "attacker", + "goals": ["InfectedData:read"], + "actionable_steps": {"by_asset_type": {"Device": ["infect"]}}, + "ttc_overrides": {"by_asset_name": {"VulnerableDevice": {"infect": "HardAndUncertain"}}}, + }), + ); + let flat = flatten_attacker_settings(&a, &graph, &model, &BTreeSet::new()); + assert_eq!(flat.goals, ids(&graph, &["InfectedData:read"])); + assert_eq!( + flat.actionable_steps, + Some(ids( + &graph, + &["InfectedDevice:infect", "VulnerableDevice:infect"] + )) + ); + let ttc = flat.ttc_dists.unwrap(); + assert_eq!( + ttc.keys().copied().collect::(), + ids(&graph, &["VulnerableDevice:infect"]) + ); + } + + #[test] + fn ttc_rule_matching_nothing_is_none() { + let (graph, model) = wiper_attack_graph(); + let a = attacker( + &graph, + json!({"type": "attacker", "ttc_overrides": {"by_asset_type": {"Nope": {"x": "HardAndUncertain"}}}}), + ); + assert_eq!( + flatten_attacker_settings(&a, &graph, &model, &BTreeSet::new()).ttc_dists, + None + ); + } + + #[test] + fn empty_actionability_rule_makes_everything_actionable_but_observes_nothing() { + let (graph, model) = wiper_attack_graph(); + let d = defender(json!({ + "type": "defender", + "actionable_steps": {"by_asset_type": {}}, + "observable_steps": {"by_asset_type": {}}, + })); + let flat = flatten_defender_settings(&d, &graph, &model); + assert_eq!(flat.actionable_steps.unwrap().len(), graph.nodes.len()); + assert_eq!(flat.observable_steps, Some(NodeSet::default())); + } + + #[test] + fn rate_maps_cover_every_node() { + let (graph, model) = wiper_attack_graph(); + let d = defender(json!({ + "type": "defender", + "false_positive_rates": {"by_asset_type": {"Device": {"infect": 0.25}}}, + "false_negative_rates": {"by_asset_type": {}}, + })); + let flat = flatten_defender_settings(&d, &graph, &model); + let fpr = flat.false_positive_rates.unwrap(); + assert_eq!(fpr.len(), graph.nodes.len()); + let infect = ids( + &graph, + &["InfectedDevice:infect", "VulnerableDevice:infect"], + ); + for (id, rate) in &fpr { + assert_eq!(*rate, if infect.contains(id) { 0.25 } else { 0.0 }); + } + let fnr = flat.false_negative_rates.unwrap(); + assert_eq!(fnr.len(), graph.nodes.len()); + assert!(fnr.values().all(|r| *r == 0.0)); + assert_eq!(flat.actionable_steps, None); + assert_eq!(flat.observable_steps, None); + } + + #[test] + fn get_entry_points_samples_one_alternative() { + let (graph, _model) = wiper_attack_graph(); + let a = attacker( + &graph, + json!({"type": "attacker", "entry_points": [["InfectedDevice:infect"], ["VulnerableDevice:infect"]]}), + ); + let options = [ + ids(&graph, &["InfectedDevice:infect"]), + ids(&graph, &["VulnerableDevice:infect"]), + ]; + let mut rng = StdRng::seed_from_u64(7); + let mut seen = [false, false]; + for _ in 0..64 { + let chosen: NodeSet = get_entry_points(&a, &mut rng).into_iter().collect(); + let index = options + .iter() + .position(|o| *o == chosen) + .expect("one of the options"); + seen[index] = true; + } + assert_eq!(seen, [true, true]); + } +} diff --git a/core/malsim-core/src/scenario/loading.rs b/core/malsim-core/src/scenario/loading.rs new file mode 100644 index 00000000..0e9a2470 --- /dev/null +++ b/core/malsim-core/src/scenario/loading.rs @@ -0,0 +1,487 @@ +//! Rust port of the scenario-file half of `python/malsim/scenario/scenario.py` +//! (`load_scenario_dict`, `_extend_scenario`, `recursive_update`, +//! `path_relative_to_file_dir` and `_validate_scenario_dict`), see +//! `PORTING_NOTES.md` §7 C2. +//! +//! Scenario dicts are `serde_json::Value`s (§11): YAML is parsed with +//! `serde_yaml` straight into that type, which is also what mal-toolbox's +//! `maltoolbox_model::from_dict` takes for an inline `model:`. + +use std::fmt; +use std::fs::File; +use std::io; +use std::path::{Path, PathBuf}; + +use serde_json::{Map, Value}; + +/// Port of `deprecated_fields`. +pub const DEPRECATED_FIELDS: [&str; 8] = [ + "attacker_agent_class", + "defender_agent_class", + "attacker_entry_points", + "rewards", + "observable_steps", + "actionable_steps", + "false_positive_rates", + "false_negative_rates", +]; + +/// One entry of `required_fields`: a single required field, or a pair of +/// which exactly one must be present. +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +pub enum RequiredField { + One(&'static str), + OneOf(&'static str, &'static str), +} + +/// Port of `required_fields`. +pub const REQUIRED_FIELDS: [RequiredField; 3] = [ + RequiredField::OneOf("agents", "agent_settings"), + RequiredField::One("lang_file"), + RequiredField::OneOf("model_file", "model"), +]; + +/// Port of `allowed_fields` (already flattened, as +/// `_validate_scenario_dict` uses it). +pub const ALLOWED_FIELDS: [&str; 6] = [ + "agents", + "agent_settings", + "lang_file", + "model_file", + "model", + "sim_settings", +]; + +#[derive(Debug)] +pub enum ScenarioFileError { + Io { + path: PathBuf, + source: io::Error, + }, + Yaml { + path: PathBuf, + source: serde_yaml::Error, + }, + /// The scenario file's top level is not a mapping. + NotAMapping(PathBuf), + /// A path-valued field (`extends`, `lang_file`, `model_file`) is not a + /// string. + PathNotAString(&'static str), + /// Python's `SyntaxError("Scenario setting '...' is deprecated ...")`. + DeprecatedField(String), + /// Python's `SyntaxError("Scenario setting '...' is not supported")`. + UnsupportedField(String), + /// Python's `RuntimeError("Setting '...' required in scenario file")`. + MissingField(&'static str), + /// Python's `RuntimeError("One of '(...)' is required in scenario file")`. + MissingOneOf(&'static str, &'static str), + /// Python's `RuntimeError("Only one of '(...)' is allowed in scenario file")`. + BothOf(&'static str, &'static str), +} + +impl fmt::Display for ScenarioFileError { + fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { + match self { + ScenarioFileError::Io { path, source } => { + write!( + f, + "Could not read scenario file {}: {source}", + path.display() + ) + } + ScenarioFileError::Yaml { path, source } => { + write!( + f, + "Could not parse scenario file {}: {source}", + path.display() + ) + } + ScenarioFileError::NotAMapping(path) => { + write!(f, "Scenario file {} must contain a mapping", path.display()) + } + ScenarioFileError::PathNotAString(key) => { + write!(f, "Scenario setting '{key}' must be a string path") + } + ScenarioFileError::DeprecatedField(key) => write!( + f, + "Scenario setting '{key}' is deprecated, see README or ./tests/testdata/scenarios" + ), + ScenarioFileError::UnsupportedField(key) => { + write!(f, "Scenario setting '{key}' is not supported") + } + ScenarioFileError::MissingField(key) => { + write!(f, "Setting '{key}' required in scenario file") + } + ScenarioFileError::MissingOneOf(a, b) => { + write!(f, "One of '('{a}', '{b}')' is required in scenario file") + } + ScenarioFileError::BothOf(a, b) => { + write!( + f, + "Only one of '('{a}', '{b}')' is allowed in scenario file" + ) + } + } + } +} + +impl std::error::Error for ScenarioFileError { + fn source(&self) -> Option<&(dyn std::error::Error + 'static)> { + match self { + ScenarioFileError::Io { source, .. } => Some(source), + ScenarioFileError::Yaml { source, .. } => Some(source), + _ => None, + } + } +} + +/// Port of `_validate_scenario_dict`: rejects deprecated/unsupported keys, +/// then checks required keys (and that exactly one of each pair is given). +pub fn validate_scenario_dict(scenario: &Map) -> Result<(), ScenarioFileError> { + for key in scenario.keys() { + if DEPRECATED_FIELDS.contains(&key.as_str()) { + return Err(ScenarioFileError::DeprecatedField(key.clone())); + } + if !ALLOWED_FIELDS.contains(&key.as_str()) { + return Err(ScenarioFileError::UnsupportedField(key.clone())); + } + } + + for required in REQUIRED_FIELDS { + match required { + RequiredField::OneOf(a, b) => { + let (has_a, has_b) = (scenario.contains_key(a), scenario.contains_key(b)); + if !has_a && !has_b { + return Err(ScenarioFileError::MissingOneOf(a, b)); + } + if has_a && has_b { + return Err(ScenarioFileError::BothOf(a, b)); + } + } + RequiredField::One(key) => { + if !scenario.contains_key(key) { + return Err(ScenarioFileError::MissingField(key)); + } + } + } + } + Ok(()) +} + +/// Port of `recursive_update`: deep-merges `new` over `old`. Where both +/// sides hold a mapping the merge recurses; otherwise `new`'s value wins +/// if present. An explicit `null` in `new` removes the key altogether. +pub fn recursive_update(old: &Map, new: &Map) -> Map { + let mut combined = Map::new(); + for key in old + .keys() + .chain(new.keys().filter(|k| !old.contains_key(*k))) + { + // Explicit `null` allows overriding values to None (i.e. removing them). + if matches!(new.get(key), Some(Value::Null)) { + continue; + } + let merged = match (old.get(key), new.get(key)) { + (Some(Value::Object(old_sub)), Some(Value::Object(new_sub))) => { + Value::Object(recursive_update(old_sub, new_sub)) + } + (_, Some(new_value)) => new_value.clone(), + (Some(old_value), None) => old_value.clone(), + (None, None) => unreachable!("key comes from one of the two maps"), + }; + combined.insert(key.clone(), merged); + } + combined +} + +/// Port of `path_relative_to_file_dir`: `rel_path` joined onto the +/// directory of the (symlink-resolved) `file`. Like `os.path.join`, an +/// absolute `rel_path` is returned unchanged. +pub fn path_relative_to_file_dir( + rel_path: &str, + file: &Path, +) -> Result { + let real_file = file + .canonicalize() + .map_err(|source| ScenarioFileError::Io { + path: file.to_path_buf(), + source, + })?; + let file_dir = real_file.parent().unwrap_or_else(|| Path::new("/")); + Ok(file_dir.join(rel_path)) +} + +/// Port of `_extend_scenario`: loads the scenario at +/// `original_scenario_path`, merges `overriding_scenario` over it and +/// drops the `extends` key. +fn extend_scenario( + original_scenario_path: &Path, + overriding_scenario: &Map, +) -> Result, ScenarioFileError> { + let original_scenario = load_scenario_dict(original_scenario_path)?; + let mut resulting_scenario = recursive_update(&original_scenario, overriding_scenario); + resulting_scenario.remove("extends"); + Ok(resulting_scenario) +} + +fn read_yaml_mapping(scenario_file: &Path) -> Result, ScenarioFileError> { + let file = File::open(scenario_file).map_err(|source| ScenarioFileError::Io { + path: scenario_file.to_path_buf(), + source, + })?; + let value: Value = serde_yaml::from_reader(file).map_err(|source| ScenarioFileError::Yaml { + path: scenario_file.to_path_buf(), + source, + })?; + match value { + Value::Object(map) => Ok(map), + _ => Err(ScenarioFileError::NotAMapping(scenario_file.to_path_buf())), + } +} + +fn path_field<'a>( + scenario: &'a Map, + key: &'static str, +) -> Result, ScenarioFileError> { + match scenario.get(key) { + None => Ok(None), + Some(Value::String(s)) => Ok(Some(s)), + Some(_) => Err(ScenarioFileError::PathNotAString(key)), + } +} + +/// Port of `load_scenario_dict`: reads a scenario YAML file, resolves +/// `extends` (recursively, relative to this file), and makes `lang_file` +/// (unless it's a `git@` URL) and `model_file` paths relative to this +/// file's directory. +/// +/// Does not validate the keys - like Python, that happens when the dict +/// is turned into a scenario (`validate_scenario_dict`). +pub fn load_scenario_dict( + scenario_file: impl AsRef, +) -> Result, ScenarioFileError> { + let scenario_file = scenario_file.as_ref(); + let mut scenario = read_yaml_mapping(scenario_file)?; + + if let Some(extends) = path_field(&scenario, "extends")? { + let original_scenario_path = path_relative_to_file_dir(extends, scenario_file)?; + scenario = extend_scenario(&original_scenario_path, &scenario)?; + } + + // Convert path relative to scenario file if lang_file is a local file. + // (Python indexes `scenario['lang_file']` unconditionally and raises a + // `KeyError` when it's missing; here a missing `lang_file` is left for + // `validate_scenario_dict` to report.) + if let Some(lang_file) = path_field(&scenario, "lang_file")? { + if !lang_file.starts_with("git@") { + let resolved = path_relative_to_file_dir(lang_file, scenario_file)?; + scenario.insert("lang_file".into(), path_to_value(&resolved)); + } + } + + if let Some(model_file) = path_field(&scenario, "model_file")? { + let resolved = path_relative_to_file_dir(model_file, scenario_file)?; + scenario.insert("model_file".into(), path_to_value(&resolved)); + } + + Ok(scenario) +} + +fn path_to_value(path: &Path) -> Value { + Value::String(path.to_string_lossy().into_owned()) +} + +#[cfg(test)] +mod tests { + use super::*; + use serde_json::json; + + fn scenarios_dir() -> PathBuf { + PathBuf::from(env!("CARGO_MANIFEST_DIR")).join("../../tests/testdata/scenarios") + } + + fn obj(value: Value) -> Map { + match value { + Value::Object(map) => map, + _ => panic!("expected an object"), + } + } + + #[test] + fn recursive_update_merges_nested_mappings() { + let old = obj(json!({ + "a": 1, + "nested": {"keep": 1, "override": 1, "deeper": {"x": 1}}, + })); + let new = obj(json!({ + "b": 2, + "nested": {"override": 2, "deeper": {"y": 2}}, + })); + assert_eq!( + Value::Object(recursive_update(&old, &new)), + json!({ + "a": 1, + "b": 2, + "nested": {"keep": 1, "override": 2, "deeper": {"x": 1, "y": 2}}, + }) + ); + } + + #[test] + fn recursive_update_explicit_null_removes_key() { + let old = + obj(json!({"agents": {"Attacker1": {"type": "attacker"}, "Defender1": {}}, "x": 1})); + let new = obj(json!({"agents": {"Attacker1": null}, "x": null, "y": null})); + assert_eq!( + Value::Object(recursive_update(&old, &new)), + json!({"agents": {"Defender1": {}}}) + ); + } + + #[test] + fn recursive_update_non_mapping_replaces_mapping_and_vice_versa() { + let old = obj(json!({"a": {"x": 1}, "b": 1, "c": [1, 2]})); + let new = obj(json!({"a": 5, "b": {"y": 2}, "c": [3]})); + assert_eq!( + Value::Object(recursive_update(&old, &new)), + json!({"a": 5, "b": {"y": 2}, "c": [3]}) + ); + } + + #[test] + fn validate_rejects_deprecated_and_unsupported_fields() { + let base = json!({"agents": {}, "lang_file": "x", "model_file": "y"}); + assert!(validate_scenario_dict(&obj(base.clone())).is_ok()); + + let mut deprecated = obj(base.clone()); + deprecated.insert("rewards".into(), json!({})); + assert!(matches!( + validate_scenario_dict(&deprecated), + Err(ScenarioFileError::DeprecatedField(k)) if k == "rewards" + )); + + let mut unsupported = obj(base); + unsupported.insert("bogus".into(), json!(1)); + assert!(matches!( + validate_scenario_dict(&unsupported), + Err(ScenarioFileError::UnsupportedField(k)) if k == "bogus" + )); + } + + #[test] + fn validate_checks_required_fields() { + assert!(matches!( + validate_scenario_dict(&obj(json!({"lang_file": "x", "model_file": "y"}))), + Err(ScenarioFileError::MissingOneOf("agents", "agent_settings")) + )); + assert!(matches!( + validate_scenario_dict(&obj(json!({"agents": {}, "model_file": "y"}))), + Err(ScenarioFileError::MissingField("lang_file")) + )); + assert!(matches!( + validate_scenario_dict(&obj(json!({ + "agents": {}, "lang_file": "x", "model_file": "y", "model": {} + }))), + Err(ScenarioFileError::BothOf("model_file", "model")) + )); + } + + #[test] + fn load_resolves_paths_relative_to_scenario_file() { + let scenario = + load_scenario_dict(scenarios_dir().join("traininglang_scenario.yml")).unwrap(); + let lang_file = PathBuf::from(scenario["lang_file"].as_str().unwrap()); + let model_file = PathBuf::from(scenario["model_file"].as_str().unwrap()); + assert!(lang_file.is_absolute() && lang_file.exists()); + assert!(model_file.is_absolute() && model_file.exists()); + assert!(lang_file.ends_with("langs/org.mal-lang.trainingLang-1.0.0.mar")); + } + + #[test] + fn load_keeps_git_lang_file_untouched() { + let scenario = + load_scenario_dict(scenarios_dir().join("simple_scenario_git_url.yml")).unwrap(); + assert!(scenario["lang_file"].as_str().unwrap().starts_with("git@")); + } + + #[test] + fn extend_scenario_overrides_rewards() { + // Port of `test_extend_scenario`'s dict-level half. + let scenario = + load_scenario_dict(scenarios_dir().join("traininglang_scenario_extended.yml")).unwrap(); + assert!(!scenario.contains_key("extends")); + let agents = scenario["agents"].as_object().unwrap(); + assert_eq!(agents.len(), 2); + let defender_rewards = &agents["Defender1"]["rewards"]["by_asset_name"]; + assert_eq!(defender_rewards["Host:0"]["notPresent"], json!(1)); + assert_eq!(defender_rewards["Data:2"]["modify"], json!(1)); + assert!(PathBuf::from(scenario["lang_file"].as_str().unwrap()).exists()); + } + + #[test] + fn extend_scenario_deeper_removes_null_agent() { + // Port of `test_extend_scenario_deeper`'s dict-level half. + let scenario = load_scenario_dict( + scenarios_dir().join("sub/traininglang_scenario_extended_again.yml"), + ) + .unwrap(); + let agents = scenario["agents"].as_object().unwrap(); + assert_eq!(agents.keys().collect::>(), vec!["Defender1"]); + assert!(PathBuf::from(scenario["lang_file"].as_str().unwrap()).exists()); + assert!(PathBuf::from(scenario["model_file"].as_str().unwrap()).exists()); + } + + #[test] + fn extend_scenario_override_lang_model_resolves_against_overriding_file() { + let scenario = load_scenario_dict( + scenarios_dir().join("sub/traininglang_scenario_override_lang_model.yml"), + ) + .unwrap(); + let lang_file = PathBuf::from(scenario["lang_file"].as_str().unwrap()); + let model_file = PathBuf::from(scenario["model_file"].as_str().unwrap()); + assert!(lang_file.exists() && model_file.exists()); + // Relative to `sub/`, not to the extended scenario's directory. + assert!(lang_file.starts_with(scenarios_dir().canonicalize().unwrap().join("sub"))); + assert_eq!(scenario["agents"].as_object().unwrap().len(), 2); + } + + /// Every `.yml` under `tests/testdata/scenarios`, recursively. + fn fixture_paths(dir: &Path, out: &mut Vec) { + for entry in std::fs::read_dir(dir).unwrap() { + let path = entry.unwrap().path(); + if path.is_dir() { + fixture_paths(&path, out); + } else if path.extension().and_then(|e| e.to_str()) == Some("yml") { + out.push(path); + } + } + } + + #[test] + fn every_fixture_loads_and_validates_like_python() { + // These three still use the deprecated top-level `rewards` key; + // Python's `_validate_scenario_dict` rejects them the same way. + const DEPRECATED_FORMAT: [&str; 3] = [ + "no_entry_points_simple_scenario.yml", + "no_existing_attacker_in_model_scenario.yml", + "run_demo_scenario.yml", + ]; + let mut paths = Vec::new(); + fixture_paths(&scenarios_dir(), &mut paths); + assert!(paths.len() > 30); + for path in paths { + let scenario = + load_scenario_dict(&path).unwrap_or_else(|e| panic!("{}: {e}", path.display())); + let file_name = path.file_name().unwrap().to_str().unwrap(); + let result = validate_scenario_dict(&scenario); + if DEPRECATED_FORMAT.contains(&file_name) { + assert!( + matches!(result, Err(ScenarioFileError::DeprecatedField(ref k)) if k == "rewards"), + "{file_name}: {result:?}" + ); + } else { + result.unwrap_or_else(|e| panic!("{}: {e}", path.display())); + } + } + } +} diff --git a/core/malsim-core/src/scenario/mod.rs b/core/malsim-core/src/scenario/mod.rs new file mode 100644 index 00000000..73de4e43 --- /dev/null +++ b/core/malsim-core/src/scenario/mod.rs @@ -0,0 +1,394 @@ +//! Rust-only scenario loading - the independent Rust implementation of +//! `python/malsim/scenario/` + `python/malsim/config/` used by an embedding +//! Rust program with no Python runtime (`PORTING_NOTES.md` §2.6, §7). The +//! PyO3 path never calls into this module: Python keeps its own `Scenario` +//! and flattens settings itself (§2.4). + +pub mod agent_settings; +pub mod flatten; +pub mod loading; +pub mod node_property_rule; + +pub use agent_settings::{ + agent_settings_from_dict, AgentSettings, AgentSettingsError, AgentType, AttackerSettings, + DefenderSettings, EntryPoints, +}; +pub use flatten::{flatten_attacker_settings, flatten_defender_settings, get_entry_points}; +pub use loading::{ + load_scenario_dict, recursive_update, validate_scenario_dict, ScenarioFileError, +}; +pub use node_property_rule::{NodePropertyRule, NodePropertyRuleError, RuleValue, StepValues}; + +use std::cell::RefCell; +use std::fmt; +use std::path::{Path, PathBuf}; +use std::rc::Rc; + +use maltoolbox_attackgraph::{AttackGraph, AttackGraphNodeId}; +use maltoolbox_language::LanguageGraph; +use maltoolbox_model::Model; +use rand::Rng; +use serde_json::{Map, Value}; + +use crate::settings::{FlatAgentSettings, MalSimulatorSettings, SettingsError}; + +#[derive(Debug)] +pub enum ScenarioError { + File(ScenarioFileError), + Settings(SettingsError), + AgentSettings(AgentSettingsError), + /// The scenario's `agents` field is missing or not a mapping. (Python + /// accepts `agent_settings` in validation but then reads `agents`, + /// raising `KeyError` - see `PORTING_NOTES.md` §12.) + InvalidAgents(String), + /// `lang_file` could not be loaded into a `LanguageGraph`. + Language { + lang_file: String, + message: String, + }, + /// `model`/`model_file` could not be loaded into a `Model`. + Model(String), + /// The attack graph could not be built from the model. + AttackGraph(String), +} + +impl fmt::Display for ScenarioError { + fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { + match self { + ScenarioError::File(e) => write!(f, "{e}"), + ScenarioError::Settings(e) => write!(f, "{e}"), + ScenarioError::AgentSettings(e) => write!(f, "{e}"), + ScenarioError::InvalidAgents(msg) => write!(f, "Invalid scenario agents: {msg}"), + ScenarioError::Language { lang_file, message } => { + write!(f, "Could not load language '{lang_file}': {message}") + } + ScenarioError::Model(msg) => write!(f, "Could not load model: {msg}"), + ScenarioError::AttackGraph(msg) => write!(f, "Could not build attack graph: {msg}"), + } + } +} + +impl std::error::Error for ScenarioError { + fn source(&self) -> Option<&(dyn std::error::Error + 'static)> { + match self { + ScenarioError::File(e) => Some(e), + ScenarioError::Settings(e) => Some(e), + ScenarioError::AgentSettings(e) => Some(e), + _ => None, + } + } +} + +impl From for ScenarioError { + fn from(e: ScenarioFileError) -> Self { + ScenarioError::File(e) + } +} + +impl From for ScenarioError { + fn from(e: SettingsError) -> Self { + ScenarioError::Settings(e) + } +} + +impl From for ScenarioError { + fn from(e: AgentSettingsError) -> Self { + ScenarioError::AgentSettings(e) + } +} + +/// Port of `Scenario`: a language, a model, the attack graph built from +/// them, agent settings (attackers with entry points/goals already +/// resolved to node ids) and simulator settings. +/// +/// The graph and model are held as `Rc>`, the handle types +/// `Simulator` takes (§2.2), so a loaded scenario plugs straight into a +/// simulator. +pub struct Scenario { + /// `lang_file` as given (made absolute by `load_from_file`). + pub lang_file: String, + /// The model file, if the model was given as `model_file` rather than + /// inline (Python's `_model_file`). + pub model_file: Option, + pub lang_graph: Rc, + pub model: Rc>, + pub attack_graph: Rc>, + /// Attackers first, then defenders, each in scenario-file order (same + /// as Python's `Scenario.agent_settings`). Attacker entry points/goals + /// are the node ids at load time; a dyna episode whose model effects + /// remove their asset regenerates those nodes with new ids on restore, + /// which is why `flatten_agents` re-resolves them by full name. + pub agent_settings: Vec>, + pub sim_settings: MalSimulatorSettings, + /// The attackers' settings with entry points/goals as full names, in + /// the same order as in `agent_settings`. + attacker_settings_by_full_name: Vec>, +} + +/// Port of `LanguageGraph.load_from_file` (`.mal`, `.mar`, `.yml`/`.yaml`, +/// `.json`). Python's `.git` URL support is not ported (§12). +fn load_language_graph(lang_file: &str) -> Result { + let language_error = |message: String| ScenarioError::Language { + lang_file: lang_file.to_string(), + message, + }; + if lang_file.ends_with(".git") { + return Err(language_error( + "loading a language from a git URL is not supported by the Rust scenario loader".into(), + )); + } + maltoolbox_language::load_language_graph_from_file(lang_file) + .map_err(|e| language_error(e.to_string())) +} + +impl Scenario { + /// Port of `Scenario.__init__`: loads the language and model, builds the + /// attack graph and resolves attacker entry points/goals against it. + pub fn new( + lang_file: &str, + model: ModelSource, + agents: Vec>, + sim_settings: MalSimulatorSettings, + ) -> Result { + let lang_graph = Rc::new(load_language_graph(lang_file)?); + + let (model, model_file) = match model { + ModelSource::File(path) => { + let model = maltoolbox_model::load_from_file(&path, lang_graph.clone()) + .map_err(|e| ScenarioError::Model(e.to_string()))?; + (model, Some(path)) + } + ModelSource::Dict(dict) => { + let model = maltoolbox_model::from_dict(&dict, lang_graph.clone()) + .map_err(|e| ScenarioError::Model(e.to_string()))?; + (model, None) + } + }; + + let attack_graph = AttackGraph::from_model(&model) + .map_err(|e| ScenarioError::AttackGraph(e.to_string()))?; + + let mut attackers = Vec::new(); + let mut defenders = Vec::new(); + let mut attacker_settings_by_full_name = Vec::new(); + for agent in agents { + match agent { + AgentSettings::Attacker(a) => { + attackers.push(AgentSettings::Attacker( + a.convert_to_attack_graph_nodes(&attack_graph)?, + )); + attacker_settings_by_full_name.push(a); + } + AgentSettings::Defender(d) => defenders.push(AgentSettings::Defender(d)), + } + } + attackers.extend(defenders); + + Ok(Scenario { + lang_file: lang_file.to_string(), + model_file, + lang_graph, + model: Rc::new(RefCell::new(model)), + attack_graph: Rc::new(RefCell::new(attack_graph)), + agent_settings: attackers, + sim_settings, + attacker_settings_by_full_name, + }) + } + + /// Port of `Scenario.from_dict`: validates the dict, parses agents + /// (skipping `null` ones) and `sim_settings`, then builds the scenario. + pub fn from_dict(scenario: &Map) -> Result { + validate_scenario_dict(scenario)?; + + let agents = match scenario.get("agents") { + Some(Value::Object(agents)) => agents, + Some(other) => { + return Err(ScenarioError::InvalidAgents(format!( + "expected a mapping, got {other}" + ))) + } + None => return Err(ScenarioError::InvalidAgents("'agents' is required".into())), + }; + let agent_settings = agents + .iter() + .filter(|(_, d)| !d.is_null()) + .map(|(name, d)| agent_settings_from_dict(name, d)) + .collect::, _>>()?; + + // `scenario_dict.get('model') or scenario_dict['model_file']` + let model = match scenario.get("model") { + Some(model) if is_truthy(model) => ModelSource::Dict(model.clone()), + _ => match scenario.get("model_file") { + Some(Value::String(path)) => ModelSource::File(PathBuf::from(path)), + _ => return Err(ScenarioFileError::MissingOneOf("model_file", "model").into()), + }, + }; + + let sim_settings = match scenario.get("sim_settings") { + None | Some(Value::Null) => MalSimulatorSettings::default(), + Some(v) => MalSimulatorSettings::from_value(v)?, + }; + + let lang_file = scenario + .get("lang_file") + .and_then(Value::as_str) + .ok_or(ScenarioFileError::PathNotAString("lang_file"))?; + + Scenario::new(lang_file, model, agent_settings, sim_settings) + } + + /// Port of `Scenario.load_from_file` (without `**override_keys`; apply + /// overrides to `load_scenario_dict`'s result and call `from_dict`). + pub fn load_from_file(scenario_file: impl AsRef) -> Result { + Scenario::from_dict(&load_scenario_dict(scenario_file)?) + } + + /// Port of `Scenario.to_dict`: the scenario in the shape + /// `from_dict` reads. Differences from Python: `sim_settings` is + /// written (Python drops it), as are `reward_mode` and + /// `ttc_overrides` per agent, so a saved scenario reloads identically. + /// A local `lang_file`/`model_file` is written as an absolute path + /// (`load_scenario_dict` leaves absolute paths alone), and a model + /// given inline is written inline. + pub fn to_dict(&self) -> Result, ScenarioError> { + let mut attackers = self.attacker_settings_by_full_name.iter(); + let mut agents = Map::new(); + for agent in &self.agent_settings { + let value = match agent { + AgentSettings::Attacker(_) => attackers + .next() + .expect("one by-full-name entry per attacker") + .to_value(), + AgentSettings::Defender(d) => d.to_value(), + }; + agents.insert(agent.name().to_string(), value); + } + + let mut dict = Map::new(); + dict.insert("lang_file".into(), absolute_path_value(&self.lang_file)?); + dict.insert("agents".into(), Value::Object(agents)); + dict.insert("sim_settings".into(), self.sim_settings.to_value()); + match &self.model_file { + Some(model_file) => { + let model_file = model_file.to_string_lossy(); + dict.insert("model_file".into(), absolute_path_value(&model_file)?); + } + None => { + dict.insert("model".into(), self.model.borrow().to_dict()); + } + } + Ok(dict) + } + + /// Port of `Scenario.save_to_file`: writes `to_dict` as YAML. The + /// result loads back with `load_from_file`. + pub fn save_to_file(&self, path: impl AsRef) -> Result<(), ScenarioError> { + let path = path.as_ref(); + let yaml = serde_yaml::to_string(&Value::Object(self.to_dict()?)).map_err(|source| { + ScenarioFileError::Yaml { + path: path.to_path_buf(), + source, + } + })?; + std::fs::write(path, yaml).map_err(|source| ScenarioFileError::Io { + path: path.to_path_buf(), + source, + })?; + Ok(()) + } + + /// Port of the `attacker_settings` property, in scenario order. + pub fn attacker_settings(&self) -> impl Iterator> { + self.agent_settings.iter().filter_map(|a| match a { + AgentSettings::Attacker(a) => Some(a), + AgentSettings::Defender(_) => None, + }) + } + + /// Port of the `defender_settings` property, in scenario order. + pub fn defender_settings(&self) -> impl Iterator { + self.agent_settings.iter().filter_map(|a| match a { + AgentSettings::Defender(d) => Some(d), + AgentSettings::Attacker(_) => None, + }) + } + + /// The flat per-agent inputs for `Simulator::reset`, mirroring what + /// Python's `MalSimulator.reset` builds before calling native: one + /// entry-point set sampled per attacker (attackers first, in order), + /// then every agent's settings flattened against the current graph. + /// + /// Attacker entry points/goals are re-resolved by full name against the + /// current graph on every call (as Python's `dyna_reset` does), so + /// nodes regenerated by a dyna restore are picked up. For a + /// `DynaSimulator`, call `DynaSimulator::restore_model` before this on every + /// reset after the first, so the rules and names see the restored + /// graph. A name only fails to resolve (`AgentSettingsError:: + /// UnknownNode`) if that restore was skipped and its node is gone. + pub fn flatten_agents( + &self, + rng: &mut impl Rng, + ) -> Result, AgentSettingsError> { + let graph = self.attack_graph.borrow(); + let model = self.model.borrow(); + let mut flat = Vec::with_capacity(self.agent_settings.len()); + for attacker in &self.attacker_settings_by_full_name { + let attacker = attacker.convert_to_attack_graph_nodes(&graph)?; + let entry_points = get_entry_points(&attacker, rng); + flat.push(( + attacker.name.clone(), + FlatAgentSettings::Attacker(flatten_attacker_settings( + &attacker, + &graph, + &model, + &entry_points, + )), + )); + } + for defender in self.defender_settings() { + flat.push(( + defender.name.clone(), + FlatAgentSettings::Defender(flatten_defender_settings(defender, &graph, &model)), + )); + } + Ok(flat) + } +} + +/// Where `Scenario::new` gets its model from (Python's `model: Model | +/// dict | str`; an already-built `Model` is not accepted since the model +/// must share the scenario's freshly loaded `LanguageGraph`). +#[derive(Debug, Clone)] +pub enum ModelSource { + File(PathBuf), + Dict(Value), +} + +/// A local path made absolute so it doesn't change meaning when the saved +/// file lives in another directory; `git@` language URLs are kept as is. +fn absolute_path_value(path: &str) -> Result { + if path.starts_with("git@") { + return Ok(Value::String(path.to_string())); + } + let absolute = std::path::absolute(path).map_err(|source| ScenarioFileError::Io { + path: PathBuf::from(path), + source, + })?; + Ok(Value::String(absolute.to_string_lossy().into_owned())) +} + +/// Python truthiness of a JSON value, for the `get(..) or ..` idioms. +fn is_truthy(value: &Value) -> bool { + match value { + Value::Null => false, + Value::Bool(b) => *b, + Value::Number(n) => n.as_f64() != Some(0.0), + Value::String(s) => !s.is_empty(), + Value::Array(a) => !a.is_empty(), + Value::Object(o) => !o.is_empty(), + } +} + +#[cfg(test)] +mod tests; diff --git a/core/malsim-core/src/scenario/node_property_rule.rs b/core/malsim-core/src/scenario/node_property_rule.rs new file mode 100644 index 00000000..78813c52 --- /dev/null +++ b/core/malsim-core/src/scenario/node_property_rule.rs @@ -0,0 +1,647 @@ +//! Rust port of `python/malsim/config/node_property_rule.py` for the +//! Rust-only scenario path - see `PORTING_NOTES.md` §2.6, §7 C1. +//! +//! This is a *second, independent* implementation of the +//! `by_asset_type`/`by_asset_name` matching: the PyO3 path never uses it +//! (Python resolves its own `NodePropertyRule` and hands flat id maps +//! across, §2.4). Both implementations target the same dict shape, the one +//! `NodePropertyRule.from_dict()`/`.to_dict()` read and write: +//! +//! ```yaml +//! by_asset_type: +//! Host: {access: 0.5} # step name -> value +//! User: [compromise] # list form: listed steps get `T::listed()` +//! by_asset_name: +//! Host:0: {access: 0.9} +//! ``` +//! +//! `value()` keeps Python's `by_asset_val or asset_type_val or default` +//! precedence *including* its truthiness: a falsy by-name value (`0`, +//! `false`) falls through to the by-type value instead of winning (see +//! `RuleValue::is_truthy`). + +use crate::types::NodeMap; +use std::collections::BTreeMap; +use std::fmt; + +use maltoolbox_attackgraph::{AttackGraph, AttackGraphNode}; +use maltoolbox_model::Model; +use serde_json::{Map, Value}; + +use crate::ttc::{named_ttc_dist, TtcDist}; + +#[derive(Debug, Clone, PartialEq, Eq)] +pub enum NodePropertyRuleError { + /// The rule is not a mapping. + NotAMapping, + /// Neither `by_asset_type` nor `by_asset_name` was given (Python's + /// "Node property dict need at least 'by_asset_type' or 'by_asset_name'"). + MissingFields, + /// Keys other than `by_asset_type`/`by_asset_name` were given. + ForbiddenFields(Vec), + /// A `by_asset_*` entry has the wrong shape (not a mapping of asset -> + /// list/mapping of steps). + InvalidShape(String), + /// A step value could not be parsed as the rule's value type. + InvalidValue { + asset: String, + step: String, + reason: String, + }, + /// List form was used for a value type that has no list-form meaning + /// (e.g. TTC overrides). + ListFormNotAllowed { asset: String }, +} + +impl fmt::Display for NodePropertyRuleError { + fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { + match self { + NodePropertyRuleError::NotAMapping => { + write!(f, "Node property rule must be a mapping") + } + NodePropertyRuleError::MissingFields => write!( + f, + "Node property dict need at least 'by_asset_type' or 'by_asset_name'" + ), + NodePropertyRuleError::ForbiddenFields(fields) => { + write!(f, "Node property fields not allowed: {fields:?}") + } + NodePropertyRuleError::InvalidShape(msg) => { + write!(f, "Invalid node property rule shape: {msg}") + } + NodePropertyRuleError::InvalidValue { + asset, + step, + reason, + } => { + write!( + f, + "Invalid node property value for '{asset}'/'{step}': {reason}" + ) + } + NodePropertyRuleError::ListFormNotAllowed { asset } => write!( + f, + "List form is not allowed for this node property (asset '{asset}')" + ), + } + } +} + +impl std::error::Error for NodePropertyRuleError {} + +/// A value type a `NodePropertyRule` can map nodes to. Captures the three +/// places Python's dynamic typing leaks into `NodePropertyRule`'s +/// behavior: how a YAML value is read, what a list-form entry means +/// (`_lookup` returns `True` for list membership), and Python truthiness +/// (which decides precedence fall-through in `value()`). +pub trait RuleValue: Sized + Clone { + /// The value a list-form entry stands for, or `None` if this type has + /// no list form. + fn listed() -> Option; + + /// Parse one step value from the scenario dict. + fn from_rule_value(value: &Value) -> Result; + + /// Serialize back to the scenario dict shape (`to_dict()`). + fn to_rule_value(&self) -> Value; + + /// Python truthiness of the value. + fn is_truthy(&self) -> bool; +} + +impl RuleValue for bool { + fn listed() -> Option { + Some(true) + } + + fn from_rule_value(value: &Value) -> Result { + value + .as_bool() + .ok_or_else(|| format!("expected a boolean, got {value}")) + } + + fn to_rule_value(&self) -> Value { + Value::Bool(*self) + } + + fn is_truthy(&self) -> bool { + *self + } +} + +impl RuleValue for f64 { + /// Python's list form yields `True`, which is `1` numerically. + fn listed() -> Option { + Some(1.0) + } + + /// Accepts ints, floats and (like Python, where `True == 1`) booleans. + fn from_rule_value(value: &Value) -> Result { + match value { + Value::Bool(b) => Ok(if *b { 1.0 } else { 0.0 }), + _ => value + .as_f64() + .ok_or_else(|| format!("expected a number, got {value}")), + } + } + + fn to_rule_value(&self) -> Value { + serde_json::json!(*self) + } + + fn is_truthy(&self) -> bool { + *self != 0.0 + } +} + +/// TTC overrides (`ttc_overrides`) name a predefined distribution +/// (`TTCDist.from_name`), the only form the Python path accepts - see +/// `native_settings.py::_flatten_ttc_dists`. +impl RuleValue for TtcDist { + fn listed() -> Option { + None + } + + fn from_rule_value(value: &Value) -> Result { + let name = value + .as_str() + .ok_or_else(|| format!("expected a TTC distribution name, got {value}"))?; + named_ttc_dist(name).ok_or_else(|| format!("unknown TTC distribution name '{name}'")) + } + + fn to_rule_value(&self) -> Value { + self.to_dict() + } + + /// A Python `TTCDist` object (and a non-empty name) is always truthy. + fn is_truthy(&self) -> bool { + true + } +} + +/// The steps configured for one asset type/name: either the list form +/// (`[read, write]`) or the mapping form (`{read: 0.5}`). +#[derive(Debug, Clone, PartialEq)] +pub enum StepValues { + Listed(Vec), + Valued(BTreeMap), +} + +impl StepValues { + /// Port of `_lookup`'s per-asset half: `True` (here `T::listed()`) for + /// list membership, the mapped value otherwise. + fn lookup(&self, step_name: &str) -> Option { + match self { + StepValues::Listed(steps) => { + if steps.iter().any(|s| s == step_name) { + T::listed() + } else { + None + } + } + StepValues::Valued(values) => values.get(step_name).cloned(), + } + } + + fn len(&self) -> usize { + match self { + StepValues::Listed(steps) => steps.len(), + StepValues::Valued(values) => values.len(), + } + } + + fn to_value(&self) -> Value { + match self { + StepValues::Listed(steps) => { + Value::Array(steps.iter().cloned().map(Value::String).collect()) + } + StepValues::Valued(values) => Value::Object( + values + .iter() + .map(|(step, v)| (step.clone(), v.to_rule_value())) + .collect(), + ), + } + } +} + +type ByAsset = BTreeMap>; + +/// Port of `NodePropertyRule`. Python's `default` dataclass field is not +/// ported: `value()` never reads it (it uses its `default` *argument*), +/// so it's dead state - see `PORTING_NOTES.md` §12. +#[derive(Debug, Clone, PartialEq)] +pub struct NodePropertyRule { + pub by_asset_type: Option>, + pub by_asset_name: Option>, +} + +impl NodePropertyRule { + /// Port of `NodePropertyRule.__init__` + `__post_init__`. + pub fn new( + by_asset_type: Option>, + by_asset_name: Option>, + ) -> Result { + if by_asset_type.is_none() && by_asset_name.is_none() { + return Err(NodePropertyRuleError::MissingFields); + } + Ok(NodePropertyRule { + by_asset_type, + by_asset_name, + }) + } + + /// Port of `from_dict()` / `_validate_dict()`. + pub fn from_value(value: &Value) -> Result { + let obj = value + .as_object() + .ok_or(NodePropertyRuleError::NotAMapping)?; + + const ALLOWED_FIELDS: [&str; 2] = ["by_asset_type", "by_asset_name"]; + if !ALLOWED_FIELDS.iter().any(|f| obj.contains_key(*f)) { + return Err(NodePropertyRuleError::MissingFields); + } + let mut forbidden: Vec = obj + .keys() + .filter(|k| !ALLOWED_FIELDS.contains(&k.as_str())) + .cloned() + .collect(); + if !forbidden.is_empty() { + forbidden.sort(); + return Err(NodePropertyRuleError::ForbiddenFields(forbidden)); + } + + Self::new( + parse_by_asset(obj.get("by_asset_type"))?, + parse_by_asset(obj.get("by_asset_name"))?, + ) + } + + /// Port of `from_optional_dict()`: an absent or `null` rule is `None`. + pub fn from_optional_value( + value: Option<&Value>, + ) -> Result, NodePropertyRuleError> { + match value { + None | Some(Value::Null) => Ok(None), + Some(v) => Self::from_value(v).map(Some), + } + } + + /// Port of `to_dict()`. + pub fn to_value(&self) -> Value { + let by_asset_to_value = |by_asset: &Option>| match by_asset { + None => Value::Null, + Some(by_asset) => Value::Object( + by_asset + .iter() + .map(|(asset, steps)| (asset.clone(), steps.to_value())) + .collect::>(), + ), + }; + serde_json::json!({ + "by_asset_type": by_asset_to_value(&self.by_asset_type), + "by_asset_name": by_asset_to_value(&self.by_asset_name), + }) + } + + /// Port of `__len__`: the number of configured asset types + names. + /// Python callers test a rule's truthiness (`if rule:`) with this, so + /// an empty rule behaves like no rule in those places. + pub fn len(&self) -> usize { + self.by_asset_type.as_ref().map_or(0, |m| m.len()) + + self.by_asset_name.as_ref().map_or(0, |m| m.len()) + } + + pub fn is_empty(&self) -> bool { + self.len() == 0 + } + + /// Port of `value(node, default)`. Returns `None` where Python would + /// return `default`, so `rule.value(..).unwrap_or(default)` is the + /// exact Python call. Precedence: asset_name > asset_type, and a falsy + /// value falls through like Python's `or` chain. + pub fn value(&self, node: &AttackGraphNode, model: &Model) -> Option { + let asset = model.get_asset_by_id(node.model_asset?)?; + + let lookup = |by_asset: &Option>, key: &str| { + by_asset + .as_ref() + .and_then(|m| m.get(key)) + .and_then(|steps| steps.lookup(&node.name)) + .filter(RuleValue::is_truthy) + }; + + lookup(&self.by_asset_name, &asset.name) + .or_else(|| lookup(&self.by_asset_type, &asset.asset_type)) + } + + /// Port of `per_node()`, keyed by node id instead of full name. Like + /// Python (`value(n, None)`, keep non-`None`), only nodes with a + /// truthy value are included. + pub fn per_node(&self, graph: &AttackGraph, model: &Model) -> NodeMap { + graph + .nodes + .iter() + .filter_map(|(id, node)| self.value(node, model).map(|v| (id, v))) + .collect() + } + + /// Number of configured steps across all assets, used only by tests + /// and diagnostics. + pub fn num_steps(&self) -> usize { + [&self.by_asset_type, &self.by_asset_name] + .into_iter() + .flatten() + .flat_map(|m| m.values()) + .map(StepValues::len) + .sum() + } +} + +fn parse_by_asset( + value: Option<&Value>, +) -> Result>, NodePropertyRuleError> { + let obj = match value { + None | Some(Value::Null) => return Ok(None), + Some(Value::Object(obj)) => obj, + Some(other) => { + return Err(NodePropertyRuleError::InvalidShape(format!( + "expected a mapping of asset to steps, got {other}" + ))) + } + }; + + obj.iter() + .map(|(asset, steps)| Ok((asset.clone(), parse_step_values(asset, steps)?))) + .collect::, _>>() + .map(Some) +} + +fn parse_step_values( + asset: &str, + steps: &Value, +) -> Result, NodePropertyRuleError> { + match steps { + Value::Array(items) => { + if T::listed().is_none() { + return Err(NodePropertyRuleError::ListFormNotAllowed { + asset: asset.to_string(), + }); + } + items + .iter() + .map(|item| { + item.as_str().map(str::to_string).ok_or_else(|| { + NodePropertyRuleError::InvalidShape(format!( + "step list for '{asset}' must contain strings, got {item}" + )) + }) + }) + .collect::, _>>() + .map(StepValues::Listed) + } + Value::Object(values) => values + .iter() + // An explicit `null` value behaves as absent in Python + // (`_lookup` returns it, the `or` chain skips it). + .filter(|(_, v)| !v.is_null()) + .map(|(step, v)| { + T::from_rule_value(v) + .map(|parsed| (step.clone(), parsed)) + .map_err(|reason| NodePropertyRuleError::InvalidValue { + asset: asset.to_string(), + step: step.clone(), + reason, + }) + }) + .collect::, _>>() + .map(StepValues::Valued), + other => Err(NodePropertyRuleError::InvalidShape(format!( + "steps for '{asset}' must be a list or a mapping, got {other}" + ))), + } +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::test_fixtures::wiper_attack_graph; + use serde_json::json; + + fn node<'a>(graph: &'a AttackGraph, full_name: &str) -> &'a AttackGraphNode { + &graph.nodes[graph.get_node_by_full_name(full_name).unwrap()] + } + + fn full_names(graph: &AttackGraph, model: &Model, per_node: &NodeMap) -> Vec { + let mut names: Vec = per_node + .keys() + .map(|&id| graph.full_name_of(id, Some(model))) + .collect(); + names.sort(); + names + } + + #[test] + fn by_asset_name_beats_by_asset_type() { + let (graph, model) = wiper_attack_graph(); + let rule = NodePropertyRule::::from_value(&json!({ + "by_asset_type": {"Device": {"infect": 0.5}}, + "by_asset_name": {"InfectedDevice": {"infect": 0.9}}, + })) + .unwrap(); + + assert_eq!( + rule.value(node(&graph, "InfectedDevice:infect"), &model), + Some(0.9) + ); + assert_eq!( + rule.value(node(&graph, "VulnerableDevice:infect"), &model), + Some(0.5) + ); + } + + #[test] + fn unmatched_node_falls_back_to_default() { + let (graph, model) = wiper_attack_graph(); + let rule = NodePropertyRule::::from_value(&json!({ + "by_asset_type": {"Device": {"infect": 0.5}}, + })) + .unwrap(); + + let data_read = node(&graph, "InfectedData:read"); + assert_eq!(rule.value(data_read, &model), None); + assert_eq!(rule.value(data_read, &model).unwrap_or(0.0), 0.0); + } + + #[test] + fn falsy_by_asset_name_value_falls_through_to_by_asset_type() { + // Python: `by_asset_val or asset_type_val or default` - a 0 reward + // by name does not override the by-type value. + let (graph, model) = wiper_attack_graph(); + let rule = NodePropertyRule::::from_value(&json!({ + "by_asset_type": {"Device": {"infect": 0.5}}, + "by_asset_name": {"InfectedDevice": {"infect": 0}}, + })) + .unwrap(); + assert_eq!( + rule.value(node(&graph, "InfectedDevice:infect"), &model), + Some(0.5) + ); + + let bool_rule = NodePropertyRule::::from_value(&json!({ + "by_asset_name": {"InfectedDevice": {"infect": false}}, + })) + .unwrap(); + assert_eq!( + bool_rule.value(node(&graph, "InfectedDevice:infect"), &model), + None + ); + } + + #[test] + fn list_form_means_listed_value() { + let (graph, model) = wiper_attack_graph(); + let rule = NodePropertyRule::::from_value(&json!({ + "by_asset_type": {"Device": ["infect"]}, + "by_asset_name": {"InfectedData": ["read"]}, + })) + .unwrap(); + + let per_node = rule.per_node(&graph, &model); + assert_eq!( + full_names(&graph, &model, &per_node), + vec![ + "InfectedData:read", + "InfectedDevice:infect", + "VulnerableDevice:infect" + ] + ); + assert!(per_node.values().all(|v| *v)); + + let reward_rule = NodePropertyRule::::from_value(&json!({ + "by_asset_type": {"Device": ["infect"]}, + })) + .unwrap(); + assert_eq!( + reward_rule.value(node(&graph, "VulnerableDevice:infect"), &model), + Some(1.0) + ); + } + + #[test] + fn per_node_only_contains_truthy_values() { + let (graph, model) = wiper_attack_graph(); + let rule = NodePropertyRule::::from_value(&json!({ + "by_asset_type": {"Device": {"infect": 0.0}}, + "by_asset_name": {"InfectedData": {"read": 2}}, + })) + .unwrap(); + let per_node = rule.per_node(&graph, &model); + assert_eq!( + full_names(&graph, &model, &per_node), + vec!["InfectedData:read"] + ); + assert_eq!(per_node.values().copied().collect::>(), vec![2.0]); + } + + #[test] + fn forbidden_or_missing_fields_are_rejected() { + // Port of `test_apply_scenario_observability_faulty`. + assert_eq!( + NodePropertyRule::::from_value(&json!({ + "NotAllowedKey": {"Data": ["read", "write", "delete"]} + })), + Err(NodePropertyRuleError::MissingFields) + ); + assert_eq!( + NodePropertyRule::::from_value(&json!({ + "by_asset_type": {"Data": ["read"]}, + "NotAllowedKey": {}, + })), + Err(NodePropertyRuleError::ForbiddenFields(vec![ + "NotAllowedKey".into() + ])) + ); + // Both fields present but `null` - Python's `__post_init__` check. + assert_eq!( + NodePropertyRule::::from_value(&json!({"by_asset_type": null})), + Err(NodePropertyRuleError::MissingFields) + ); + } + + #[test] + fn old_rewards_format_is_rejected() { + // Port of `test_apply_scenario_rewards_old_format`. + let old_format = json!({ + "OS App:notPresent": 2, + "Data:5:notPresent": 1, + }); + assert!(NodePropertyRule::::from_value(&old_format).is_err()); + } + + #[test] + fn from_optional_value_maps_absent_and_null_to_none() { + assert_eq!(NodePropertyRule::::from_optional_value(None), Ok(None)); + assert_eq!( + NodePropertyRule::::from_optional_value(Some(&Value::Null)), + Ok(None) + ); + } + + #[test] + fn ttc_rule_parses_named_distributions_and_rejects_list_form() { + let (graph, model) = wiper_attack_graph(); + let rule = NodePropertyRule::::from_value(&json!({ + "by_asset_type": {"Device": {"infect": "HardAndUncertain"}}, + })) + .unwrap(); + assert_eq!( + rule.value(node(&graph, "InfectedDevice:infect"), &model), + named_ttc_dist("HardAndUncertain") + ); + + assert_eq!( + NodePropertyRule::::from_value(&json!({ + "by_asset_type": {"Device": ["infect"]}, + })), + Err(NodePropertyRuleError::ListFormNotAllowed { + asset: "Device".into() + }) + ); + assert!(matches!( + NodePropertyRule::::from_value(&json!({ + "by_asset_type": {"Device": {"infect": "NotADistribution"}}, + })), + Err(NodePropertyRuleError::InvalidValue { .. }) + )); + } + + #[test] + fn to_value_round_trips() { + let original = json!({ + "by_asset_type": {"Device": ["infect"], "Data": {"read": true}}, + "by_asset_name": null, + }); + let rule = NodePropertyRule::::from_value(&original).unwrap(); + assert_eq!(rule.to_value(), original); + assert_eq!(rule.len(), 2); + assert_eq!(rule.num_steps(), 2); + } + + #[test] + fn node_without_model_asset_gets_default() { + let (mut graph, model) = wiper_attack_graph(); + let step_id = graph.nodes[graph + .get_node_by_full_name("InfectedDevice:infect") + .unwrap()] + .lg_attack_step; + let orphan = graph + .add_node(step_id, None, None, None, None, None, None) + .unwrap(); + let rule = NodePropertyRule::::from_value(&json!({ + "by_asset_type": {"Device": ["infect"]}, + })) + .unwrap(); + assert_eq!(rule.value(&graph.nodes[orphan], &model), None); + } +} diff --git a/core/malsim-core/src/scenario/tests.rs b/core/malsim-core/src/scenario/tests.rs new file mode 100644 index 00000000..9827bf6a --- /dev/null +++ b/core/malsim-core/src/scenario/tests.rs @@ -0,0 +1,590 @@ +//! Scenario-level tests (Phase C4), ported from the scenario-building +//! cases of `tests/test_scenario.py`. Dict-level `extends`/validation +//! cases live in `loading.rs`; per-node rule semantics in +//! `node_property_rule.rs`/`flatten.rs`. + +use std::collections::{BTreeMap, BTreeSet}; +use std::path::PathBuf; + +use rand::rngs::StdRng; +use rand::SeedableRng; + +use super::*; +use crate::settings::{RewardMode, TtcMode}; +use crate::types::NodeSet; + +fn scenario_path(name: &str) -> PathBuf { + PathBuf::from(env!("CARGO_MANIFEST_DIR")) + .join("../../tests/testdata/scenarios") + .join(name) +} + +fn load(name: &str) -> Scenario { + Scenario::load_from_file(scenario_path(name)).unwrap_or_else(|e| panic!("{name}: {e}")) +} + +fn attacker<'a>(scenario: &'a Scenario, name: &str) -> &'a AttackerSettings { + scenario + .attacker_settings() + .find(|a| a.name == name) + .expect("attacker exists") +} + +fn defender<'a>(scenario: &'a Scenario, name: &str) -> &'a DefenderSettings { + scenario + .defender_settings() + .find(|d| d.name == name) + .expect("defender exists") +} + +fn node_id(scenario: &Scenario, full_name: &str) -> AttackGraphNodeId { + scenario + .attack_graph + .borrow() + .get_node_by_full_name(full_name) + .unwrap() +} + +/// Full names of the nodes a rule's `per_node` maps, with their values. +fn per_node_by_name( + scenario: &Scenario, + rule: &NodePropertyRule, +) -> BTreeMap { + let graph = scenario.attack_graph.borrow(); + let model = scenario.model.borrow(); + rule.per_node(&graph, &model) + .into_iter() + .map(|(id, v)| (graph.full_name_of(id, Some(&model)), v)) + .collect() +} + +fn valued(rule: &NodePropertyRule, asset: &str, step: &str) -> f64 { + match &rule.by_asset_name.as_ref().unwrap()[asset] { + StepValues::Valued(values) => values[step], + StepValues::Listed(_) => panic!("expected mapping form"), + } +} + +#[test] +fn load_scenario() { + // Port of `test_load_scenario`. + let scenario = load("simple_scenario.yml"); + + let rewards = defender(&scenario, "Defender1").rewards.as_ref().unwrap(); + assert_eq!(valued(rewards, "OS App", "notPresent"), 2.0); + assert_eq!(valued(rewards, "OS App", "supplyChainAuditing"), 7.0); + assert_eq!(valued(rewards, "Program 1", "notPresent"), 3.0); + assert_eq!(valued(rewards, "Program 1", "supplyChainAuditing"), 7.0); + assert_eq!( + valued(rewards, "SoftwareVulnerability:4", "notPresent"), + 4.0 + ); + assert_eq!(valued(rewards, "Data:5", "notPresent"), 1.0); + assert_eq!(valued(rewards, "Credentials:6", "notPhishable"), 7.0); + assert_eq!(valued(rewards, "Identity:11", "notPresent"), 3.5); + + let attacker1 = attacker(&scenario, "Attacker1"); + let entry_point = node_id(&scenario, "OS App:fullAccess"); + assert_eq!( + attacker1.entry_points, + EntryPoints::Single(BTreeSet::from([entry_point])) + ); + + assert_eq!(attacker1.policy.as_deref(), Some("BreadthFirstAttacker")); + assert_eq!( + defender(&scenario, "Defender1").policy.as_deref(), + Some("PassiveAgent") + ); + assert!(scenario + .model_file + .as_ref() + .unwrap() + .ends_with("models/simple_test_model.yml")); + assert!(scenario + .lang_file + .ends_with("langs/org.mal-lang.coreLang-1.0.0.mar")); +} + +#[test] +fn extend_scenario() { + // Port of `test_extend_scenario`. + let scenario = load("traininglang_scenario_extended.yml"); + let rewards = defender(&scenario, "Defender1").rewards.as_ref().unwrap(); + let per_node = per_node_by_name(&scenario, rewards); + assert_eq!(per_node.len(), 7); + assert!(per_node.values().all(|r| *r == 1.0)); + assert_eq!(scenario.agent_settings.len(), 2); +} + +#[test] +fn extend_scenario_deeper() { + // Port of `test_extend_scenario_deeper`. + let scenario = load("sub/traininglang_scenario_extended_again.yml"); + let rewards = defender(&scenario, "Defender1").rewards.as_ref().unwrap(); + let per_node = per_node_by_name(&scenario, rewards); + assert_eq!(per_node.len(), 7); + assert!(per_node.values().all(|r| *r == 1.0)); + // Attacker1 is set to null in the final scenario. + assert_eq!(scenario.agent_settings.len(), 1); +} + +#[test] +fn extend_scenario_override_lang_model() { + // Port of `test_extend_scenario_override_lang_model`. + let scenario = load("sub/traininglang_scenario_override_lang_model.yml"); + let rewards = attacker(&scenario, "Attacker1").rewards.as_ref().unwrap(); + let graph = scenario.attack_graph.borrow(); + let model = scenario.model.borrow(); + let reward = |full_name: &str| { + rewards + .value( + &graph.nodes[graph.get_node_by_full_name(full_name).unwrap()], + &model, + ) + .unwrap() + }; + assert_eq!(reward("Host:0:notPresent"), 2.0); + assert_eq!(reward("Host:0:access"), 4.0); + assert_eq!(reward("Host:1:notPresent"), 7.0); + assert_eq!(reward("Host:1:access"), 5.0); + assert_eq!(reward("Data:2:notPresent"), 8.0); + assert_eq!(reward("Data:2:read"), 5.0); + assert_eq!(reward("Data:2:modify"), 10.0); + drop((graph, model)); + assert_eq!(scenario.agent_settings.len(), 2); +} + +#[test] +fn load_scenario_no_defender_agent() { + // Port of `test_load_scenario_no_defender_agent` (policy kept as a + // name: no agent is instantiated in Rust, §2.6). + let scenario = load("no_defender_agent_scenario.yml"); + assert_eq!(scenario.defender_settings().count(), 0); + assert_eq!( + attacker(&scenario, "attacker1").policy.as_deref(), + Some("BreadthFirstAttacker") + ); +} + +#[test] +fn load_scenario_unknown_policy_is_carried_opaquely() { + // Python's `test_load_scenario_agent_class_error` expects a + // `LookupError` here; the Rust loader has no policy registry and + // keeps the names as given (`PORTING_NOTES.md` §12, C3). + let scenario = load("wrong_agent_classes_scenario.yml"); + assert_eq!( + attacker(&scenario, "Attacker1").policy.as_deref(), + Some("BananaAttacker") + ); + assert_eq!( + defender(&scenario, "Defender1").policy.as_deref(), + Some("FishAttacker") + ); +} + +#[test] +fn load_scenario_observability_given() { + // Port of `test_load_scenario_observability_given`. + let scenario = load("simple_filtered_observability_scenario.yml"); + let observable = defender(&scenario, "Defender1") + .observable_steps + .as_ref() + .unwrap(); + let per_node = per_node_by_name(&scenario, observable); + assert!(per_node.values().all(|v| *v)); + + let graph = scenario.attack_graph.borrow(); + let model = scenario.model.borrow(); + let expected: BTreeSet = graph + .nodes + .values() + .filter_map(|node| { + let asset = model.get_asset_by_id(node.model_asset?)?; + let matches = (asset.asset_type == "Application" + && (node.name == "fullAccess" || node.name == "supplyChainAuditing")) + || (asset.name == "Identity:8" && node.name == "assume"); + matches.then(|| format!("{}:{}", asset.name, node.name)) + }) + .collect(); + assert!(!expected.is_empty()); + assert_eq!(per_node.keys().cloned().collect::>(), expected); +} + +#[test] +fn load_scenario_observability_not_given() { + // Port of `test_load_scenario_observability_not_given`. + let scenario = load("simple_scenario.yml"); + assert!(defender(&scenario, "Defender1").observable_steps.is_none()); +} + +#[test] +fn load_scenario_false_positive_negative_rate() { + // Port of `test_load_scenario_false_positive_negative_rate`. + let scenario = load("traininglang_fp_fn_scenario.yml"); + let defender = defender(&scenario, "defender"); + let fpr = per_node_by_name(&scenario, defender.false_positive_rates.as_ref().unwrap()); + let fnr = per_node_by_name(&scenario, defender.false_negative_rates.as_ref().unwrap()); + + assert_eq!( + fpr, + BTreeMap::from([ + ("Host:0:access".to_string(), 0.2), + ("Host:1:access".to_string(), 0.3) + ]) + ); + assert_eq!( + fnr, + BTreeMap::from([ + ("Host:0:access".to_string(), 0.4), + ("Host:1:access".to_string(), 0.5), + ("User:3:compromise".to_string(), 1.0), + ]) + ); +} + +#[test] +fn scenario_advanced_agent_settings() { + // Port of `test_scenario_advanced_agent_settings` (minus the + // `to_dict` round trip, which the Rust loader doesn't implement). + let scenario = load("traininglang_scenario_advanced_agent_settings.yml"); + assert!(scenario + .lang_file + .ends_with("langs/org.mal-lang.trainingLang-1.0.0.mar")); + assert!(scenario + .model_file + .as_ref() + .unwrap() + .ends_with("models/traininglang_model.yml")); + + let attacker = attacker(&scenario, "Attacker1"); + let rewards = attacker.rewards.as_ref().unwrap(); + assert_eq!(valued(rewards, "Host:0", "access"), 4.0); + assert_eq!(valued(rewards, "Host:1", "access"), 5.0); + assert_eq!(valued(rewards, "Data:2", "read"), 5.0); + assert_eq!(valued(rewards, "Data:2", "modify"), 10.0); + assert_eq!(valued(rewards, "Host:0", "authenticate"), 1000.0); + + assert_eq!( + attacker.entry_points, + EntryPoints::Single(BTreeSet::from([ + node_id(&scenario, "User:3:phishing"), + node_id(&scenario, "Host:0:connect"), + ])) + ); + assert_eq!(attacker.policy.as_deref(), Some("BreadthFirstAttacker")); + assert_eq!( + attacker.actionable_steps.as_ref().unwrap().by_asset_type, + Some(BTreeMap::from([ + ( + "Host".to_string(), + StepValues::Listed(vec!["authenticate".into(), "connect".into()]) + ), + ( + "User".to_string(), + StepValues::Listed(vec!["compromise".into()]) + ), + ])) + ); + + let defender = defender(&scenario, "Defender1"); + assert_eq!( + defender.actionable_steps.as_ref().unwrap().by_asset_type, + Some(BTreeMap::from([( + "Host".to_string(), + StepValues::Listed(vec!["notPresent".into()]) + )])) + ); + assert!(defender.observable_steps.is_some()); + let fnr = defender.false_negative_rates.as_ref().unwrap(); + let fpr = defender.false_positive_rates.as_ref().unwrap(); + let by_type = |rule: &NodePropertyRule, asset: &str, step: &str| match &rule + .by_asset_type + .as_ref() + .unwrap()[asset] + { + StepValues::Valued(values) => values[step], + StepValues::Listed(_) => panic!("expected mapping form"), + }; + assert_eq!(by_type(fnr, "Host", "access"), 0.5); + assert_eq!(by_type(fpr, "Host", "connect"), 0.5); + assert_eq!( + valued(defender.rewards.as_ref().unwrap(), "Host:0", "notPresent"), + 100.0 + ); +} + +#[test] +fn inline_model_and_ttc_overrides() { + let scenario = load("ttc_lang_scenario_override_ttcs.yml"); + assert!(scenario.model_file.is_none()); + let bad = attacker(&scenario, "BadAttacker"); + let per_node = per_node_by_name(&scenario, bad.ttc_dists.as_ref().unwrap()); + // `by_asset_type: Computer: easyConnect` - one node per Computer asset. + assert_eq!( + per_node.keys().cloned().collect::>(), + ["ComputerA", "ComputerB", "ComputerC", "ComputerD"] + .iter() + .map(|c| format!("{c}:easyConnect")) + .collect() + ); + assert!(attacker(&scenario, "GoodAttacker").ttc_dists.is_none()); +} + +#[test] +fn sim_settings_are_parsed() { + let mut dict = load_scenario_dict(scenario_path("simple_scenario.yml")).unwrap(); + dict.insert( + "sim_settings".into(), + serde_json::json!({"ttc_mode": "EXPECTED_VALUE", "seed": 3, "attack_surface": {"skip_unnecessary": true}}), + ); + let scenario = Scenario::from_dict(&dict).unwrap(); + assert_eq!(scenario.sim_settings.ttc_mode, TtcMode::ExpectedValue); + assert_eq!(scenario.sim_settings.seed, Some(3)); + assert!(scenario.sim_settings.attack_surface.skip_unnecessary); + + let default = load("simple_scenario.yml"); + assert_eq!(default.sim_settings, MalSimulatorSettings::default()); + assert_eq!( + attacker(&default, "Attacker1").reward_mode, + RewardMode::ExpectedTtc + ); +} + +#[test] +fn unknown_entry_point_is_an_error() { + let mut dict = load_scenario_dict(scenario_path("simple_scenario.yml")).unwrap(); + dict["agents"]["Attacker1"]["entry_points"] = serde_json::json!(["Nope:fullAccess"]); + assert!(matches!( + Scenario::from_dict(&dict), + Err(ScenarioError::AgentSettings( + AgentSettingsError::UnknownNode { .. } + )) + )); +} + +#[test] +fn git_lang_url_is_reported() { + assert!(matches!( + Scenario::load_from_file(scenario_path("simple_scenario_git_url.yml")), + Err(ScenarioError::Language { .. }) + )); +} + +#[test] +fn flatten_agents_orders_attackers_first_and_resolves_ids() { + let scenario = load("simple_scenario.yml"); + let flat = scenario + .flatten_agents(&mut StdRng::seed_from_u64(0)) + .unwrap(); + let names: Vec<&str> = flat.iter().map(|(name, _)| name.as_str()).collect(); + assert_eq!(names, vec!["Attacker1", "Defender1"]); + match &flat[0].1 { + FlatAgentSettings::Attacker(a) => { + assert_eq!( + a.entry_points, + [node_id(&scenario, "OS App:fullAccess")] + .into_iter() + .collect::() + ); + } + FlatAgentSettings::Defender(_) => panic!("expected attacker first"), + } + assert!(matches!(flat[1].1, FlatAgentSettings::Defender(_))); +} + +/// A scratch file path unique to this test process, removed on drop. +struct TempScenarioFile(PathBuf); + +impl TempScenarioFile { + fn new(test: &str) -> Self { + let dir = std::env::temp_dir().join(format!("malsim-core-{}", std::process::id())); + std::fs::create_dir_all(&dir).unwrap(); + TempScenarioFile(dir.join(format!("{test}.yml"))) + } +} + +impl Drop for TempScenarioFile { + fn drop(&mut self) { + let _ = std::fs::remove_file(&self.0); + } +} + +/// Saves `scenario`, reloads it and checks that the agents and settings +/// came back unchanged. Returns the reloaded scenario. +fn assert_round_trips(test: &str, scenario: &Scenario) -> Scenario { + let file = TempScenarioFile::new(test); + scenario.save_to_file(&file.0).unwrap(); + let reloaded = Scenario::load_from_file(&file.0).unwrap(); + + assert_eq!(reloaded.sim_settings, scenario.sim_settings); + assert_eq!( + reloaded.attacker_settings_by_full_name, + scenario.attacker_settings_by_full_name + ); + assert_eq!( + reloaded.defender_settings().collect::>(), + scenario.defender_settings().collect::>() + ); + assert_eq!( + reloaded + .agent_settings + .iter() + .map(AgentSettings::name) + .collect::>(), + scenario + .agent_settings + .iter() + .map(AgentSettings::name) + .collect::>() + ); + assert_eq!(reloaded.to_dict().unwrap(), scenario.to_dict().unwrap()); + reloaded +} + +#[test] +fn save_to_file_round_trips_model_file_scenario() { + let scenario = load("simple_scenario.yml"); + let reloaded = assert_round_trips("save_model_file", &scenario); + + // The model is referenced by file, not inlined. + let dict = reloaded.to_dict().unwrap(); + assert!(dict.contains_key("model_file")); + assert!(!dict.contains_key("model")); + assert_eq!(reloaded.model_file, scenario.model_file); + assert_eq!( + attacker(&reloaded, "Attacker1").policy.as_deref(), + Some("BreadthFirstAttacker") + ); +} + +#[test] +fn save_to_file_round_trips_inline_model() { + let mut dict = load_scenario_dict(scenario_path("simple_scenario.yml")).unwrap(); + let model_file = dict.remove("model_file").unwrap(); + let model: Value = + serde_yaml::from_reader(std::fs::File::open(model_file.as_str().unwrap()).unwrap()) + .unwrap(); + dict.insert("model".into(), model); + let scenario = Scenario::from_dict(&dict).unwrap(); + assert!(scenario.model_file.is_none()); + + let reloaded = assert_round_trips("save_inline_model", &scenario); + assert!(reloaded.model_file.is_none()); + assert_eq!( + reloaded.model.borrow().to_dict(), + scenario.model.borrow().to_dict() + ); +} + +#[test] +fn save_to_file_round_trips_defender_rules() { + let scenario = load("traininglang_fp_fn_scenario.yml"); + let reloaded = assert_round_trips("save_fp_fn", &scenario); + assert!(defender(&reloaded, "defender") + .false_positive_rates + .is_some()); + assert!(defender(&reloaded, "defender") + .false_negative_rates + .is_some()); +} + +#[test] +fn save_to_file_round_trips_observability_and_actionability() { + for (test, file) in [ + ( + "save_observability", + "traininglang_observability_scenario.yml", + ), + ( + "save_actionability", + "traininglang_actionability_scenario.yml", + ), + ] { + assert_round_trips(test, &load(file)); + } +} + +#[test] +fn save_to_file_round_trips_multiple_entry_point_sets() { + let scenario = load("bfs_vs_bfs_scenario_multiple_entrypoint_sets.yml"); + let reloaded = assert_round_trips("save_multiple_entry_points", &scenario); + assert!(matches!( + attacker(&reloaded, "attacker1").entry_points, + EntryPoints::Multiple(_) + )); +} + +#[test] +fn save_to_file_keeps_goals_sim_settings_reward_mode_and_ttc_overrides() { + let mut dict = load_scenario_dict(scenario_path("simple_scenario.yml")).unwrap(); + dict.insert( + "sim_settings".into(), + serde_json::json!({ + "ttc_mode": "PRE_SAMPLE", + "seed": 7, + "attack_surface": {"skip_compromised": false, "skip_unnecessary": true}, + "compromise_entrypoints_at_start": false, + "run_defense_step_bernoullis": false, + "run_attack_step_bernoullis": false, + }), + ); + dict["agents"]["Attacker1"]["goals"] = serde_json::json!(["OS App:fullAccess"]); + dict["agents"]["Attacker1"]["reward_mode"] = "EXPECTED_TTC".into(); + dict["agents"]["Attacker1"]["config"] = serde_json::json!({"depth": 3}); + dict["agents"]["Attacker1"]["ttc_overrides"] = serde_json::json!({ + "by_asset_name": {"OS App": {"fullAccess": "HardAndUncertain"}}, + }); + dict["agents"]["Defender1"]["reward_mode"] = "ONE_OFF".into(); + let scenario = Scenario::from_dict(&dict).unwrap(); + + let reloaded = assert_round_trips("save_everything", &scenario); + let sim = reloaded.sim_settings; + assert_eq!(sim.ttc_mode, TtcMode::PreSample); + assert_eq!(sim.seed, Some(7)); + assert!(!sim.attack_surface.skip_compromised); + assert!(sim.attack_surface.skip_unnecessary); + assert!(!sim.compromise_entrypoints_at_start); + + let a = attacker(&reloaded, "Attacker1"); + assert_eq!(a.reward_mode, RewardMode::ExpectedTtc); + assert_eq!(a.config["depth"], 3); + assert_eq!( + a.goals, + BTreeSet::from([node_id(&reloaded, "OS App:fullAccess")]) + ); + assert!(a.ttc_dists.is_some()); + assert_eq!( + defender(&reloaded, "Defender1").reward_mode, + RewardMode::OneOff + ); +} + +#[test] +fn save_to_file_writes_absolute_paths_and_omits_empty_fields() { + let scenario = load("simple_scenario.yml"); + let dict = scenario.to_dict().unwrap(); + assert!(Path::new(dict["lang_file"].as_str().unwrap()).is_absolute()); + assert!(Path::new(dict["model_file"].as_str().unwrap()).is_absolute()); + + let attacker = dict["agents"]["Attacker1"].as_object().unwrap(); + assert_eq!(attacker["type"], "attacker"); + for absent in ["goals", "ttc_overrides", "actionable_steps"] { + assert!(!attacker.contains_key(absent), "{absent} should be omitted"); + } + assert_eq!(attacker["config"]["seed"], 1); + let defender = dict["agents"]["Defender1"].as_object().unwrap(); + assert_eq!(defender["type"], "defender"); + assert!(!defender.contains_key("false_positive_rates")); +} + +#[test] +fn save_to_file_to_unwritable_path_is_an_io_error() { + let scenario = load("simple_scenario.yml"); + let missing_dir = std::env::temp_dir() + .join(format!("malsim-core-missing-{}", std::process::id())) + .join("scenario.yml"); + assert!(matches!( + scenario.save_to_file(&missing_dir), + Err(ScenarioError::File(ScenarioFileError::Io { .. })) + )); +} diff --git a/core/malsim-core/src/settings.rs b/core/malsim-core/src/settings.rs new file mode 100644 index 00000000..de9cae7d --- /dev/null +++ b/core/malsim-core/src/settings.rs @@ -0,0 +1,344 @@ +//! Settings shared by the Rust-only scenario loader (`crate::scenario`) and +//! the `Simulator`: a port of `python/malsim/config/sim_settings.py` +//! (`MalSimulatorSettings`, `AttackSurfaceSettings`, `RewardMode`) plus the +//! flat, id-resolved per-agent inputs the `Simulator` consumes - the Rust +//! counterpart of what `native_settings.py`'s `flatten_*_settings` produce +//! on the Python side. See `PORTING_NOTES.md` §11. + +use crate::types::{NodeMap, NodeSet}; +use std::fmt; + +use serde_json::{Map, Value}; + +pub use crate::graph_state::TtcMode; +use crate::ttc::TtcDist; + +#[derive(Debug, Clone, PartialEq, Eq)] +pub enum SettingsError { + /// A settings value that must be a mapping isn't one. + NotAMapping(&'static str), + /// A key the settings dataclass doesn't have (Python raises a + /// `TypeError` from the dataclass constructor). + UnknownField { + settings: &'static str, + field: String, + }, + /// A field with a value of the wrong type or an unknown enum name. + InvalidValue { field: String, reason: String }, +} + +impl fmt::Display for SettingsError { + fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { + match self { + SettingsError::NotAMapping(what) => write!(f, "{what} must be a mapping"), + SettingsError::UnknownField { settings, field } => { + write!(f, "{settings} got an unexpected keyword argument '{field}'") + } + SettingsError::InvalidValue { field, reason } => { + write!(f, "Invalid value for '{field}': {reason}") + } + } + } +} + +impl std::error::Error for SettingsError {} + +/// Port of `RewardMode`. Carried through settings for inspection only: +/// rewards are not computed in Rust (§2.4). +#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)] +pub enum RewardMode { + Cumulative, + OneOff, + ExpectedTtc, + SampleTtc, +} + +impl RewardMode { + /// Parses the Python enum member name (`RewardMode[name]`). + pub fn from_name(name: &str) -> Option { + match name { + "CUMULATIVE" => Some(RewardMode::Cumulative), + "ONE_OFF" => Some(RewardMode::OneOff), + "EXPECTED_TTC" => Some(RewardMode::ExpectedTtc), + "SAMPLE_TTC" => Some(RewardMode::SampleTtc), + _ => None, + } + } + + /// The Python enum member name (`RewardMode.name`). + pub fn name(self) -> &'static str { + match self { + RewardMode::Cumulative => "CUMULATIVE", + RewardMode::OneOff => "ONE_OFF", + RewardMode::ExpectedTtc => "EXPECTED_TTC", + RewardMode::SampleTtc => "SAMPLE_TTC", + } + } +} + +/// Port of `AttackSurfaceSettings`. +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +pub struct AttackSurfaceSettings { + /// If true, do not add already compromised nodes to the attack surface. + pub skip_compromised: bool, + /// If true, do not add unnecessary nodes to the attack surface. + pub skip_unnecessary: bool, +} + +impl Default for AttackSurfaceSettings { + fn default() -> Self { + AttackSurfaceSettings { + skip_compromised: true, + skip_unnecessary: false, + } + } +} + +impl AttackSurfaceSettings { + /// Inverse of `from_value`. + pub fn to_value(&self) -> Value { + serde_json::json!({ + "skip_compromised": self.skip_compromised, + "skip_unnecessary": self.skip_unnecessary, + }) + } + + /// Port of `AttackSurfaceSettings(**d)`. + pub fn from_value(value: &Value) -> Result { + let obj = value + .as_object() + .ok_or(SettingsError::NotAMapping("attack_surface"))?; + let mut settings = AttackSurfaceSettings::default(); + for (key, v) in obj { + match key.as_str() { + "skip_compromised" => settings.skip_compromised = bool_field(key, v)?, + "skip_unnecessary" => settings.skip_unnecessary = bool_field(key, v)?, + _ => { + return Err(SettingsError::UnknownField { + settings: "AttackSurfaceSettings", + field: key.clone(), + }) + } + } + } + Ok(settings) + } +} + +/// Port of `MalSimulatorSettings`. +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +pub struct MalSimulatorSettings { + /// Defined but unread, same as on the Python side (§2.5). + pub uncompromise_untraversable_steps: bool, + pub ttc_mode: TtcMode, + pub seed: Option, + pub attack_surface: AttackSurfaceSettings, + /// If true, each attacker compromises its entry points at the start of + /// the simulation. + pub compromise_entrypoints_at_start: bool, + /// If true, sample defense step bernoullis to decide their initial states. + pub run_defense_step_bernoullis: bool, + /// If true, sample attack step bernoullis to decide if they are + /// impossible/exist. + pub run_attack_step_bernoullis: bool, +} + +impl Default for MalSimulatorSettings { + fn default() -> Self { + MalSimulatorSettings { + uncompromise_untraversable_steps: false, + ttc_mode: TtcMode::Disabled, + seed: None, + attack_surface: AttackSurfaceSettings::default(), + compromise_entrypoints_at_start: true, + run_defense_step_bernoullis: true, + run_attack_step_bernoullis: true, + } + } +} + +impl MalSimulatorSettings { + /// Inverse of `from_value`: every field, with `ttc_mode` as its enum + /// name and an unset `seed` as `null`. + pub fn to_value(&self) -> Value { + serde_json::json!({ + "uncompromise_untraversable_steps": self.uncompromise_untraversable_steps, + "ttc_mode": self.ttc_mode.name(), + "seed": self.seed, + "attack_surface": self.attack_surface.to_value(), + "compromise_entrypoints_at_start": self.compromise_entrypoints_at_start, + "run_defense_step_bernoullis": self.run_defense_step_bernoullis, + "run_attack_step_bernoullis": self.run_attack_step_bernoullis, + }) + } + + /// Port of `MalSimulatorSettings(**d)` including `__post_init__`'s + /// string-to-enum (`ttc_mode`) and dict-to-dataclass (`attack_surface`) + /// conversions. `null` for a field leaves its default only for `seed`, + /// whose Python default is `None`. + pub fn from_value(value: &Value) -> Result { + let obj: &Map = value + .as_object() + .ok_or(SettingsError::NotAMapping("sim_settings"))?; + let mut settings = MalSimulatorSettings::default(); + for (key, v) in obj { + match key.as_str() { + "uncompromise_untraversable_steps" => { + settings.uncompromise_untraversable_steps = bool_field(key, v)? + } + "ttc_mode" => { + let name = v + .as_str() + .ok_or_else(|| invalid(key, "expected a TTCMode name"))?; + settings.ttc_mode = TtcMode::from_name(name) + .ok_or_else(|| invalid(key, &format!("unknown TTCMode '{name}'")))?; + } + "seed" => { + settings.seed = match v { + Value::Null => None, + _ => Some( + v.as_u64() + .ok_or_else(|| invalid(key, "expected a non-negative integer"))?, + ), + } + } + "attack_surface" => settings.attack_surface = AttackSurfaceSettings::from_value(v)?, + "compromise_entrypoints_at_start" => { + settings.compromise_entrypoints_at_start = bool_field(key, v)? + } + "run_defense_step_bernoullis" => { + settings.run_defense_step_bernoullis = bool_field(key, v)? + } + "run_attack_step_bernoullis" => { + settings.run_attack_step_bernoullis = bool_field(key, v)? + } + _ => { + return Err(SettingsError::UnknownField { + settings: "MalSimulatorSettings", + field: key.clone(), + }) + } + } + } + Ok(settings) + } +} + +fn invalid(field: &str, reason: &str) -> SettingsError { + SettingsError::InvalidValue { + field: field.to_string(), + reason: reason.to_string(), + } +} + +fn bool_field(field: &str, value: &Value) -> Result { + value + .as_bool() + .ok_or_else(|| invalid(field, "expected a boolean")) +} + +/// One attacker's id-resolved settings, as `Simulator::reset` consumes +/// them - the Rust counterpart of `native_settings.py:: +/// flatten_attacker_settings`'s output. `entry_points` is a single, +/// already-sampled set (multiple entry-point sets are sampled by the +/// caller before reset). +#[derive(Debug, Clone, Default, PartialEq)] +pub struct FlatAttackerSettings { + pub entry_points: NodeSet, + pub goals: NodeSet, + /// `None`: no actionability rule (every node actionable). + pub actionable_steps: Option, + /// `None`: no TTC overrides. + pub ttc_dists: Option>, +} + +/// One defender's id-resolved settings - the Rust counterpart of +/// `native_settings.py::flatten_defender_settings`'s output. +#[derive(Debug, Clone, Default, PartialEq)] +pub struct FlatDefenderSettings { + /// `None`: no actionability rule (every node actionable). + pub actionable_steps: Option, + /// `None`: no observability rule (every node observable). + pub observable_steps: Option, + pub false_positive_rates: Option>, + pub false_negative_rates: Option>, +} + +#[derive(Debug, Clone, PartialEq)] +pub enum FlatAgentSettings { + Attacker(FlatAttackerSettings), + Defender(FlatDefenderSettings), +} + +#[cfg(test)] +mod tests { + use super::*; + use serde_json::json; + + #[test] + fn defaults_match_python_dataclass() { + let s = MalSimulatorSettings::default(); + assert!(!s.uncompromise_untraversable_steps); + assert_eq!(s.ttc_mode, TtcMode::Disabled); + assert_eq!(s.seed, None); + assert!(s.attack_surface.skip_compromised); + assert!(!s.attack_surface.skip_unnecessary); + assert!(s.compromise_entrypoints_at_start); + assert!(s.run_defense_step_bernoullis); + assert!(s.run_attack_step_bernoullis); + assert_eq!(MalSimulatorSettings::from_value(&json!({})), Ok(s)); + } + + #[test] + fn from_value_converts_enum_names_and_nested_attack_surface() { + let s = MalSimulatorSettings::from_value(&json!({ + "ttc_mode": "PRE_SAMPLE", + "seed": 42, + "attack_surface": {"skip_unnecessary": true}, + "run_attack_step_bernoullis": false, + })) + .unwrap(); + assert_eq!(s.ttc_mode, TtcMode::PreSample); + assert_eq!(s.seed, Some(42)); + assert!(s.attack_surface.skip_compromised); + assert!(s.attack_surface.skip_unnecessary); + assert!(!s.run_attack_step_bernoullis); + } + + #[test] + fn from_value_rejects_unknown_fields_and_values() { + assert!(matches!( + MalSimulatorSettings::from_value(&json!({"bogus": 1})), + Err(SettingsError::UnknownField { .. }) + )); + assert!(matches!( + MalSimulatorSettings::from_value(&json!({"ttc_mode": "FAST"})), + Err(SettingsError::InvalidValue { .. }) + )); + assert!(matches!( + MalSimulatorSettings::from_value(&json!({"attack_surface": {"bogus": true}})), + Err(SettingsError::UnknownField { .. }) + )); + } + + #[test] + fn enum_names_round_trip() { + for mode in [ + TtcMode::EffortBasedPerStepSample, + TtcMode::PerStepSample, + TtcMode::PreSample, + TtcMode::ExpectedValue, + TtcMode::Disabled, + ] { + assert_eq!(TtcMode::from_name(mode.name()), Some(mode)); + } + for mode in [ + RewardMode::Cumulative, + RewardMode::OneOff, + RewardMode::ExpectedTtc, + RewardMode::SampleTtc, + ] { + assert_eq!(RewardMode::from_name(mode.name()), Some(mode)); + } + } +} diff --git a/core/malsim-core/src/simulator.rs b/core/malsim-core/src/simulator.rs new file mode 100644 index 00000000..a6c8b451 --- /dev/null +++ b/core/malsim-core/src/simulator.rs @@ -0,0 +1,1291 @@ +//! The pure-Rust reset/step orchestration of `malsim`'s simulator over a +//! *static* instance model/attack graph: a port of +//! `python/malsim/mal_simulator/simulator.py::reset`/`step`, moved here from +//! `malsim-pyo3`'s `Simulator` pyclass so it can be driven without Python. +//! The dynamic counterpart, where model effects mutate the `Model`/ +//! `AttackGraph` mid-episode, is [`crate::dyna_simulator::DynaSimulator`]: it +//! wraps a `Simulator` and reuses its reset and per-agent bookkeeping, +//! swapping in only the dyna step functions (`PORTING_NOTES.md` §11). +//! +//! `malsim-pyo3` is a thin wrapper around both: it parses Python dicts into +//! `MalSimulatorSettings`/`FlatAgentSettings`, calls `reset`/`step`, and +//! builds its plain-`dict` output from `StepOutcome` + `state()`. +//! +//! All node references here are `AttackGraphNodeId` slotmap keys; the +//! stable `AttackGraphNode.id: i64` translation stays at the FFI boundary. +//! +//! Agents are kept in `BTreeMap`s keyed by name, and every loop over agents +//! in `reset`/`step` iterates in that (sorted-name) order, so multi-agent +//! RNG consumption is deterministic across processes for the same seed. + +use crate::types::{NodeMap, NodeSet}; +use std::cell::RefCell; +use std::collections::{BTreeMap, HashMap}; +use std::fmt; +use std::rc::Rc; + +use maltoolbox_attackgraph::{AttackGraph, AttackGraphNodeId}; +use rand::rngs::StdRng; +use rand::SeedableRng; + +use crate::attack_surface::{ + get_attack_surface, get_effects_of_attack_step, update_attack_surface, +}; +use crate::attacker_step::{attacker_step, AttackerStepError}; +use crate::defender_step::{defender_step, DefenderStepError}; +use crate::defense_surface::get_defense_surface; +use crate::dyna_attacker_step::DynaAttackerStepError; +use crate::dyna_defender_step::DynaDefenderStepError; +use crate::event_logger::{collect_false_positives, collect_logs, EventLoggerError, LogEntry}; +use crate::graph_state::{ + attack_step_ttc_value, compute_initial_graph_state, is_impossible_attack_step, GraphState, + GraphStateError, TtcMode, +}; +use crate::graph_utils::{node_order_key, sort_by_node_id, GraphUtilsError}; +use crate::history::{ + recording_order, surface_diff, AttackerStepRecord, DefenderStepRecord, EpisodeHistory, + StepRecord, +}; +use crate::model_state::ModelStateError; +use crate::observability::observed_nodes; +use crate::settings::{ + FlatAgentSettings, FlatAttackerSettings, FlatDefenderSettings, MalSimulatorSettings, +}; +use crate::ttc::TtcDist; + +#[derive(Debug)] +pub enum SimulatorError { + /// `Simulator::step` was called before any successful + /// `Simulator::reset`. + NotReset, + /// Mirrors `_pre_step_check`'s `KeyError` on an `actions` key that + /// names no registered agent. + UnknownAgent(String), + AttackerStep(AttackerStepError), + DefenderStep(DefenderStepError), + DynaAttackerStep(DynaAttackerStepError), + DynaDefenderStep(DynaDefenderStepError), + GraphState(GraphStateError), + GraphUtils(GraphUtilsError), + EventLogger(EventLoggerError), + /// Boxed (same as `DynaAttackerStepError::ModelEffects`) to keep + /// `Result<_, SimulatorError>` small - clippy's `result_large_err`. + ModelState(Box), +} + +impl From for SimulatorError { + fn from(e: AttackerStepError) -> Self { + SimulatorError::AttackerStep(e) + } +} + +impl From for SimulatorError { + fn from(e: DefenderStepError) -> Self { + SimulatorError::DefenderStep(e) + } +} + +impl From for SimulatorError { + fn from(e: DynaAttackerStepError) -> Self { + SimulatorError::DynaAttackerStep(e) + } +} + +impl From for SimulatorError { + fn from(e: DynaDefenderStepError) -> Self { + SimulatorError::DynaDefenderStep(e) + } +} + +impl From for SimulatorError { + fn from(e: GraphStateError) -> Self { + SimulatorError::GraphState(e) + } +} + +impl From for SimulatorError { + fn from(e: GraphUtilsError) -> Self { + SimulatorError::GraphUtils(e) + } +} + +impl From for SimulatorError { + fn from(e: EventLoggerError) -> Self { + SimulatorError::EventLogger(e) + } +} + +impl From for SimulatorError { + fn from(e: ModelStateError) -> Self { + SimulatorError::ModelState(Box::new(e)) + } +} + +impl fmt::Display for SimulatorError { + fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { + match self { + SimulatorError::NotReset => write!(f, "Simulator.step() called before reset()"), + SimulatorError::UnknownAgent(name) => write!(f, "No agent has name '{name}'"), + SimulatorError::AttackerStep(e) => write!(f, "{e}"), + SimulatorError::DefenderStep(e) => write!(f, "{e}"), + SimulatorError::DynaAttackerStep(e) => write!(f, "{e}"), + SimulatorError::DynaDefenderStep(e) => write!(f, "{e}"), + SimulatorError::GraphState(e) => write!(f, "{e}"), + SimulatorError::GraphUtils(e) => write!(f, "{e}"), + SimulatorError::EventLogger(e) => write!(f, "{e}"), + SimulatorError::ModelState(e) => write!(f, "{e}"), + } + } +} + +impl std::error::Error for SimulatorError {} + +// --- Per-agent runtime state --- + +/// The Rust-side equivalent of the mutable parts of `AttackerState` the +/// simulator needs (§3) - not a port of a specific Python type. +#[derive(Debug, Clone, PartialEq)] +pub struct AttackerRuntime { + pub entry_points: NodeSet, + pub goals: NodeSet, + pub actionable_steps: Option, + pub performed_nodes: NodeSet, + /// The nodes of `performed_nodes` in the order they were performed: + /// first the nodes compromised at reset (entry points and their + /// effects, each in node-id order, which is arbitrary but + /// deterministic), then every step's successful compromises in the + /// order `attacker_step` produced them (the chosen node, then its + /// effects in node-id order). A node appears once, at its first + /// compromise. + pub performed_history: Vec, + pub attempted_nodes: NodeSet, + pub action_surface: NodeSet, + pub num_attempts: NodeMap, + pub iteration: u64, + /// Per-node TTC distribution overrides (`AttackerSettings.ttc_dists`, + /// already flattened - §2.4) - `None` when this attacker has no + /// override configured, matching `attempt_attacker_step`'s existing + /// `ttc_dist_overrides` parameter shape. + pub ttc_dist_overrides: Option>, + /// The `AttackerState.ttc_values`/`.impossible_steps` fields this + /// attacker exposes - override-only when `ttc_dist_overrides` is + /// `Some`, otherwise a clone of the graph-wide values (see + /// `attacker_ttc_overrides`'s doc comment). + pub ttc_values: NodeMap, + pub impossible_steps: NodeSet, +} + +/// The Rust-side equivalent of the mutable parts of `DefenderState` - see +/// `AttackerRuntime`'s doc comment. +#[derive(Debug, Clone, PartialEq)] +pub struct DefenderRuntime { + pub actionable_steps: Option, + pub observable_steps: Option, + pub false_positive_rates: Option>, + pub false_negative_rates: Option>, + pub performed_nodes: NodeSet, + pub compromised_nodes: NodeSet, + pub observed_nodes: NodeSet, + pub action_surface: NodeSet, + pub iteration: u64, + pub logs: Vec, +} + +/// Everything computed/mutated by `Simulator::reset`/`Simulator::step`, as +/// a separate type from `Simulator` itself so helper functions can take +/// `&mut SimState` directly - letting the borrow checker see `graph_state`/ +/// `enabled_defenses`/`rng`/`attackers`/`defenders` as disjoint fields. +#[derive(Debug)] +pub struct SimState { + pub(crate) rng: StdRng, + pub settings: MalSimulatorSettings, + pub graph_state: GraphState, + pub enabled_defenses: NodeSet, + pub attackers: BTreeMap, + pub defenders: BTreeMap, + /// This episode's step history - see `history()`/`snapshot_at()`. + pub(crate) history: EpisodeHistory, +} + +impl SimState { + /// Port of `attacker_is_terminated` for the attacker named `name`; + /// `None` when no attacker has that name. + pub fn attacker_is_terminated(&self, name: &str) -> Option { + self.attackers.get(name).map(|a| { + crate::attacker_step::attacker_is_terminated( + &a.action_surface, + &a.goals, + &a.performed_nodes, + ) + }) + } + + /// Port of `defender_is_terminated`: shared by every defender, true once + /// every attacker is terminated (vacuously true with no attackers). + pub fn defender_is_terminated(&self) -> bool { + crate::defender_step::defender_is_terminated( + self.attackers + .values() + .map(|a| (&a.action_surface, &a.goals, &a.performed_nodes)), + ) + } + + /// Port of `MalSimulator.done`: true once no agent is alive, i.e. + /// every attacker is terminated and (if there are defenders) the + /// defenders are too. + pub fn done(&self) -> bool { + self.attackers + .keys() + .all(|name| self.attacker_is_terminated(name) == Some(true)) + && (self.defenders.is_empty() || self.defender_is_terminated()) + } +} + +/// One attacker's deltas from a single `Simulator::step` (or +/// `DynaSimulator::step`) - the nodes this +/// step compromised/attempted, not the episode-accumulated sets (those +/// live in `AttackerRuntime`). +#[derive(Debug, Clone, PartialEq)] +pub struct AttackerStepOutcome { + pub step_performed_nodes: NodeSet, + pub step_attempted_nodes: NodeSet, +} + +/// One defender's deltas from a single `Simulator::step` - newly observed +/// nodes and newly produced logs. +#[derive(Debug, Clone, PartialEq)] +pub struct DefenderStepOutcome { + pub step_observed_nodes: NodeSet, + pub step_logs: Vec, +} + +/// Everything a single `Simulator::step` produced, as deltas. +#[derive(Debug, Clone)] +pub struct StepOutcome { + pub attackers: BTreeMap, + pub defenders: BTreeMap, + /// Defenses enabled by this step's defender actions. + pub step_enabled_defenses: NodeSet, +} + +impl StepOutcome { + /// A reset seen as a step: everything `state` holds is new. This is + /// what Python's `step_*` properties return on the state `reset()` + /// gives back (`previous_state is None`). + pub fn from_reset(state: &SimState) -> StepOutcome { + StepOutcome { + attackers: state + .attackers + .iter() + .map(|(name, a)| { + let outcome = AttackerStepOutcome { + step_performed_nodes: a.performed_nodes.clone(), + step_attempted_nodes: a.attempted_nodes.clone(), + }; + (name.clone(), outcome) + }) + .collect(), + defenders: state + .defenders + .iter() + .map(|(name, d)| { + let outcome = DefenderStepOutcome { + step_observed_nodes: d.observed_nodes.clone(), + step_logs: d.logs.clone(), + }; + (name.clone(), outcome) + }) + .collect(), + step_enabled_defenses: state.enabled_defenses.clone(), + } + } +} + +/// Port of `MalSimulator`'s reset/step orchestration, over a static +/// instance model/attack graph. See `DynaSimulator` for the dynamic one. +pub struct Simulator { + graph: Rc>, + state: Option, +} + +impl Simulator { + /// A simulator over the static `graph`. + pub fn new(graph: Rc>) -> Self { + Simulator { graph, state: None } + } + + pub fn graph(&self) -> &Rc> { + &self.graph + } + + /// The state built by the last successful `reset` (and advanced by every + /// `step` since) - `None` before the first successful `reset`. + pub fn state(&self) -> Option<&SimState> { + self.state.as_ref() + } + + /// `state`, mutably, for `DynaSimulator::step`. + pub(crate) fn state_mut(&mut self) -> Result<&mut SimState, SimulatorError> { + self.state.as_mut().ok_or(SimulatorError::NotReset) + } + + /// Resets the simulator: (re)computes the initial graph state (TTC values, pre-enabled + /// defenses, impossible attack steps, necessity - + /// `graph_state::compute_initial_graph_state`) and (re)builds every + /// agent's runtime state from scratch, mirroring `reset_agent.py`'s + /// `reset_attackers`/`reset_defenders` - including entry-point + /// compromise-at-start (`compromise_entrypoints_at_start`) and the + /// resulting pre-compromised-nodes feed into every defender's initial + /// `observed_nodes`/`logs`. Attackers are reset before defenders, each + /// in name order. On error, the previous state (if any) is kept. + pub fn reset( + &mut self, + settings: &MalSimulatorSettings, + agents: Vec<(String, FlatAgentSettings)>, + seed: u64, + ) -> Result<&SimState, SimulatorError> { + let state = reset_state(&self.graph.borrow(), *settings, agents, seed)?; + Ok(self.state.insert(state)) + } + + /// Steps the simulation: defenders act first (mirroring + /// `simulator.py::step`'s own ordering), newly-enabled defenses are + /// folded into `enabled_defenses` before any attacker acts, then + /// attackers act, then every agent's runtime state (action surface, + /// observed nodes, logs, ...) is recomputed from the step's results - + /// same two-phase "compute all steps, then update all state" shape + /// `simulator.py::step` uses. + /// + /// Defenders/attackers act via `defender_step`/`attacker_step`. + /// `actions` maps agent name to chosen nodes; an agent missing + /// from it takes no action, and a name that is no registered agent is + /// an error. + pub fn step( + &mut self, + actions: &HashMap>, + ) -> Result { + let state = self.state.as_mut().ok_or(SimulatorError::NotReset)?; + check_action_agents(state, actions)?; + plain_step(&self.graph, state, actions) + } +} + +/// Mirrors `_pre_step_check`'s `KeyError` on an `actions` key that names no +/// registered agent. The smallest unknown name is reported, so the error +/// doesn't depend on `HashMap` order. +pub(crate) fn check_action_agents( + state: &SimState, + actions: &HashMap>, +) -> Result<(), SimulatorError> { + match actions + .keys() + .filter(|name| !state.attackers.contains_key(*name) && !state.defenders.contains_key(*name)) + .min() + { + Some(unknown) => Err(SimulatorError::UnknownAgent(unknown.clone())), + None => Ok(()), + } +} + +/// The nodes `name` chose this step - none when `actions` doesn't name it. +pub(crate) fn agent_actions<'a>( + actions: &'a HashMap>, + name: &str, +) -> &'a [AttackGraphNodeId] { + actions.get(name).map(Vec::as_slice).unwrap_or(&[]) +} + +/// Computes an attacker's override-only `ttc_values`/`impossible_steps` +/// (§2.4/§10: when `ttc_dists` is configured, these fields hold *only* +/// the override-affected nodes - mirroring +/// `attacker_state_factories.py::attacker_overriding_ttc_settings` +/// exactly, not a merge with the graph-wide values). +fn attacker_ttc_overrides( + graph: &AttackGraph, + rng: &mut StdRng, + ttc_mode: TtcMode, + ttc_dist_overrides: &NodeMap, +) -> Result<(NodeMap, NodeSet), SimulatorError> { + let mut ttc_values = NodeMap::default(); + let mut impossible_steps = NodeSet::default(); + // The TTC/Bernoulli draws below follow this order, so not the map's. + let mut overrides: Vec<(AttackGraphNodeId, &TtcDist)> = ttc_dist_overrides + .iter() + .map(|(&id, dist)| (id, dist)) + .collect(); + overrides.sort_unstable_by_key(|&(id, _)| node_order_key(graph, id)); + for (node_id, dist) in overrides { + // `ttc_dist_overrides` is resolved once at reset and never + // updated - a dyna-path node it names can later be removed from + // the graph. A removed node has no TTC left to override. + let Some(node) = graph.nodes.get(node_id) else { + continue; + }; + if let Some(v) = attack_step_ttc_value(node, Some(dist), ttc_mode, rng)? { + ttc_values.insert(node_id, v); + } + if is_impossible_attack_step(node, Some(dist), rng)? { + impossible_steps.insert(node_id); + } + } + Ok((ttc_values, impossible_steps)) +} + +/// Shared body of `Simulator::reset` and `DynaSimulator::reset` (after its +/// model restore) - see `Simulator::reset`'s doc comment. +pub(crate) fn reset_state( + graph: &AttackGraph, + settings: MalSimulatorSettings, + agents: Vec<(String, FlatAgentSettings)>, + seed: u64, +) -> Result { + let mut rng = StdRng::seed_from_u64(seed); + + let graph_state = compute_initial_graph_state( + graph, + settings.ttc_mode, + settings.run_defense_step_bernoullis, + settings.run_attack_step_bernoullis, + &mut rng, + )?; + let enabled_defenses = graph_state.pre_enabled_defenses.clone(); + + let mut attacker_cfgs: BTreeMap = BTreeMap::new(); + let mut defender_cfgs: BTreeMap = BTreeMap::new(); + for (name, cfg) in agents { + match cfg { + FlatAgentSettings::Attacker(a) => { + attacker_cfgs.insert(name, a); + } + FlatAgentSettings::Defender(d) => { + defender_cfgs.insert(name, d); + } + } + } + + let mut attackers: BTreeMap = BTreeMap::new(); + let mut pre_compromised: NodeSet = NodeSet::default(); + + for (name, cfg) in attacker_cfgs { + let FlatAttackerSettings { + entry_points, + goals, + actionable_steps, + ttc_dists: ttc_dist_overrides, + } = cfg; + + let (ttc_values, impossible_steps) = match &ttc_dist_overrides { + Some(overrides) => { + attacker_ttc_overrides(graph, &mut rng, settings.ttc_mode, overrides)? + } + None => ( + graph_state.ttc_values.clone(), + graph_state.impossible_attack_steps.clone(), + ), + }; + + let mut performed_nodes: NodeSet = NodeSet::default(); + let mut performed_history: Vec = Vec::new(); + if settings.compromise_entrypoints_at_start { + let mut sorted_entry_points: Vec = + entry_points.iter().copied().collect(); + sort_by_node_id(graph, &mut sorted_entry_points); + for entry_point in sorted_entry_points { + if performed_nodes.insert(entry_point) { + performed_history.push(entry_point); + } + let effects = get_effects_of_attack_step( + graph, + entry_point, + &performed_nodes, + &graph_state.impossible_attack_steps, + &enabled_defenses, + &graph_state.necessity_per_node, + )?; + let mut effects: Vec = effects.into_iter().collect(); + sort_by_node_id(graph, &mut effects); + for effect in effects { + if performed_nodes.insert(effect) { + performed_history.push(effect); + } + } + } + } + + let mut action_surface = get_attack_surface( + graph, + settings.attack_surface.skip_compromised, + settings.attack_surface.skip_unnecessary, + actionable_steps.as_ref(), + &performed_nodes, + None, + &graph_state.impossible_attack_steps, + &enabled_defenses, + &graph_state.necessity_per_node, + )?; + if !settings.compromise_entrypoints_at_start { + action_surface.extend(entry_points.iter().copied()); + } + + pre_compromised.extend(performed_nodes.iter().copied()); + + attackers.insert( + name, + AttackerRuntime { + entry_points, + goals, + actionable_steps, + performed_nodes, + performed_history, + attempted_nodes: NodeSet::default(), + action_surface, + num_attempts: NodeMap::default(), + iteration: 0, + ttc_dist_overrides, + ttc_values, + impossible_steps, + }, + ); + } + + let mut defenders: BTreeMap = BTreeMap::new(); + for (name, cfg) in defender_cfgs { + let FlatDefenderSettings { + actionable_steps, + observable_steps, + false_positive_rates, + false_negative_rates, + } = cfg; + + let action_surface = get_defense_surface( + graph, + actionable_steps.as_ref(), + &graph_state.impossible_attack_steps, + &enabled_defenses, + )?; + + let new_observed_nodes = observed_nodes( + observable_steps.as_ref(), + false_positive_rates.as_ref(), + false_negative_rates.as_ref(), + graph, + &pre_compromised, + &mut rng, + ); + + let mut logs = collect_logs( + 0, + graph, + pre_compromised.iter().copied(), + &NodeSet::default(), + &mut rng, + )?; + logs.extend(collect_false_positives(0, graph, &mut rng)?); + + defenders.insert( + name, + DefenderRuntime { + actionable_steps, + observable_steps, + false_positive_rates, + false_negative_rates, + performed_nodes: enabled_defenses.clone(), + compromised_nodes: pre_compromised.clone(), + observed_nodes: new_observed_nodes, + action_surface, + iteration: 0, + logs, + }, + ); + } + + let history = EpisodeHistory::from_reset(&attackers, &defenders); + Ok(SimState { + rng, + settings, + graph_state, + enabled_defenses, + attackers, + defenders, + history, + }) +} + +/// Each attacker's `(compromised, attempted)` nodes from this step's +/// stepping pass, before `update_attacker_runtimes` folds them in. +/// Per attacker: the step's deltas and its history record. +pub(crate) type AttackerUpdates = ( + BTreeMap, + BTreeMap, +); + +/// Per defender: the step's deltas and its history record. +pub(crate) type DefenderUpdates = ( + BTreeMap, + BTreeMap, +); + +pub(crate) type AttackerResults = + BTreeMap, Vec)>; + +/// `Simulator::step` for a plain simulator (`defender_step`/ +/// `attacker_step`). +fn plain_step( + graph_rc: &Rc>, + state: &mut SimState, + actions: &HashMap>, +) -> Result { + let attacker_names: Vec = state.attackers.keys().cloned().collect(); + let defender_names: Vec = state.defenders.keys().cloned().collect(); + + // --- Defenders act first --- + let mut step_enabled_defenses: NodeSet = NodeSet::default(); + { + let graph = graph_rc.borrow(); + for name in &defender_names { + let runtime = &state.defenders[name]; + let enabled = defender_step( + &graph, + agent_actions(actions, name), + &runtime.action_surface, + )?; + step_enabled_defenses.extend(enabled); + } + } + let new_enabled_defenses: NodeSet = step_enabled_defenses + .difference(&state.enabled_defenses) + .copied() + .collect(); + state + .enabled_defenses + .extend(step_enabled_defenses.iter().copied()); + + // --- Attackers act afterwards --- + let mut step_compromised_nodes: NodeSet = NodeSet::default(); + let mut attacker_results: AttackerResults = BTreeMap::new(); + { + let graph = graph_rc.borrow(); + for name in &attacker_names { + let runtime = &state.attackers[name]; + let (compromised, attempted) = attacker_step( + &graph, + &mut state.rng, + state.settings.ttc_mode, + agent_actions(actions, name), + &runtime.entry_points, + &runtime.action_surface, + &runtime.performed_nodes, + &runtime.num_attempts, + runtime.ttc_dist_overrides.as_ref(), + Some(&runtime.ttc_values), + &state.graph_state.ttc_values, + &state.graph_state.impossible_attack_steps, + &state.enabled_defenses, + &state.graph_state.necessity_per_node, + )?; + step_compromised_nodes.extend(compromised.iter().copied()); + attacker_results.insert(name.clone(), (compromised, attempted)); + } + } + + let graph = graph_rc.borrow(); + let (attackers, attacker_records) = update_attacker_runtimes( + &graph, + state, + &attacker_names, + attacker_results, + Some(&new_enabled_defenses), + )?; + let (defenders, defender_records) = update_defender_runtimes( + &graph, + state, + &defender_names, + &step_enabled_defenses, + &step_compromised_nodes, + )?; + let record = StepRecord { + new_enabled_defenses, + modification_record_len: state.history.modification_record().len(), + ..StepRecord::default() + }; + push_step_record( + &graph, + state, + actions, + record, + attacker_records, + defender_records, + ); + + Ok(StepOutcome { + attackers, + defenders, + step_enabled_defenses, + }) +} + +/// Completes `record` with the per-agent records and this step's +/// `recording` entry, and appends it to the episode history. Called after +/// the runtimes were updated, by both the static and the dyna step. +pub(crate) fn push_step_record( + graph: &AttackGraph, + state: &mut SimState, + actions: &HashMap>, + mut record: StepRecord, + attacker_records: BTreeMap, + defender_records: BTreeMap, +) { + for (name, r) in &attacker_records { + let runtime = &state.attackers[name]; + let new_nodes = &runtime.performed_history[r.performed_history_len..]; + let new_set: NodeSet = new_nodes.iter().copied().collect(); + let order = recording_order( + agent_actions(actions, name), + &new_set, + new_nodes.iter().copied(), + ); + record.recording.insert(name.clone(), order); + } + for (name, r) in &defender_records { + let mut rest: Vec = r.new_performed_nodes.iter().copied().collect(); + sort_by_node_id(graph, &mut rest); + let order = recording_order(agent_actions(actions, name), &r.new_performed_nodes, rest); + record.recording.insert(name.clone(), order); + } + record.attackers = attacker_records; + record.defenders = defender_records; + state.history.push_step(record); +} + +/// Recomputes each attacker's bookkeeping (`action_surface`, +/// `performed_nodes`, `attempted_nodes`, `num_attempts`, `iteration`) +/// after this step's compromises/attempts - shared by `plain_step` and +/// `DynaSimulator::step`: identical regardless of whether the compromises came from +/// plain `attacker_step` or model-effect-aware `dyna_attacker_step`, since +/// both only ever grow `graph_state`/`enabled_defenses`/the graph itself +/// and this just reads whatever the graph looks like once stepping has +/// finished. +/// +/// `new_enabled_defenses` are the defenses enabled this step. With them, +/// action surfaces are updated incrementally (`update_attack_surface`); +/// `None` rebuilds them from scratch, which a step that ran a dyna-MAL +/// model effect needs. +pub(crate) fn update_attacker_runtimes( + graph: &AttackGraph, + state: &mut SimState, + attacker_names: &[String], + mut attacker_results: AttackerResults, + new_enabled_defenses: Option<&NodeSet>, +) -> Result { + let mut attacker_step_deltas = BTreeMap::new(); + let mut records = BTreeMap::new(); + for name in attacker_names { + let (compromised, attempted) = attacker_results + .remove(name) + .expect("every attacker stepped this step"); + let performed_history_len = state.attackers[name].performed_history.len(); + { + let runtime = state + .attackers + .get_mut(name) + .expect("attacker_names come from state.attackers"); + for &node_id in &compromised { + if runtime.performed_nodes.insert(node_id) { + runtime.performed_history.push(node_id); + } + } + } + let settings = &state.settings; + let graph_state = &state.graph_state; + let enabled_defenses = &state.enabled_defenses; + let runtime = state + .attackers + .get_mut(name) + .expect("attacker_names come from state.attackers"); + let new_attempted_nodes: NodeSet = attempted + .iter() + .copied() + .filter(|id| !runtime.attempted_nodes.contains(id)) + .collect(); + runtime.attempted_nodes.extend(attempted.iter().copied()); + for &node_id in &attempted { + *runtime.num_attempts.entry(node_id).or_insert(0) += 1; + } + + let full_surface = |runtime: &AttackerRuntime| { + get_attack_surface( + graph, + settings.attack_surface.skip_compromised, + settings.attack_surface.skip_unnecessary, + runtime.actionable_steps.as_ref(), + &runtime.performed_nodes, + None, + &graph_state.impossible_attack_steps, + enabled_defenses, + &graph_state.necessity_per_node, + ) + }; + // `update_attack_surface` needs a surface that is exactly + // `get_attack_surface`'s result. Before the first step it is not + // when reset added the entry points to it. + let surface_is_exact = runtime.iteration > 0 || settings.compromise_entrypoints_at_start; + let (surface_added, surface_removed) = match new_enabled_defenses { + Some(new_defenses) if surface_is_exact => update_attack_surface( + graph, + &mut runtime.action_surface, + settings.attack_surface.skip_compromised, + settings.attack_surface.skip_unnecessary, + runtime.actionable_steps.as_ref(), + &runtime.performed_nodes, + runtime.performed_history[performed_history_len..] + .iter() + .copied(), + &graph_state.impossible_attack_steps, + enabled_defenses, + new_defenses.iter().copied(), + &graph_state.necessity_per_node, + )?, + _ => { + let new_action_surface = full_surface(runtime)?; + let diff = surface_diff(&runtime.action_surface, &new_action_surface); + runtime.action_surface = new_action_surface; + diff + } + }; + #[cfg(debug_assertions)] + assert_eq!( + runtime.action_surface, + full_surface(runtime)?, + "incremental attack surface of {name} differs from a full rebuild" + ); + runtime.iteration += 1; + + let outcome = AttackerStepOutcome { + step_performed_nodes: compromised.into_iter().collect(), + step_attempted_nodes: attempted.into_iter().collect(), + }; + records.insert( + name.clone(), + AttackerStepRecord { + step_performed_nodes: outcome.step_performed_nodes.clone(), + performed_history_len, + new_attempted_nodes, + step_attempted_nodes: outcome.step_attempted_nodes.clone(), + surface_added, + surface_removed, + }, + ); + attacker_step_deltas.insert(name.clone(), outcome); + } + Ok((attacker_step_deltas, records)) +} + +/// Per-defender intermediate results computed in +/// `update_defender_runtimes`'s first pass (defense surface, newly observed +/// nodes, new logs, previous `performed_nodes`) before the second pass +/// applies them. +type DefenderStepUpdate = (NodeSet, NodeSet, Vec, NodeSet); + +/// Recomputes each defender's bookkeeping (`action_surface`, +/// `performed_nodes`, `compromised_nodes`, `observed_nodes`, `logs`, +/// `iteration`) after this step's enables/compromises - shared by +/// `plain_step` and `DynaSimulator::step`, same reasoning as `update_attacker_runtimes`. +pub(crate) fn update_defender_runtimes( + graph: &AttackGraph, + state: &mut SimState, + defender_names: &[String], + step_enabled_defenses: &NodeSet, + step_compromised_nodes: &NodeSet, +) -> Result { + let mut defender_step_deltas = BTreeMap::new(); + let mut records = BTreeMap::new(); + let mut pass1: BTreeMap = BTreeMap::new(); + + for name in defender_names { + let runtime = &state.defenders[name]; + let previous_performed = runtime.performed_nodes.clone(); + + let defense_surface_full = get_defense_surface( + graph, + runtime.actionable_steps.as_ref(), + &state.graph_state.impossible_attack_steps, + &state.enabled_defenses, + )?; + + let new_observed = observed_nodes( + runtime.observable_steps.as_ref(), + runtime.false_positive_rates.as_ref(), + runtime.false_negative_rates.as_ref(), + graph, + step_compromised_nodes, + &mut state.rng, + ); + + let mut logs = collect_logs( + runtime.iteration, + graph, + step_compromised_nodes.iter().copied(), + &runtime.compromised_nodes, + &mut state.rng, + )?; + logs.extend(collect_false_positives( + runtime.iteration, + graph, + &mut state.rng, + )?); + + pass1.insert( + name.clone(), + (defense_surface_full, new_observed, logs, previous_performed), + ); + } + + for name in defender_names { + let (defense_surface_full, new_observed, logs, previous_performed) = pass1 + .remove(name) + .expect("every defender went through pass 1"); + let runtime = state + .defenders + .get_mut(name) + .expect("defender_names come from state.defenders"); + let new_performed_nodes: NodeSet = step_enabled_defenses + .difference(&runtime.performed_nodes) + .copied() + .collect(); + let new_compromised_nodes: NodeSet = step_compromised_nodes + .difference(&runtime.compromised_nodes) + .copied() + .collect(); + let new_observed_nodes: NodeSet = new_observed + .difference(&runtime.observed_nodes) + .copied() + .collect(); + let logs_len = runtime.logs.len(); + runtime + .performed_nodes + .extend(step_enabled_defenses.iter().copied()); + let new_action_surface: NodeSet = defense_surface_full + .difference(&previous_performed) + .copied() + .collect(); + let (surface_added, surface_removed) = + surface_diff(&runtime.action_surface, &new_action_surface); + runtime.action_surface = new_action_surface; + runtime.observed_nodes.extend(new_observed.iter().copied()); + runtime + .compromised_nodes + .extend(step_compromised_nodes.iter().copied()); + runtime.logs.extend(logs.iter().cloned()); + runtime.iteration += 1; + records.insert( + name.clone(), + DefenderStepRecord { + step_performed_nodes: step_enabled_defenses.clone(), + new_performed_nodes, + new_compromised_nodes, + new_observed_nodes, + logs_len, + surface_added, + surface_removed, + }, + ); + + defender_step_deltas.insert( + name.clone(), + DefenderStepOutcome { + step_observed_nodes: new_observed, + step_logs: logs, + }, + ); + } + + Ok((defender_step_deltas, records)) +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::test_fixtures::{add_dummy_node, dummy_graph, wiper_attack_graph}; + + /// Settings with every bernoulli off, so tests are not at the mercy of + /// sampled impossible steps/pre-enabled defenses. + fn settings() -> MalSimulatorSettings { + MalSimulatorSettings { + run_defense_step_bernoullis: false, + run_attack_step_bernoullis: false, + ..MalSimulatorSettings::default() + } + } + + fn attacker(entry_points: &[AttackGraphNodeId]) -> FlatAgentSettings { + FlatAgentSettings::Attacker(FlatAttackerSettings { + entry_points: entry_points.iter().copied().collect(), + ..FlatAttackerSettings::default() + }) + } + + fn defender() -> FlatAgentSettings { + FlatAgentSettings::Defender(FlatDefenderSettings::default()) + } + + /// A dummy graph: `entry -> child` (an OR attack step reachable from the + /// entry point) plus one unenabled defense. + struct DummyNodes { + entry: AttackGraphNodeId, + child: AttackGraphNodeId, + defense: AttackGraphNodeId, + } + + fn dummy_sim() -> (Simulator, DummyNodes) { + let mut graph = dummy_graph(); + let entry = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let child = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let defense = add_dummy_node(&mut graph, "DummyDefenseAttackStep"); + graph.nodes[entry].children.insert(child); + graph.nodes[child].parents.insert(entry); + ( + Simulator::new(Rc::new(RefCell::new(graph))), + DummyNodes { + entry, + child, + defense, + }, + ) + } + + fn actions( + entries: &[(&str, Vec)], + ) -> HashMap> { + entries + .iter() + .map(|(name, nodes)| (name.to_string(), nodes.clone())) + .collect() + } + + #[test] + fn reset_builds_attacker_and_defender_runtimes() { + let (mut sim, n) = dummy_sim(); + let state = sim + .reset( + &settings(), + vec![ + ("attacker".to_string(), attacker(&[n.entry])), + ("defender".to_string(), defender()), + ], + 1, + ) + .unwrap(); + + let a = &state.attackers["attacker"]; + assert!(a.performed_nodes.contains(&n.entry)); + assert_eq!(a.action_surface, [n.child].into_iter().collect()); + assert_eq!(a.iteration, 0); + assert_eq!(state.attacker_is_terminated("attacker"), Some(false)); + assert_eq!(state.attacker_is_terminated("nobody"), None); + + let d = &state.defenders["defender"]; + assert_eq!(d.action_surface, [n.defense].into_iter().collect()); + assert!(d.compromised_nodes.contains(&n.entry)); + assert!(d.performed_nodes.is_empty()); + assert!(!state.defender_is_terminated()); + } + + #[test] + fn reset_on_wiper_fixture_without_entrypoint_compromise() { + let (graph, _model) = wiper_attack_graph(); + let infect = graph.full_name_to_node["InfectedDevice:infect"]; + let mut sim = Simulator::new(Rc::new(RefCell::new(graph))); + let s = MalSimulatorSettings { + compromise_entrypoints_at_start: false, + ..settings() + }; + let state = sim + .reset( + &s, + vec![ + ("WiperController".to_string(), attacker(&[infect])), + ("defender".to_string(), defender()), + ], + 42, + ) + .unwrap(); + let a = &state.attackers["WiperController"]; + assert!(a.performed_nodes.is_empty()); + assert!(a.action_surface.contains(&infect)); + } + + #[test] + fn step_compromises_node_on_attack_surface() { + let (mut sim, n) = dummy_sim(); + sim.reset( + &settings(), + vec![ + ("attacker".to_string(), attacker(&[n.entry])), + ("defender".to_string(), defender()), + ], + 1, + ) + .unwrap(); + + let outcome = sim.step(&actions(&[("attacker", vec![n.child])])).unwrap(); + let delta = &outcome.attackers["attacker"]; + assert_eq!(delta.step_performed_nodes, [n.child].into_iter().collect()); + // Only failed attempts are recorded as attempts. + assert!(delta.step_attempted_nodes.is_empty()); + assert!(outcome.step_enabled_defenses.is_empty()); + + let state = sim.state().unwrap(); + let a = &state.attackers["attacker"]; + assert!(a.performed_nodes.contains(&n.child)); + assert!(a.action_surface.is_empty()); + assert_eq!(a.iteration, 1); + assert_eq!(state.attacker_is_terminated("attacker"), Some(true)); + let d = &state.defenders["defender"]; + assert!(d.compromised_nodes.contains(&n.child)); + assert_eq!(d.iteration, 1); + } + + #[test] + fn defender_step_enables_defense() { + let (mut sim, n) = dummy_sim(); + sim.reset( + &settings(), + vec![ + ("attacker".to_string(), attacker(&[n.entry])), + ("defender".to_string(), defender()), + ], + 1, + ) + .unwrap(); + + let outcome = sim + .step(&actions(&[("defender", vec![n.defense])])) + .unwrap(); + assert_eq!( + outcome.step_enabled_defenses, + [n.defense].into_iter().collect() + ); + let state = sim.state().unwrap(); + assert!(state.enabled_defenses.contains(&n.defense)); + assert!(state.defenders["defender"] + .performed_nodes + .contains(&n.defense)); + // The attacker took no action this step. + assert!(outcome.attackers["attacker"] + .step_performed_nodes + .is_empty()); + } + + #[test] + fn step_rejects_unknown_agent() { + let (mut sim, n) = dummy_sim(); + sim.reset( + &settings(), + vec![("attacker".to_string(), attacker(&[n.entry]))], + 1, + ) + .unwrap(); + let err = sim + .step(&actions(&[("nobody", vec![]), ("attacker", vec![])])) + .unwrap_err(); + assert!(matches!(&err, SimulatorError::UnknownAgent(name) if name == "nobody")); + assert_eq!(err.to_string(), "No agent has name 'nobody'"); + } + + #[test] + fn step_before_reset_fails() { + let (mut sim, _) = dummy_sim(); + assert!(sim.state().is_none()); + assert!(matches!( + sim.step(&HashMap::new()), + Err(SimulatorError::NotReset) + )); + } + + /// The static `Simulator` never runs model effects: compromising + /// `InfectedDevice:infect`, whose model effect adds a `Wiper` asset + /// under `DynaSimulator` (see `dyna_simulator.rs`), leaves the graph + /// exactly as it was. + #[test] + fn step_does_not_run_model_effects() { + let (graph, _model) = wiper_attack_graph(); + let infect = graph.full_name_to_node["InfectedDevice:infect"]; + let pristine: Vec<_> = graph.nodes.keys().collect(); + let mut sim = Simulator::new(Rc::new(RefCell::new(graph))); + let s = MalSimulatorSettings { + compromise_entrypoints_at_start: false, + ..settings() + }; + sim.reset( + &s, + vec![("WiperController".to_string(), attacker(&[infect]))], + 42, + ) + .unwrap(); + + let outcome = sim + .step(&actions(&[("WiperController", vec![infect])])) + .unwrap(); + assert!(outcome.attackers["WiperController"] + .step_performed_nodes + .contains(&infect)); + assert_eq!( + sim.graph().borrow().nodes.keys().collect::>(), + pristine + ); + } + + /// Runs reset + two steps on a fresh dummy simulator with two attackers + /// and a defender, in `PER_STEP_SAMPLE` mode so the RNG is consumed by + /// every attacker's attempts. + fn run_dummy_episode(seed: u64) -> (Simulator, Vec) { + let (mut sim, n) = dummy_sim(); + let s = MalSimulatorSettings { + ttc_mode: TtcMode::PerStepSample, + ..MalSimulatorSettings::default() + }; + sim.reset( + &s, + vec![ + ("attacker_b".to_string(), attacker(&[n.entry])), + ("attacker_a".to_string(), attacker(&[n.entry])), + ("defender".to_string(), defender()), + ], + seed, + ) + .unwrap(); + let mut outcomes = Vec::new(); + for _ in 0..2 { + outcomes.push( + sim.step(&actions(&[ + ("attacker_a", vec![n.child]), + ("attacker_b", vec![n.child]), + ])) + .unwrap(), + ); + } + (sim, outcomes) + } + + #[test] + fn same_seed_gives_identical_results() { + let (sim1, outcomes1) = run_dummy_episode(7); + let (sim2, outcomes2) = run_dummy_episode(7); + let (state1, state2) = (sim1.state().unwrap(), sim2.state().unwrap()); + + assert_eq!(state1.graph_state, state2.graph_state); + assert_eq!(state1.enabled_defenses, state2.enabled_defenses); + assert_eq!(state1.attackers, state2.attackers); + assert_eq!(state1.defenders, state2.defenders); + assert_eq!(outcomes1.len(), outcomes2.len()); + for (o1, o2) in outcomes1.iter().zip(&outcomes2) { + assert_eq!(o1.attackers, o2.attackers); + assert_eq!(o1.defenders, o2.defenders); + assert_eq!(o1.step_enabled_defenses, o2.step_enabled_defenses); + } + assert_eq!( + state1.attackers.keys().collect::>(), + ["attacker_a", "attacker_b"] + ); + } +} diff --git a/core/malsim-core/src/test_fixtures.rs b/core/malsim-core/src/test_fixtures.rs new file mode 100644 index 00000000..10020bd2 --- /dev/null +++ b/core/malsim-core/src/test_fixtures.rs @@ -0,0 +1,88 @@ +//! Shared test-only fixtures for building real `AttackGraphNode`s with a +//! specific `step_type` - needed by `graph_utils.rs`/`necessity.rs` tests +//! since `AttackStepId` (unlike `AttackGraphNodeId`) can only be minted by +//! a real `maltoolbox_language::graph::LanguageGraph`. See +//! `PORTING_NOTES.md` §10's A3/A4 entries for why this dependency (test- +//! only, `maltoolbox-language` in `[dev-dependencies]`) was added at A4. +//! +//! Mirrors `tests/conftest.py::dummy_lang_graph` - compiles the same +//! `tests/testdata/langs/dummy_lang.mal` fixture the Python test suite +//! uses, so Rust and Python tests exercise the same language. + +use std::path::PathBuf; +use std::rc::Rc; + +use maltoolbox_attackgraph::{AttackGraph, AttackGraphNodeId}; +use maltoolbox_model::Model; + +fn dummy_lang_path() -> PathBuf { + PathBuf::from(env!("CARGO_MANIFEST_DIR")).join("../../tests/testdata/langs/dummy_lang.mal") +} + +fn wiper_lang_path() -> PathBuf { + PathBuf::from(env!("CARGO_MANIFEST_DIR")).join("../../tests/testdata/langs/wiperLang.mal") +} + +fn wiper_model_path() -> PathBuf { + PathBuf::from(env!("CARGO_MANIFEST_DIR")).join("../../tests/testdata/models/wiper_model.yml") +} + +/// Port of `tests/conftest.py::dummy_lang_graph`. +pub(crate) fn dummy_lang_graph() -> maltoolbox_language::graph::LanguageGraph { + maltoolbox_language::from_mal_spec(dummy_lang_path()).expect("compile dummy_lang.mal") +} + +/// An empty `AttackGraph` over the dummy language, mirroring +/// `AttackGraph(dummy_lang_graph)` in the Python tests. +pub(crate) fn dummy_graph() -> AttackGraph { + AttackGraph::empty(Rc::new(dummy_lang_graph())) +} + +/// Adds a node of the given `DummyAsset` attack step (by name, e.g. +/// `"DummyAndAttackStep"`) to `graph`, mirroring +/// `attack_graph.add_node(dummy_attack_steps[step_name])` in the Python +/// tests. +pub(crate) fn add_dummy_node(graph: &mut AttackGraph, step_name: &str) -> AttackGraphNodeId { + let asset_id = graph + .lang_graph + .asset_id("DummyAsset") + .expect("DummyAsset exists in dummy_lang.mal"); + let step_id = graph.lang_graph.assets[asset_id].attack_steps[step_name]; + graph + .add_node(step_id, None, None, None, None, None, None) + .expect("add dummy node") +} + +/// Port of `tests/conftest.py::wiperLang_lang_graph`. Used by Phase B1/B2 +/// tests (`PORTING_NOTES.md` §6) - wiperLang has model effects +/// (`additive_model_effects`/`subtractive_model_effects`), which +/// `dummy_lang.mal` deliberately doesn't declare. +pub(crate) fn wiper_lang_graph() -> maltoolbox_language::graph::LanguageGraph { + maltoolbox_language::from_mal_spec(wiper_lang_path()).expect("compile wiperLang.mal") +} + +/// Port of `tests/conftest.py::wiperLang_model` + `wiperLang_attack_graph`: +/// loads the same `tests/testdata/models/wiper_model.yml` fixture the +/// Python test suite uses (Internet/C2Server/InfectedDevice/InfectedData/ +/// VulnerableDevice, no Wiper/Malware asset yet - those get added at +/// runtime by `InfectedDevice:infect`'s model effect) and builds the full +/// attack graph from it. +pub(crate) fn wiper_attack_graph() -> (AttackGraph, Model) { + let lang_graph = Rc::new(wiper_lang_graph()); + let model = maltoolbox_model::load_from_file(wiper_model_path(), lang_graph) + .expect("load wiper_model.yml"); + let graph = AttackGraph::from_model(&model).expect("build wiperLang attack graph"); + (graph, model) +} + +/// An empty `AttackGraph` plus an assetless `Model`, both sharing one +/// `Rc` - used by Phase B2 tests that need a real `&mut +/// Model` to pass through `execute_model_effects`/`partially_regenerate_graph` +/// but don't need any actual model effects to fire (`dummy_lang.mal` +/// declares none), unlike the wiperLang fixtures above. +pub(crate) fn dummy_graph_and_model() -> (AttackGraph, Model) { + let lang_graph = Rc::new(dummy_lang_graph()); + let graph = AttackGraph::empty(lang_graph.clone()); + let model = Model::new("dummy_test_model", lang_graph); + (graph, model) +} diff --git a/core/malsim-core/src/ttc.rs b/core/malsim-core/src/ttc.rs new file mode 100644 index 00000000..9b4623ae --- /dev/null +++ b/core/malsim-core/src/ttc.rs @@ -0,0 +1,809 @@ +//! Rust port of `python/malsim/mal_simulator/ttc_utils.py`'s `TTCDist` - +//! see `PORTING_NOTES.md` §5 Phase A2. +//! +//! Terminology kept close to the Python original (§2.7): `DistFunction`, +//! `Operation`, `TtcDist` (PascalCase per Rust naming conventions - same +//! concept as Python's `TTCDist`), `named_ttc_dist`, `expected_value`, +//! `sample_value`, `success_probability`, `attempt_ttc_with_effort`, +//! `attempt_bernoulli`. +//! +//! Sampling is statistically-, not bit-, equivalent to the Python/numpy/ +//! scipy original - see `PORTING_NOTES.md` §2.1. `expected_value` and +//! `success_probability`, however, are closed-form and therefore exactly +//! reproducible - the two are not equally affected by the RNG-equivalence +//! caveat. +//! +//! Scope note: `default_ttc_dist`/`TTCDist.from_node`/`from_name` +//! (Python's graph-node-dependent helpers) are *not* ported here - they +//! belong to Phase A3, which ports the pieces of `compute_initial_graph_state` +//! that read `AttackGraphNode`. This module is deliberately independent of +//! `maltoolbox_attackgraph` so it's unit-testable on its own. + +use std::fmt; + +use rand::{Rng, RngExt}; +use serde_json::{json, Value}; +use statrs::distribution::{ + Bernoulli as StatrsBernoulli, Binomial as StatrsBinomial, ContinuousCDF, DiscreteCDF, Exp, + Gamma, LogNormal, Uniform as StatrsUniform, +}; +use statrs::statistics::Distribution as StatrsMean; + +/// Mirrors Python's `DistFunction` enum - variant names are PascalCase per +/// Rust convention, but `as_str`/`from_str` use the exact same string +/// values as Python's `DistFunction.value` (the wire format used by +/// `to_dict`/`from_dict`). +#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)] +pub enum DistFunction { + Bernoulli, + Exponential, + Binomial, + Gamma, + LogNormal, + Uniform, +} + +impl DistFunction { + pub fn as_str(self) -> &'static str { + match self { + DistFunction::Bernoulli => "Bernoulli", + DistFunction::Exponential => "Exponential", + DistFunction::Binomial => "Binomial", + DistFunction::Gamma => "Gamma", + DistFunction::LogNormal => "LogNormal", + DistFunction::Uniform => "Uniform", + } + } + + /// Number of distribution parameters `TtcDist::new` expects in `args`. + fn expected_arg_count(self) -> usize { + match self { + DistFunction::Bernoulli => 1, + DistFunction::Exponential => 1, + DistFunction::Binomial => 2, + DistFunction::Gamma => 2, + DistFunction::LogNormal => 2, + DistFunction::Uniform => 2, + } + } +} + +impl std::str::FromStr for DistFunction { + type Err = (); + + fn from_str(s: &str) -> Result { + match s { + "Bernoulli" => Ok(DistFunction::Bernoulli), + "Exponential" => Ok(DistFunction::Exponential), + "Binomial" => Ok(DistFunction::Binomial), + "Gamma" => Ok(DistFunction::Gamma), + "LogNormal" => Ok(DistFunction::LogNormal), + "Uniform" => Ok(DistFunction::Uniform), + _ => Err(()), + } + } +} + +/// Mirrors Python's `Operation` enum (combination operator between two +/// `TtcDist`s). `as_str`/`FromStr` match Python's `Operation.value` +/// strings (lowercase). +#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)] +pub enum Operation { + Addition, + Subtraction, + Multiplication, + Division, + Exponentiation, +} + +impl Operation { + pub fn as_str(self) -> &'static str { + match self { + Operation::Addition => "addition", + Operation::Subtraction => "subtraction", + Operation::Multiplication => "multiplication", + Operation::Division => "division", + Operation::Exponentiation => "exponentiation", + } + } +} + +impl std::str::FromStr for Operation { + type Err = (); + + /// Case-insensitive, matching Python's `Operation[ttc_dict['type'].upper()]` + /// lookup-by-member-name (the wire format itself is always lowercase, + /// via `to_dict`, but `from_dict` normalizes case the same way Python + /// does before matching). + fn from_str(s: &str) -> Result { + match s.to_ascii_lowercase().as_str() { + "addition" => Ok(Operation::Addition), + "subtraction" => Ok(Operation::Subtraction), + "multiplication" => Ok(Operation::Multiplication), + "division" => Ok(Operation::Division), + "exponentiation" => Ok(Operation::Exponentiation), + _ => Err(()), + } + } +} + +pub fn perform_operation(op: Operation, a: f64, b: f64) -> f64 { + match op { + Operation::Addition => a + b, + Operation::Subtraction => a - b, + Operation::Multiplication => a * b, + Operation::Division => a / b, + Operation::Exponentiation => a.powf(b), + } +} + +#[derive(Debug, Clone, PartialEq)] +pub enum TtcDistError { + WrongArgCount { + function: DistFunction, + expected: usize, + actual: usize, + }, + InvalidDistributionParams(String), + UnknownDistFunction(String), + UnknownOperation(String), + InvalidShape(String), +} + +impl fmt::Display for TtcDistError { + fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { + match self { + TtcDistError::WrongArgCount { + function, + expected, + actual, + } => write!( + f, + "{} expects {expected} argument(s), got {actual}", + function.as_str() + ), + TtcDistError::InvalidDistributionParams(msg) => { + write!(f, "invalid distribution parameters: {msg}") + } + TtcDistError::UnknownDistFunction(name) => { + write!(f, "unknown distribution function name '{name}'") + } + TtcDistError::UnknownOperation(name) => { + write!(f, "unknown operation '{name}'") + } + TtcDistError::InvalidShape(msg) => write!(f, "invalid TtcDist dict shape: {msg}"), + } + } +} + +impl std::error::Error for TtcDistError {} + +/// Rust port of Python's `TTCDist`. Unlike the Python original, this does +/// not eagerly store a constructed `scipy`-equivalent distribution object - +/// `statrs` distribution values are cheap to (re)build from `args` on each +/// call, so they're constructed on demand in each method instead +/// (`TtcDist::new` still validates `args` eagerly, for the same fail-fast +/// behavior as Python's constructor). +#[derive(Debug, Clone, PartialEq)] +pub struct TtcDist { + pub function: DistFunction, + pub args: Vec, + pub combine_with: Option>, + pub combine_op: Option, +} + +impl TtcDist { + pub fn new(function: DistFunction, args: Vec) -> Result { + let expected = function.expected_arg_count(); + if args.len() != expected { + return Err(TtcDistError::WrongArgCount { + function, + expected, + actual: args.len(), + }); + } + let dist = TtcDist { + function, + args, + combine_with: None, + combine_op: None, + }; + // Validate params eagerly (mirrors Python's constructor, which + // eagerly builds `self.dist` and raises immediately on bad params). + dist.validate()?; + Ok(dist) + } + + pub fn with_combine(mut self, combine_with: TtcDist, combine_op: Operation) -> TtcDist { + self.combine_with = Some(Box::new(combine_with)); + self.combine_op = Some(combine_op); + self + } + + fn validate(&self) -> Result<(), TtcDistError> { + match self.function { + DistFunction::Bernoulli => { + StatrsBernoulli::new(self.args[0]) + .map_err(|e| TtcDistError::InvalidDistributionParams(e.to_string()))?; + } + DistFunction::Exponential => { + Exp::new(self.args[0]) + .map_err(|e| TtcDistError::InvalidDistributionParams(e.to_string()))?; + } + DistFunction::Binomial => { + StatrsBinomial::new(self.args[1], self.args[0] as u64) + .map_err(|e| TtcDistError::InvalidDistributionParams(e.to_string()))?; + } + DistFunction::Gamma => { + Gamma::new(self.args[0], 1.0 / self.args[1]) + .map_err(|e| TtcDistError::InvalidDistributionParams(e.to_string()))?; + } + DistFunction::LogNormal => { + LogNormal::new(self.args[0], self.args[1]) + .map_err(|e| TtcDistError::InvalidDistributionParams(e.to_string()))?; + } + DistFunction::Uniform => { + StatrsUniform::new(self.args[0], self.args[1]) + .map_err(|e| TtcDistError::InvalidDistributionParams(e.to_string()))?; + } + } + Ok(()) + } + + // These re-construct the underlying `statrs` distribution each call - + // `args` was already validated in `new`, so these are expected to + // never fail; see `validate` above. + fn bernoulli(&self) -> StatrsBernoulli { + StatrsBernoulli::new(self.args[0]).expect("validated in TtcDist::new") + } + fn exponential(&self) -> Exp { + Exp::new(self.args[0]).expect("validated in TtcDist::new") + } + fn binomial(&self) -> StatrsBinomial { + // Python: `n, p = args` (args[0] = n, args[1] = p); statrs takes + // (p, n), the opposite order. + StatrsBinomial::new(self.args[1], self.args[0] as u64).expect("validated in TtcDist::new") + } + fn gamma(&self) -> Gamma { + // Python: `shape, scale = args`; statrs takes (shape, rate), where + // rate = 1 / scale. + Gamma::new(self.args[0], 1.0 / self.args[1]).expect("validated in TtcDist::new") + } + fn log_normal(&self) -> LogNormal { + // Python: `mean, std = args` matches statrs's (location, scale). + LogNormal::new(self.args[0], self.args[1]).expect("validated in TtcDist::new") + } + fn uniform(&self) -> StatrsUniform { + // Python: `low, high = args` matches statrs's (min, max). + StatrsUniform::new(self.args[0], self.args[1]).expect("validated in TtcDist::new") + } + + /// Return the expected value of this `TtcDist`. Closed-form (no RNG) - + /// exactly reproducible, unlike `sample_value`. + pub fn expected_value(&self) -> f64 { + let value = match self.function { + // Expected value of Bernoulli always assumes a successful + // trial for attack-step existence / defense initial state + // purposes - not the distribution's real mean. Mirrors + // Python's identically-worded special case. + DistFunction::Bernoulli => 1.0, + DistFunction::Exponential => self.exponential().mean().expect("exponential has mean"), + DistFunction::Binomial => self.binomial().mean().expect("binomial has mean"), + DistFunction::Gamma => self.gamma().mean().expect("gamma has mean"), + DistFunction::LogNormal => self.log_normal().mean().expect("log-normal has mean"), + DistFunction::Uniform => self.uniform().mean().expect("uniform has mean"), + }; + match (&self.combine_with, self.combine_op) { + (Some(rhs), Some(op)) => perform_operation(op, value, rhs.expected_value()), + _ => value, + } + } + + /// Sample a value from this `TtcDist`. Statistically, not bit-, + /// equivalent to Python's `sample_value` - see module docs / §2.1. + pub fn sample_value(&self, rng: &mut impl Rng) -> f64 { + let value = match self.function { + // Mirrors Python: sampled Bernoulli value is always 1.0 + // (existence/initial-state semantics, not a real draw). + DistFunction::Bernoulli => 1.0, + DistFunction::Exponential => rng.sample(self.exponential()), + DistFunction::Binomial => rng.sample(self.binomial()), + DistFunction::Gamma => rng.sample(self.gamma()), + DistFunction::LogNormal => rng.sample(self.log_normal()), + DistFunction::Uniform => rng.sample(self.uniform()), + }; + match (&self.combine_with, self.combine_op) { + (Some(rhs), Some(op)) => perform_operation(op, value, rhs.sample_value(rng)), + _ => value, + } + } + + /// The probability of success with the given `effort` (previous + /// attempts) - the base distribution's CDF at `effort`. Note this + /// reads only `self`'s own distribution, never `combine_with` - + /// mirrors Python's `success_probability` exactly (not a bug carried + /// over silently: Python's `dist.cdf(effort)` likewise never touches + /// `self.combine_with`). + pub fn success_probability(&self, effort: u64) -> f64 { + match self.function { + DistFunction::Bernoulli => self.bernoulli().cdf(effort), + DistFunction::Exponential => self.exponential().cdf(effort as f64), + DistFunction::Binomial => self.binomial().cdf(effort), + DistFunction::Gamma => self.gamma().cdf(effort as f64), + DistFunction::LogNormal => self.log_normal().cdf(effort as f64), + DistFunction::Uniform => self.uniform().cdf(effort as f64), + } + } + + /// Attempt to compromise a step by sampling a success probability + /// proportional to the TTC distribution, given previous attempts. + /// Ignores Bernoullis (see `success_probability`). + pub fn attempt_ttc_with_effort(&self, effort: u64, rng: &mut impl Rng) -> bool { + self.success_probability(effort) > rng.random::() + } + + /// Attempt bernoulli from a TTC distribution: if `self` is a + /// Bernoulli, sample it directly; otherwise dig into `combine_with` + /// (mirrors Python's recursive descent); if there's nothing left to + /// dig into, the attempt is unconditionally successful. + pub fn attempt_bernoulli(&self, rng: &mut impl Rng) -> bool { + if self.function == DistFunction::Bernoulli { + let threshold = self.args[0]; + rng.random::() <= threshold + } else if let Some(rhs) = &self.combine_with { + rhs.attempt_bernoulli(rng) + } else { + true + } + } + + /// Parse a `TtcDist` from the same JSON-ish dict shape Python's + /// `TTCDist.to_dict`/`from_dict` use - either a named/simple + /// `{"name": ..., "arguments": [...], "type": "function"}` leaf, or a + /// `{"lhs": ..., "rhs": ..., "type": }` combination node. + pub fn from_dict(value: &Value) -> Result { + let obj = value + .as_object() + .ok_or_else(|| TtcDistError::InvalidShape("expected a JSON object".to_string()))?; + + if let Some(name) = obj.get("name").and_then(Value::as_str) { + if let Some(named) = named_ttc_dist(name) { + return Ok(named); + } + let function: DistFunction = name + .parse() + .map_err(|()| TtcDistError::UnknownDistFunction(name.to_string()))?; + let args = obj + .get("arguments") + .and_then(Value::as_array) + .ok_or_else(|| TtcDistError::InvalidShape("missing 'arguments' array".to_string()))? + .iter() + .map(|v| { + v.as_f64().ok_or_else(|| { + TtcDistError::InvalidShape("non-numeric argument".to_string()) + }) + }) + .collect::, _>>()?; + return TtcDist::new(function, args); + } + + let lhs_value = obj + .get("lhs") + .ok_or_else(|| TtcDistError::InvalidShape("missing 'lhs'".to_string()))?; + let rhs_value = obj + .get("rhs") + .ok_or_else(|| TtcDistError::InvalidShape("missing 'rhs'".to_string()))?; + let op_name = obj + .get("type") + .and_then(Value::as_str) + .ok_or_else(|| TtcDistError::InvalidShape("missing 'type'".to_string()))?; + + let mut lhs = TtcDist::from_dict(lhs_value)?; + let op: Operation = op_name + .parse() + .map_err(|()| TtcDistError::UnknownOperation(op_name.to_string()))?; + let rhs = TtcDist::from_dict(rhs_value)?; + lhs.combine_op = Some(op); + lhs.combine_with = Some(Box::new(rhs)); + Ok(lhs) + } + + /// Inverse of `from_dict` - produces the same dict shape Python's + /// `to_dict` does. + pub fn to_dict(&self) -> Value { + let base = json!({ + "name": self.function.as_str(), + "arguments": self.args, + "type": "function", + }); + match (&self.combine_with, self.combine_op) { + (Some(rhs), Some(op)) => json!({ + "lhs": base, + "rhs": rhs.to_dict(), + "type": op.as_str(), + }), + _ => base, + } + } +} + +/// Mirrors Python's `named_ttc_dists` dict - the MAL-defined named TTC +/// distributions. Returns a fresh `TtcDist` (cheap: no precomputed +/// distribution object is cached on `TtcDist` itself) rather than a +/// reference into a shared static, since the Python original's per-entry +/// identity is not otherwise observable (`TTCDist.__eq__` is structural). +/// Names accepted by `named_ttc_dist`, in lookup order. +pub const NAMED_TTC_DISTS: [&str; 9] = [ + "EasyAndUncertain", + "HardAndUncertain", + "VeryHardAndUncertain", + "EasyAndCertain", + "HardAndCertain", + "VeryHardAndCertain", + "Enabled", + "Instant", + "Disabled", +]; + +/// Inverse of `named_ttc_dist`: the first predefined name whose +/// distribution equals `dist` (`Enabled` and `Instant` are identical, so +/// they come back as `Enabled`), or `None` for a custom distribution. +pub fn ttc_dist_name(dist: &TtcDist) -> Option<&'static str> { + NAMED_TTC_DISTS + .into_iter() + .find(|name| named_ttc_dist(name).as_ref() == Some(dist)) +} + +pub fn named_ttc_dist(name: &str) -> Option { + let simple = |function: DistFunction, args: &[f64]| { + TtcDist::new(function, args.to_vec()).expect("named_ttc_dist args are hand-verified valid") + }; + + Some(match name { + "EasyAndUncertain" => simple(DistFunction::Bernoulli, &[0.5]), + "HardAndUncertain" => simple(DistFunction::Exponential, &[0.1]).with_combine( + simple(DistFunction::Bernoulli, &[0.5]), + Operation::Multiplication, + ), + "VeryHardAndUncertain" => simple(DistFunction::Exponential, &[0.01]).with_combine( + simple(DistFunction::Bernoulli, &[0.5]), + Operation::Multiplication, + ), + "EasyAndCertain" => simple(DistFunction::Exponential, &[1.0]), + "HardAndCertain" => simple(DistFunction::Exponential, &[0.1]), + "VeryHardAndCertain" => simple(DistFunction::Exponential, &[0.01]), + "Enabled" => simple(DistFunction::Bernoulli, &[1.0]), + "Instant" => simple(DistFunction::Bernoulli, &[1.0]), + "Disabled" => simple(DistFunction::Bernoulli, &[0.0]), + _ => return None, + }) +} + +#[cfg(test)] +mod tests { + use super::*; + use rand::rngs::StdRng; + use rand::SeedableRng; + + fn approx(a: f64, b: f64) { + assert!((a - b).abs() < 1e-6, "expected {b}, got {a}"); + } + + fn dist(function: DistFunction, args: &[f64]) -> TtcDist { + TtcDist::new(function, args.to_vec()).unwrap() + } + + // --- expected_value: one per DistFunction variant, hand-computed --- + + #[test] + fn expected_value_bernoulli_is_always_one() { + approx(dist(DistFunction::Bernoulli, &[0.5]).expected_value(), 1.0); + approx(dist(DistFunction::Bernoulli, &[0.0]).expected_value(), 1.0); + approx(dist(DistFunction::Bernoulli, &[1.0]).expected_value(), 1.0); + } + + #[test] + fn expected_value_exponential_is_one_over_rate() { + approx( + dist(DistFunction::Exponential, &[0.1]).expected_value(), + 10.0, + ); + approx( + dist(DistFunction::Exponential, &[1.0]).expected_value(), + 1.0, + ); + approx( + dist(DistFunction::Exponential, &[0.01]).expected_value(), + 100.0, + ); + } + + #[test] + fn expected_value_binomial_is_n_times_p() { + approx( + dist(DistFunction::Binomial, &[10.0, 0.1]).expected_value(), + 1.0, + ); + } + + #[test] + fn expected_value_gamma_is_shape_times_scale() { + approx(dist(DistFunction::Gamma, &[1.0, 0.1]).expected_value(), 0.1); + } + + #[test] + fn expected_value_log_normal_matches_scipy_formula() { + let expected = (1.0_f64 + 0.1_f64.powi(2) / 2.0).exp(); + approx( + dist(DistFunction::LogNormal, &[1.0, 0.1]).expected_value(), + expected, + ); + } + + #[test] + fn expected_value_uniform_is_midpoint() { + approx( + dist(DistFunction::Uniform, &[1.0, 10.0]).expected_value(), + 5.5, + ); + } + + // --- combine_with/combine_op composition - ported 1:1 from + // test_ttc_utils.py::test_all_ttc_distributions, since expected_value + // is deterministic (no RNG) and therefore portable bit-for-bit, unlike + // sample_value. --- + + #[test] + fn combine_multiplication_hard_and_uncertain() { + let d = dist(DistFunction::Exponential, &[0.1]).with_combine( + dist(DistFunction::Bernoulli, &[0.5]), + Operation::Multiplication, + ); + approx(d.expected_value(), 10.0); + } + + #[test] + fn combine_addition_binomial_plus_exponential() { + let d = dist(DistFunction::Binomial, &[10.0, 0.1]) + .with_combine(dist(DistFunction::Exponential, &[0.1]), Operation::Addition); + let expected = (10.0 * 0.1) + (1.0 / 0.1); + approx(d.expected_value(), expected); + } + + #[test] + fn combine_subtraction_gamma_minus_binomial() { + let d = dist(DistFunction::Gamma, &[1.0, 0.1]).with_combine( + dist(DistFunction::Binomial, &[10.0, 0.1]), + Operation::Subtraction, + ); + let expected = (1.0 * 0.1) - (10.0 * 0.1); + approx(d.expected_value(), expected); + } + + #[test] + fn combine_multiplication_log_normal_times_gamma() { + let d = dist(DistFunction::LogNormal, &[1.0, 0.1]).with_combine( + dist(DistFunction::Gamma, &[1.0, 0.1]), + Operation::Multiplication, + ); + let lognorm_mean = (1.0_f64 + 0.1_f64.powi(2) / 2.0).exp(); + let gamma_mean = 1.0 * 0.1; + approx(d.expected_value(), lognorm_mean * gamma_mean); + } + + #[test] + fn combine_division_uniform_over_log_normal() { + let d = dist(DistFunction::Uniform, &[1.0, 10.0]).with_combine( + dist(DistFunction::LogNormal, &[1.0, 0.1]), + Operation::Division, + ); + let uniform_mean = (1.0 + 10.0) / 2.0; + let lognorm_mean = (1.0_f64 + 0.1_f64.powi(2) / 2.0).exp(); + approx(d.expected_value(), uniform_mean / lognorm_mean); + } + + #[test] + fn combine_multiplication_exponential_times_uniform() { + let d = dist(DistFunction::Exponential, &[0.5]).with_combine( + dist(DistFunction::Uniform, &[1.0, 10.0]), + Operation::Multiplication, + ); + let exp_mean = 1.0 / 0.5; + let uniform_mean = (1.0 + 10.0) / 2.0; + approx(d.expected_value(), exp_mean * uniform_mean); + } + + // --- success_probability / degenerate-distribution checks, as used + // by (the not-yet-ported) get_pre_enabled_defenses --- + + #[test] + fn success_probability_degenerate_bernoulli() { + // 'Disabled': Bernoulli(0.0) -> cdf(0) = 1 - p = 1.0 ("always + // succeeds", i.e. not pre-enabled in A3's caller). + approx( + named_ttc_dist("Disabled").unwrap().success_probability(0), + 1.0, + ); + // 'Enabled'/'Instant': Bernoulli(1.0) -> cdf(0) = 1 - p = 0.0 + // ("never succeeds" at effort 0, i.e. pre-enabled in A3's caller). + approx( + named_ttc_dist("Enabled").unwrap().success_probability(0), + 0.0, + ); + } + + #[test] + fn success_probability_monotonic_with_effort() { + let d = dist(DistFunction::Exponential, &[0.01]); + let low = d.success_probability(1); + let high = d.success_probability(500); + assert!(low < high, "{low} should be < {high}"); + approx(d.success_probability(0), 0.0); + } + + #[test] + fn success_probability_ignores_combine_with() { + // success_probability reads only self's own distribution, never + // combine_with - mirrors Python's dist.cdf(effort) exactly. + let plain = dist(DistFunction::Exponential, &[0.1]); + let combined = dist(DistFunction::Exponential, &[0.1]).with_combine( + dist(DistFunction::Bernoulli, &[0.5]), + Operation::Multiplication, + ); + approx( + plain.success_probability(5), + combined.success_probability(5), + ); + } + + // --- attempt_bernoulli / attempt_ttc_with_effort: RNG-touching, so + // only structural/statistical properties are asserted, not exact + // sampled values pinned to one seed - see §2.1. --- + + #[test] + fn attempt_bernoulli_degenerate_cases_are_deterministic() { + let mut rng = StdRng::seed_from_u64(10); + let always_true = dist(DistFunction::Bernoulli, &[1.0]); + for _ in 0..100 { + assert!(always_true.attempt_bernoulli(&mut rng)); + } + } + + #[test] + fn attempt_bernoulli_uncertain_gives_both_outcomes() { + let mut rng = StdRng::seed_from_u64(10); + let uncertain = dist(DistFunction::Bernoulli, &[0.5]); + let mut saw_true = false; + let mut saw_false = false; + for _ in 0..200 { + if uncertain.attempt_bernoulli(&mut rng) { + saw_true = true; + } else { + saw_false = true; + } + } + assert!(saw_true && saw_false); + } + + #[test] + fn attempt_bernoulli_digs_into_combine_with() { + let mut rng = StdRng::seed_from_u64(1); + // HardAndUncertain: Exponential(0.1) * Bernoulli(0.5) - attempt_bernoulli + // should recurse into the Bernoulli, not the Exponential. + let d = named_ttc_dist("HardAndUncertain").unwrap(); + let mut saw_true = false; + let mut saw_false = false; + for _ in 0..200 { + if d.attempt_bernoulli(&mut rng) { + saw_true = true; + } else { + saw_false = true; + } + } + assert!(saw_true && saw_false); + } + + #[test] + fn attempt_ttc_with_effort_success_rate_matches_probability() { + let mut rng = StdRng::seed_from_u64(42); + let d = dist(DistFunction::Exponential, &[0.1]); + let effort = 10; + let expected_p = d.success_probability(effort); + let trials = 5000; + let successes = (0..trials) + .filter(|_| d.attempt_ttc_with_effort(effort, &mut rng)) + .count(); + let observed_p = successes as f64 / trials as f64; + assert!( + (observed_p - expected_p).abs() < 0.05, + "observed {observed_p}, expected ~{expected_p}" + ); + } + + // --- from_dict / to_dict --- + + #[test] + fn to_dict_from_dict_roundtrip_simple() { + let d = dist(DistFunction::Exponential, &[0.1]); + let round_tripped = TtcDist::from_dict(&d.to_dict()).unwrap(); + assert_eq!(d, round_tripped); + } + + #[test] + fn to_dict_from_dict_roundtrip_combined() { + let d = dist(DistFunction::Gamma, &[1.0, 0.1]).with_combine( + dist(DistFunction::Binomial, &[10.0, 0.1]), + Operation::Subtraction, + ); + let round_tripped = TtcDist::from_dict(&d.to_dict()).unwrap(); + assert_eq!(d, round_tripped); + } + + #[test] + fn from_dict_resolves_named_dist_by_name() { + let value = json!({"name": "Instant"}); + let parsed = TtcDist::from_dict(&value).unwrap(); + assert_eq!(parsed, named_ttc_dist("Instant").unwrap()); + } + + #[test] + fn from_dict_rejects_unknown_function_name() { + let value = json!({"name": "NotARealDistribution", "arguments": [1.0]}); + assert!(matches!( + TtcDist::from_dict(&value), + Err(TtcDistError::UnknownDistFunction(_)) + )); + } + + #[test] + fn from_dict_rejects_unknown_operation() { + let value = json!({ + "lhs": {"name": "Exponential", "arguments": [0.1]}, + "rhs": {"name": "Bernoulli", "arguments": [0.5]}, + "type": "not_a_real_op", + }); + assert!(matches!( + TtcDist::from_dict(&value), + Err(TtcDistError::UnknownOperation(_)) + )); + } + + #[test] + fn new_rejects_wrong_arg_count() { + assert!(matches!( + TtcDist::new(DistFunction::Exponential, vec![0.1, 0.2]), + Err(TtcDistError::WrongArgCount { .. }) + )); + } + + #[test] + fn ttc_dist_name_inverts_named_ttc_dist() { + for name in NAMED_TTC_DISTS { + let dist = named_ttc_dist(name).expect("listed names are valid"); + let found = ttc_dist_name(&dist).expect("named dists have a name"); + assert_eq!(named_ttc_dist(found), Some(dist)); + } + assert_eq!( + ttc_dist_name(&named_ttc_dist("Instant").unwrap()), + Some("Enabled") + ); + let custom = TtcDist::new(DistFunction::Exponential, vec![3.0]).unwrap(); + assert_eq!(ttc_dist_name(&custom), None); + } + + #[test] + fn named_ttc_dist_matches_expected_structure() { + let hard_and_uncertain = named_ttc_dist("HardAndUncertain").unwrap(); + assert_eq!(hard_and_uncertain.function, DistFunction::Exponential); + assert_eq!(hard_and_uncertain.args, vec![0.1]); + assert_eq!( + hard_and_uncertain.combine_op, + Some(Operation::Multiplication) + ); + let combine_with = hard_and_uncertain.combine_with.as_ref().unwrap(); + assert_eq!(combine_with.function, DistFunction::Bernoulli); + assert_eq!(combine_with.args, vec![0.5]); + + assert!(named_ttc_dist("NotANamedDist").is_none()); + } +} diff --git a/core/malsim-core/src/types.rs b/core/malsim-core/src/types.rs new file mode 100644 index 00000000..1e606b93 --- /dev/null +++ b/core/malsim-core/src/types.rs @@ -0,0 +1,14 @@ +//! Collection aliases for sets and maps keyed by [`AttackGraphNodeId`]. +//! +//! Node ids are slotmap keys (not attacker-controlled), so the fast +//! non-cryptographic FxHash is safe here. It is also non-randomized, so +//! iteration order is deterministic for a given insertion sequence. + +use maltoolbox_attackgraph::AttackGraphNodeId; + +/// A set of attack graph node ids, hashed with FxHash. +pub type NodeSet = rustc_hash::FxHashSet; +/// A map keyed by attack graph node id, hashed with FxHash. +pub type NodeMap = rustc_hash::FxHashMap; +/// Dense per-node storage for values every node has; indexed by slot, no hashing. +pub type NodeDenseMap = slotmap::SecondaryMap; diff --git a/core/malsim-core/src/viability.rs b/core/malsim-core/src/viability.rs new file mode 100644 index 00000000..8e7fc746 --- /dev/null +++ b/core/malsim-core/src/viability.rs @@ -0,0 +1,673 @@ +//! Rust port of `python/malsim/mal_simulator/graph_processing.py`'s +//! viability propagation and pruning half - the counterpart of +//! `crate::necessity`, which ports the necessity half of the same file. +//! +//! Ports `evaluate_viability`, `_propagate_viability_from_node` (as +//! `propagate_viability_from_node`), `calculate_viability`, +//! `make_node_unviable` and `prune_unviable_and_unnecessary_nodes`. +//! Viability = whether a node can be traversed under any circumstances, or +//! whether the model structure (existence status), enabled defenses or +//! impossible attack steps make it unviable. Pruning removes `or`/`and` +//! nodes that are unviable or unnecessary (see `crate::necessity`). +//! +//! Signature shape mirrors `crate::necessity`: `node_id` is passed +//! alongside `&AttackGraphNode` (the node doesn't carry its own slotmap +//! key), per-node results are `NodeMap`, and node +//! sets are `NodeSet`. Python's `logger.debug`/ +//! `logger.error` calls are dropped - the crate has no logging dependency. +//! +//! Tests use `crate::test_fixtures`' dummy-language graphs, same as +//! `crate::necessity`'s tests. + +use crate::types::{NodeDenseMap, NodeMap, NodeSet}; +use std::fmt; + +use maltoolbox_attackgraph::{AttackGraph, AttackGraphNode, AttackGraphNodeId, GraphError}; +use maltoolbox_language::graph::attack_step::AttackStepType; + +/// Not `Clone`/`PartialEq`/`Eq` (unlike `crate::necessity::NecessityError`) +/// because the `Graph` variant wraps `maltoolbox_attackgraph::GraphError`, +/// which only derives `Debug` - same trade-off as +/// `crate::model_effects::ModelEffectsError`/`crate::model_state::ModelStateError`. +#[derive(Debug)] +pub enum ViabilityError { + /// Mirrors Python's `assert isinstance(node.existence_status, bool)` + /// for `exist`/`notExist` nodes. + MissingExistenceStatus(AttackGraphNodeId), + /// A node's viability was read before it was computed - mirrors + /// Python's `viability_per_node[node]` `KeyError` (the dict must be + /// pre-seeded for every node before `evaluate_viability` runs; see + /// `calculate_viability`). Also raised by + /// `prune_unviable_and_unnecessary_nodes` for an `or`/`and` node + /// missing from `viability_per_node`. + MissingViability(AttackGraphNodeId), + /// Mirrors Python's `necessity_per_node[node]` `KeyError` in + /// `prune_unviable_and_unnecessary_nodes`. + MissingNecessity(AttackGraphNodeId), + /// Wraps an error from `AttackGraph::remove_node` during pruning - + /// same convention as `ModelEffectsError::Graph`/`ModelStateError::Graph`. + Graph(GraphError), +} + +impl From for ViabilityError { + fn from(e: GraphError) -> Self { + ViabilityError::Graph(e) + } +} + +impl fmt::Display for ViabilityError { + fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { + match self { + ViabilityError::MissingExistenceStatus(id) => { + write!(f, "existence status not defined for node {id:?}") + } + ViabilityError::MissingViability(id) => { + write!(f, "viability not yet computed for node {id:?}") + } + ViabilityError::MissingNecessity(id) => { + write!(f, "necessity not computed for node {id:?}") + } + ViabilityError::Graph(e) => write!(f, "{e}"), + } + } +} + +impl std::error::Error for ViabilityError {} + +/// Port of `evaluate_viability`. Takes `node_id` alongside `node` since +/// `AttackGraphNode` doesn't carry its own slotmap key - needed to check +/// membership in `impossible_attack_steps`/`enabled_defenses` and for +/// error context. +pub fn evaluate_viability( + node_id: AttackGraphNodeId, + node: &AttackGraphNode, + viability_per_node: &NodeMap, + enabled_defenses: &NodeSet, + impossible_attack_steps: &NodeSet, +) -> Result { + if impossible_attack_steps.contains(&node_id) { + // Impossible step is not viable + return Ok(false); + } + + let parent_viability = |id: &AttackGraphNodeId| -> Result { + viability_per_node + .get(id) + .copied() + .ok_or(ViabilityError::MissingViability(*id)) + }; + + match node.step_type { + AttackStepType::Exist => node + .existence_status + .ok_or(ViabilityError::MissingExistenceStatus(node_id)), + AttackStepType::NotExist => node + .existence_status + .map(|exists| !exists) + .ok_or(ViabilityError::MissingExistenceStatus(node_id)), + AttackStepType::Defense => Ok(!enabled_defenses.contains(&node_id)), + // Python: `any(viability_per_node[p] for p in node.parents) or not + // node.parents` - `any(())` is `False`, so the empty-parents case + // genuinely needs the explicit `True` here (the mirror image of + // necessity's "and" branch). + AttackStepType::Or => { + if node.parents.is_empty() { + return Ok(true); + } + for parent in &node.parents { + if parent_viability(parent)? { + return Ok(true); + } + } + Ok(false) + } + // Python: `all(viability_per_node[p] for p in node.parents) or not + // node.parents` - `all(())` is already `True`, so the `or not + // node.parents` is redundant for "and"; this loop mirrors that + // without the redundant check. + AttackStepType::And => { + for parent in &node.parents { + if !parent_viability(parent)? { + return Ok(false); + } + } + Ok(true) + } + } +} + +/// Port of `_propagate_viability_from_node`. +pub fn propagate_viability_from_node( + node_id: AttackGraphNodeId, + graph: &AttackGraph, + viability_per_node: &mut NodeMap, + impossible_attack_steps: &NodeSet, +) -> Result { + let mut changed_nodes = NodeSet::default(); + for &child_id in &graph.nodes[node_id].children { + let child = &graph.nodes[child_id]; + // Python passes `frozenset()` (not the real `enabled_defenses`) + // into this inner `evaluate_viability` call - mirrored exactly. + // Note that unlike necessity, a `defense` child reached here + // *would* be re-evaluated as viable (it's not in the empty set); + // that is Python's behaviour too, and defense nodes normally have + // no parents in a generated graph, so it isn't reached in practice. + let is_viable = evaluate_viability( + child_id, + child, + viability_per_node, + &NodeSet::default(), + impossible_attack_steps, + )?; + // Python indexes `viability_per_node[child]` (`KeyError` if + // missing) - surfaced as `MissingViability` rather than treating + // a missing entry as "changed". + let current = viability_per_node + .get(&child_id) + .copied() + .ok_or(ViabilityError::MissingViability(child_id))?; + if is_viable != current { + viability_per_node.insert(child_id, is_viable); + changed_nodes.insert(child_id); + changed_nodes.extend(propagate_viability_from_node( + child_id, + graph, + viability_per_node, + impossible_attack_steps, + )?); + } + } + Ok(changed_nodes) +} + +/// Port of `calculate_viability`. +pub fn calculate_viability( + graph: &AttackGraph, + enabled_defenses: &NodeSet, + impossible_attack_steps: &NodeSet, +) -> Result, ViabilityError> { + let mut viability_per_node: NodeMap = graph.nodes.keys().map(|id| (id, true)).collect(); + + // Unlike `calculate_necessity`, Python evaluates *every* node here, + // not just `exist`/`notExist`/`defense` ones. + let node_ids: Vec = graph.nodes.keys().collect(); + for node_id in node_ids { + let node = &graph.nodes[node_id]; + let is_viable = evaluate_viability( + node_id, + node, + &viability_per_node, + enabled_defenses, + impossible_attack_steps, + )?; + viability_per_node.insert(node_id, is_viable); + if !is_viable { + propagate_viability_from_node( + node_id, + graph, + &mut viability_per_node, + impossible_attack_steps, + )?; + } + } + Ok(viability_per_node) +} + +/// Port of `make_node_unviable`. Python mutates `viability_per_node` in +/// place *and* returns it alongside the changed set; here the map is +/// mutated through `&mut` and only the set of nodes made unviable is +/// returned (returning the map too would be redundant with `&mut`). +pub fn make_node_unviable( + node_id: AttackGraphNodeId, + graph: &AttackGraph, + viability_per_node: &mut NodeMap, + impossible_attack_steps: &NodeSet, +) -> Result { + viability_per_node.insert(node_id, false); + propagate_viability_from_node(node_id, graph, viability_per_node, impossible_attack_steps) +} + +/// Port of `prune_unviable_and_unnecessary_nodes`. Removes every `or`/`and` +/// node that is unviable or unnecessary from `graph`. +pub fn prune_unviable_and_unnecessary_nodes( + graph: &mut AttackGraph, + viability_per_node: &NodeMap, + necessity_per_node: &NodeDenseMap, +) -> Result<(), ViabilityError> { + let viability = |id: AttackGraphNodeId| -> Result { + viability_per_node + .get(&id) + .copied() + .ok_or(ViabilityError::MissingViability(id)) + }; + let necessity = |id: AttackGraphNodeId| -> Result { + necessity_per_node + .get(id) + .copied() + .ok_or(ViabilityError::MissingNecessity(id)) + }; + + // Python collects into a `set`; a `Vec` in graph order is used here so + // removal order is deterministic (each node is pushed at most once). + let mut nodes_to_remove = Vec::new(); + for (node_id, node) in &graph.nodes { + if matches!(node.step_type, AttackStepType::Or | AttackStepType::And) { + // Python: `not viability_per_node[node] or not + // necessity_per_node[node]` - short-circuits, so necessity is + // only read here for viable nodes... + let remove = !viability(node_id)? || !necessity(node_id)?; + if remove { + // ...but Python's removal loop then reads + // `necessity_per_node[node]` unconditionally for its debug + // log ('unviable' vs 'unnecessary'), so a missing necessity + // entry for any node to remove is a `KeyError` there. + // Checked up front so nothing is removed before erroring. + necessity(node_id)?; + nodes_to_remove.push(node_id); + } + } + } + + // Do the removal separately so we don't remove nodes from the + // collection we are looping over. + for node_id in nodes_to_remove { + graph.remove_node(node_id)?; + } + Ok(()) +} + +#[cfg(test)] +mod tests { + use super::*; + use crate::necessity::calculate_necessity; + use crate::test_fixtures::{add_dummy_node, dummy_graph}; + + // Port of `tests/test_graph_processing.py::test_viability_viable_nodes`. + #[test] + fn calculate_viability_viable_nodes() { + let mut graph = dummy_graph(); + + // Exists + existence -> viable + let exist_node = add_dummy_node(&mut graph, "DummyExistAttackStep"); + graph.nodes[exist_node].existence_status = Some(true); + + // NotExists + nonexistence -> viable + let not_exist_node = add_dummy_node(&mut graph, "DummyNotExistAttackStep"); + graph.nodes[not_exist_node].existence_status = Some(false); + + // defense not enabled -> viable + let defense_step_node = add_dummy_node(&mut graph, "DummyDefenseAttackStep"); + + // or-node with viable parent -> viable (Python only sets + // `or_node.parents`, not the parent's `children` - mirrored) + let or_node = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let or_node_parent = add_dummy_node(&mut graph, "DummyOrAttackStep"); + graph.nodes[or_node].parents.insert(or_node_parent); + + // and-node with no parents -> viable (Python's unused `ttc_values` + // bookkeeping for this node is dropped) + let and_node = add_dummy_node(&mut graph, "DummyAndAttackStep"); + + // and-node with viable parents -> viable + let and_node2 = add_dummy_node(&mut graph, "DummyAndAttackStep"); + let and_node_parent1 = add_dummy_node(&mut graph, "DummyAndAttackStep"); + let and_node_parent2 = add_dummy_node(&mut graph, "DummyAndAttackStep"); + graph.nodes[and_node2].parents = [and_node_parent1, and_node_parent2].into_iter().collect(); + + let enabled_defenses = NodeSet::default(); + let viable_nodes = + calculate_viability(&graph, &enabled_defenses, &NodeSet::default()).unwrap(); + + // Python asserts `node in viable_nodes` (dict key membership, which + // holds for every node); the value check below is the stronger + // intended property. + for node in [ + exist_node, + not_exist_node, + defense_step_node, + or_node, + and_node, + and_node2, + ] { + assert!(viable_nodes.contains_key(&node)); + assert!(viable_nodes[&node]); + } + } + + // Port of `tests/test_graph_processing.py::test_viability_unviable_nodes`. + #[test] + fn calculate_viability_unviable_nodes() { + let mut impossible_attack_steps = NodeSet::default(); + let mut graph = dummy_graph(); + + // exists, existence_status = False -> not viable + let exist_node = add_dummy_node(&mut graph, "DummyExistAttackStep"); + graph.nodes[exist_node].existence_status = Some(false); + + // notExists, existence_status = True -> not viable + let not_exist_node = add_dummy_node(&mut graph, "DummyNotExistAttackStep"); + graph.nodes[not_exist_node].existence_status = Some(true); + + // Defense status on -> not viable + let defense_step_node = add_dummy_node(&mut graph, "DummyDefenseAttackStep"); + + // or-node with no viable parent -> non viable + let or_node = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let unviable_or_node_parent = add_dummy_node(&mut graph, "DummyOrAttackStep"); + graph.nodes[or_node].parents.insert(unviable_or_node_parent); + graph.nodes[unviable_or_node_parent] + .children + .insert(or_node); + impossible_attack_steps.insert(unviable_or_node_parent); + + // and-node with two non-viable parents -> non viable + let and_node = add_dummy_node(&mut graph, "DummyAndAttackStep"); + let unviable_and_node_parent1 = add_dummy_node(&mut graph, "DummyAndAttackStep"); + let unviable_and_node_parent2 = add_dummy_node(&mut graph, "DummyAndAttackStep"); + graph.nodes[and_node].parents = [unviable_and_node_parent1, unviable_and_node_parent2] + .into_iter() + .collect(); + // Python adds `and_node` to parent1's children twice and never to + // parent2's - mirrored (one insert into parent1 only). + graph.nodes[unviable_and_node_parent1] + .children + .insert(and_node); + impossible_attack_steps.insert(unviable_and_node_parent1); + impossible_attack_steps.insert(unviable_and_node_parent2); + + let enabled_defenses: NodeSet = [defense_step_node].into_iter().collect(); + let viability_per_node = + calculate_viability(&graph, &enabled_defenses, &impossible_attack_steps).unwrap(); + + assert!(!viability_per_node[&unviable_or_node_parent]); + assert!(!viability_per_node[&unviable_and_node_parent1]); + assert!(!viability_per_node[&unviable_and_node_parent2]); + + assert!(!viability_per_node[&exist_node]); + assert!(!viability_per_node[¬_exist_node]); + assert!(!viability_per_node[&defense_step_node]); + assert!(!viability_per_node[&or_node]); + assert!(!viability_per_node[&and_node]); + } + + // Port of + // `tests/test_graph_processing.py::test_analyzers_apriori_propagate_viability`. + #[test] + fn propagate_viability_from_node_updates_downstream_or_and_and_nodes() { + let mut graph = dummy_graph(); + + let vp1 = add_dummy_node(&mut graph, "DummyDefenseAttackStep"); + let vp2 = add_dummy_node(&mut graph, "DummyDefenseAttackStep"); + let uvp1 = add_dummy_node(&mut graph, "DummyDefenseAttackStep"); + let uvp2 = add_dummy_node(&mut graph, "DummyDefenseAttackStep"); + + let or_1vp = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let or_2uvp = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let and_1uvp = add_dummy_node(&mut graph, "DummyAndAttackStep"); + let and_2vp = add_dummy_node(&mut graph, "DummyAndAttackStep"); + + graph.nodes[or_1vp].parents = [vp1, uvp1].into_iter().collect(); + graph.nodes[or_2uvp].parents = [uvp1, uvp2].into_iter().collect(); + graph.nodes[and_1uvp].parents = [vp1, uvp1].into_iter().collect(); + graph.nodes[and_2vp].parents = [vp1, vp2].into_iter().collect(); + + graph.nodes[vp1].children = [or_1vp, and_1uvp, and_2vp].into_iter().collect(); + graph.nodes[vp2].children = [and_2vp].into_iter().collect(); + graph.nodes[uvp1].children = [or_1vp, or_2uvp, and_1uvp].into_iter().collect(); + graph.nodes[uvp2].children = [or_2uvp].into_iter().collect(); + + let mut viability_per_node = + calculate_viability(&graph, &NodeSet::default(), &NodeSet::default()).unwrap(); + + // Make unviable + viability_per_node.insert(uvp1, false); + viability_per_node.insert(uvp2, false); + + let mut changed_nodes = NodeSet::default(); + for &parent in &[vp1, vp2, uvp1, uvp2] { + changed_nodes.extend( + propagate_viability_from_node( + parent, + &graph, + &mut viability_per_node, + &NodeSet::default(), + ) + .unwrap(), + ); + } + + assert_eq!(changed_nodes, [or_2uvp, and_1uvp].into_iter().collect()); + + for node in [vp1, vp2, or_1vp, and_2vp] { + assert!(viability_per_node[&node]); + } + for node in [uvp1, uvp2, or_2uvp, and_1uvp] { + assert!(!viability_per_node[&node]); + } + } + + // Port of + // `tests/test_graph_processing.py::test_analyzers_apriori_prune_unviable_and_unnecessary_nodes`. + // + // The Python test runs against the `model` fixture (coreLang + + // `tests/testdata/models/simple_test_model.yml`), which has no Rust + // fixture equivalent in `crate::test_fixtures`. Ported against a + // hand-built dummy-language graph instead, exercising the same + // property: pick the first `or` node and make it unnecessary, the + // first `and` node and make it unviable, prune, and check both are + // gone. + #[test] + fn prune_unviable_and_unnecessary_nodes_removes_marked_nodes() { + let mut graph = dummy_graph(); + + // A plain or -> and -> or -> and chain with a parentless root: + // everything is viable and necessary until marked otherwise, so + // only the two manual marks below can cause pruning. + let or_a = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let and_a = add_dummy_node(&mut graph, "DummyAndAttackStep"); + let or_b = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let and_b = add_dummy_node(&mut graph, "DummyAndAttackStep"); + + graph.nodes[and_a].parents.insert(or_a); + graph.nodes[or_b].parents.insert(and_a); + graph.nodes[and_b].parents.insert(or_b); + graph.nodes[or_a].children.insert(and_a); + graph.nodes[and_a].children.insert(or_b); + graph.nodes[or_b].children.insert(and_b); + + // Pick out an or node and make it non-necessary, and an and node + // to make unviable - mirrors Python's `next(node for node in + // graph.nodes.values() if node.type == ...)`. + let node_to_make_unnecessary = graph + .nodes + .iter() + .find(|(_, n)| n.step_type == AttackStepType::Or) + .map(|(id, _)| id) + .unwrap(); + let node_to_make_unviable = graph + .nodes + .iter() + .find(|(_, n)| n.step_type == AttackStepType::And) + .map(|(id, _)| id) + .unwrap(); + + let mut viability_per_node = + calculate_viability(&graph, &NodeSet::default(), &NodeSet::default()).unwrap(); + let mut necessity_per_node = calculate_necessity(&graph, &NodeSet::default()).unwrap(); + necessity_per_node.insert(node_to_make_unnecessary, false); + viability_per_node.insert(node_to_make_unviable, false); + + prune_unviable_and_unnecessary_nodes(&mut graph, &viability_per_node, &necessity_per_node) + .unwrap(); + + // Make sure the node was pruned + assert!(!graph.nodes.contains_key(node_to_make_unviable)); + assert!(!graph.nodes.contains_key(node_to_make_unnecessary)); + } + + // Second port of the same Python test, this time against a real + // generated attack graph (wiperLang + `wiper_model.yml`) rather than a + // hand-built chain - closer to the Python test's `model` fixture, which + // is also a language-compiled, model-generated graph. + #[test] + fn prune_unviable_and_unnecessary_nodes_on_generated_graph() { + let (mut graph, _model) = crate::test_fixtures::wiper_attack_graph(); + + let node_to_make_unnecessary = graph + .nodes + .iter() + .find(|(_, n)| n.step_type == AttackStepType::Or) + .map(|(id, _)| id) + .unwrap(); + let node_to_make_unviable = graph + .nodes + .iter() + .find(|(_, n)| n.step_type == AttackStepType::And) + .map(|(id, _)| id) + .unwrap(); + + let mut viability_per_node = + calculate_viability(&graph, &NodeSet::default(), &NodeSet::default()).unwrap(); + let mut necessity_per_node = calculate_necessity(&graph, &NodeSet::default()).unwrap(); + necessity_per_node.insert(node_to_make_unnecessary, false); + viability_per_node.insert(node_to_make_unviable, false); + + prune_unviable_and_unnecessary_nodes(&mut graph, &viability_per_node, &necessity_per_node) + .unwrap(); + + assert!(!graph.nodes.contains_key(node_to_make_unviable)); + assert!(!graph.nodes.contains_key(node_to_make_unnecessary)); + } + + // Extra (no Python counterpart): `make_node_unviable` marks the node + // unviable and returns exactly the downstream nodes it made unviable. + #[test] + fn make_node_unviable_propagates_and_returns_changed_nodes() { + let mut graph = dummy_graph(); + + let root = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let other = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let and_child = add_dummy_node(&mut graph, "DummyAndAttackStep"); + let or_child = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let grandchild = add_dummy_node(&mut graph, "DummyOrAttackStep"); + + // and_child needs both root and other -> unviable once root is. + // or_child has `other` as an alternative parent -> stays viable. + graph.nodes[and_child].parents = [root, other].into_iter().collect(); + graph.nodes[or_child].parents = [root, other].into_iter().collect(); + graph.nodes[grandchild].parents.insert(and_child); + graph.nodes[root].children = [and_child, or_child].into_iter().collect(); + graph.nodes[other].children = [and_child, or_child].into_iter().collect(); + graph.nodes[and_child].children.insert(grandchild); + + let mut viability_per_node = + calculate_viability(&graph, &NodeSet::default(), &NodeSet::default()).unwrap(); + assert!(viability_per_node.values().all(|&v| v)); + + let made_unviable = + make_node_unviable(root, &graph, &mut viability_per_node, &NodeSet::default()).unwrap(); + + assert_eq!(made_unviable, [and_child, grandchild].into_iter().collect()); + assert!(!viability_per_node[&root]); + assert!(!viability_per_node[&and_child]); + assert!(!viability_per_node[&grandchild]); + assert!(viability_per_node[&other]); + assert!(viability_per_node[&or_child]); + } + + // Extra (no Python counterpart): mirrors Python's + // `assert isinstance(node.existence_status, bool)`. + #[test] + fn evaluate_viability_missing_existence_status_errors() { + let mut graph = dummy_graph(); + let exist_node = add_dummy_node(&mut graph, "DummyExistAttackStep"); + let not_exist_node = add_dummy_node(&mut graph, "DummyNotExistAttackStep"); + + let err = + calculate_viability(&graph, &NodeSet::default(), &NodeSet::default()).unwrap_err(); + assert!(matches!( + err, + ViabilityError::MissingExistenceStatus(id) if id == exist_node + )); + + let err = evaluate_viability( + not_exist_node, + &graph.nodes[not_exist_node], + &NodeMap::default(), + &NodeSet::default(), + &NodeSet::default(), + ) + .unwrap_err(); + assert!(matches!( + err, + ViabilityError::MissingExistenceStatus(id) if id == not_exist_node + )); + } + + // Extra (no Python counterpart): an impossible attack step is unviable + // regardless of type - checked before the type match, so it wins even + // over an otherwise-erroring node. + #[test] + fn evaluate_viability_impossible_step_short_circuits() { + let mut graph = dummy_graph(); + let exist_node = add_dummy_node(&mut graph, "DummyExistAttackStep"); + let impossible: NodeSet = [exist_node].into_iter().collect(); + + let is_viable = evaluate_viability( + exist_node, + &graph.nodes[exist_node], + &NodeMap::default(), + &NodeSet::default(), + &impossible, + ) + .unwrap(); + assert!(!is_viable); + } + + // Extra (no Python counterpart): mirrors Python's + // `viability_per_node[parent]` `KeyError`. + #[test] + fn evaluate_viability_missing_parent_viability_errors() { + let mut graph = dummy_graph(); + let parent = add_dummy_node(&mut graph, "DummyOrAttackStep"); + let child = add_dummy_node(&mut graph, "DummyAndAttackStep"); + graph.nodes[child].parents.insert(parent); + + let err = evaluate_viability( + child, + &graph.nodes[child], + &NodeMap::default(), + &NodeSet::default(), + &NodeSet::default(), + ) + .unwrap_err(); + assert!(matches!(err, ViabilityError::MissingViability(id) if id == parent)); + } + + // Extra (no Python counterpart): mirrors Python's + // `necessity_per_node[node]` `KeyError` in pruning, and checks nothing + // is removed when it errors. + #[test] + fn prune_missing_necessity_errors_without_removing() { + let mut graph = dummy_graph(); + let or_node = add_dummy_node(&mut graph, "DummyOrAttackStep"); + + let viability_per_node: NodeMap<_> = [(or_node, false)].into_iter().collect(); + let err = prune_unviable_and_unnecessary_nodes( + &mut graph, + &viability_per_node, + &NodeDenseMap::default(), + ) + .unwrap_err(); + assert!(matches!(err, ViabilityError::MissingNecessity(id) if id == or_node)); + assert!(graph.nodes.contains_key(or_node)); + + let err = prune_unviable_and_unnecessary_nodes( + &mut graph, + &NodeMap::default(), + &NodeDenseMap::default(), + ) + .unwrap_err(); + assert!(matches!(err, ViabilityError::MissingViability(id) if id == or_node)); + } +} diff --git a/core/malsim-core/tests/history.rs b/core/malsim-core/tests/history.rs new file mode 100644 index 00000000..c4e9414c --- /dev/null +++ b/core/malsim-core/tests/history.rs @@ -0,0 +1,325 @@ +//! `SimState::snapshot_at` and the episode history (`PORTING_NOTES.md` +//! §14): the state rebuilt for an earlier iteration must equal the state +//! the simulator actually had at that iteration, on the static and the +//! dyna path (including steps whose model effects change the graph). + +use std::collections::{BTreeMap, HashMap, HashSet}; +use std::path::PathBuf; + +use malsim_core::dyna_simulator::DynaSimulator; +use malsim_core::history::StateSnapshot; +use malsim_core::model_effects::ModEffectOp; +use malsim_core::scenario::{load_scenario_dict, Scenario}; +use malsim_core::simulator::{SimState, Simulator}; +use malsim_core::types::NodeSet; +use maltoolbox_attackgraph::AttackGraphNodeId; +use maltoolbox_language::graph::model_effect::ModelEffectType; +use rand::rngs::StdRng; +use rand::{RngExt, SeedableRng}; +use slotmap::Key; + +fn testdata(path: &str) -> PathBuf { + PathBuf::from(env!("CARGO_MANIFEST_DIR")) + .join("../../tests/testdata") + .join(path) +} + +fn scenario(file: &str) -> Scenario { + let dict = load_scenario_dict(testdata("scenarios").join(file)).unwrap(); + Scenario::from_dict(&dict).unwrap() +} + +enum Driver { + Static(Simulator), + Dyna(DynaSimulator), +} + +impl Driver { + fn state(&self) -> &SimState { + match self { + Driver::Static(s) => s.state().unwrap(), + Driver::Dyna(s) => s.state().unwrap(), + } + } + + fn step(&mut self, actions: &HashMap>) -> Vec { + match self { + Driver::Static(s) => { + s.step(actions).unwrap(); + Vec::new() + } + Driver::Dyna(s) => s.step(actions).unwrap().step_modification_record, + } + } +} + +fn current(state: &SimState) -> StateSnapshot { + state.snapshot_at(state.history().iteration()).unwrap() +} + +fn sorted(ids: &NodeSet) -> Vec { + let mut v: Vec<_> = ids.iter().copied().collect(); + v.sort(); + v +} + +fn assert_same(label: &str, expected: &StateSnapshot, actual: &StateSnapshot) { + assert_eq!(expected.iteration, actual.iteration, "{label}: iteration"); + assert_eq!( + expected.graph_state, actual.graph_state, + "{label}: graph_state" + ); + assert_eq!( + sorted(&expected.enabled_defenses), + sorted(&actual.enabled_defenses), + "{label}: enabled_defenses" + ); + assert_eq!( + expected.modification_record.len(), + actual.modification_record.len(), + "{label}: modification_record" + ); + assert_eq!(expected.attackers, actual.attackers, "{label}: attackers"); + assert_eq!(expected.defenders, actual.defenders, "{label}: defenders"); +} + +/// What a run exercised, so the dyna test can check that the delta-based +/// `snapshot_at` was tested on the cases that matter. +#[derive(Debug, Default, Clone, Copy)] +struct Coverage { + /// Model operations seen. + modifications: usize, + /// Steps with at least one additive operation. + additive_steps: usize, + /// Steps with at least one subtractive operation. + subtractive_steps: usize, + /// New node keys reusing the slot index of an earlier key. + reused_slots: usize, + /// Surviving nodes whose necessity changed between two captures. + necessity_changes: usize, + /// New keys in `pre_enabled_defenses`. + new_pre_enabled: usize, + /// New keys in `impossible_attack_steps`. + new_impossible: usize, + /// Steps where `enabled_defenses` grew by an action. + action_enabled_steps: usize, +} + +impl Coverage { + fn add(&mut self, other: Coverage) { + self.modifications += other.modifications; + self.additive_steps += other.additive_steps; + self.subtractive_steps += other.subtractive_steps; + self.reused_slots += other.reused_slots; + self.necessity_changes += other.necessity_changes; + self.new_pre_enabled += other.new_pre_enabled; + self.new_impossible += other.new_impossible; + self.action_enabled_steps += other.action_enabled_steps; + } +} + +fn slot(id: AttackGraphNodeId) -> u32 { + id.data().as_ffi() as u32 +} + +/// Runs `file` for up to `steps` steps with random actions, +/// capturing the live state after reset and after every step, then checks +/// `snapshot_at` against every capture. Returns what the run covered, so +/// dyna callers can check effects actually ran. +fn check(file: &str, dyna: bool, seed: u64, steps: u64) -> Coverage { + let scenario = scenario(file); + let mut driver = if dyna { + let mut sim = DynaSimulator::new(scenario.attack_graph.clone(), scenario.model.clone()); + sim.restore_model().unwrap(); + Driver::Dyna(sim) + } else { + Driver::Static(Simulator::new(scenario.attack_graph.clone())) + }; + let mut rng = StdRng::seed_from_u64(seed); + let agents = scenario.flatten_agents(&mut rng).unwrap(); + match &mut driver { + Driver::Static(sim) => sim.reset(&scenario.sim_settings, agents, seed).map(|_| ()), + Driver::Dyna(sim) => sim.reset(&scenario.sim_settings, agents, seed).map(|_| ()), + } + .unwrap(); + + let mut captured = vec![current(driver.state())]; + let mut coverage = Coverage::default(); + let mut seen_keys: HashSet = + captured[0].graph_state.necessity_per_node.keys().collect(); + let mut seen_slots: HashSet = seen_keys.iter().map(|&k| slot(k)).collect(); + for _ in 0..steps { + if driver.state().done() { + break; + } + let state = driver.state(); + let mut actions: HashMap> = HashMap::new(); + let surfaces = state + .attackers + .iter() + .map(|(n, a)| (n, &a.action_surface)) + .chain(state.defenders.iter().map(|(n, d)| (n, &d.action_surface))); + for (name, surface) in surfaces { + // At most one action per agent, as the policies do: on the dyna + // path an earlier action's model effect may remove a later one. + let surface = sorted(surface); + if !surface.is_empty() && rng.random_bool(0.8) { + let pick = surface[rng.random_range(0..surface.len())]; + actions.insert(name.clone(), vec![pick]); + } + } + let ops = driver.step(&actions); + coverage.modifications += ops.len(); + let is_additive = |op: &&ModEffectOp| matches!(op.effect_type(), ModelEffectType::Additive); + if ops.iter().any(|op| is_additive(&op)) { + coverage.additive_steps += 1; + } + if ops.iter().any(|op| !is_additive(&op)) { + coverage.subtractive_steps += 1; + } + captured.push(current(driver.state())); + + let prev = &captured[captured.len() - 2].graph_state; + let prev_enabled = &captured[captured.len() - 2].enabled_defenses; + let cur = &captured[captured.len() - 1]; + let now = &cur.graph_state; + for key in now.necessity_per_node.keys() { + if seen_keys.insert(key) && !seen_slots.insert(slot(key)) { + coverage.reused_slots += 1; + } + } + for (key, necessary) in &now.necessity_per_node { + if prev + .necessity_per_node + .get(key) + .is_some_and(|p| p != necessary) + { + coverage.necessity_changes += 1; + } + } + coverage.new_pre_enabled += now + .pre_enabled_defenses + .difference(&prev.pre_enabled_defenses) + .count(); + coverage.new_impossible += now + .impossible_attack_steps + .difference(&prev.impossible_attack_steps) + .count(); + if cur + .enabled_defenses + .iter() + .any(|d| !prev_enabled.contains(d) && !now.pre_enabled_defenses.contains(d)) + { + coverage.action_enabled_steps += 1; + } + } + + let state = driver.state(); + let record = state.episode_record(); + for (k, expected) in captured.iter().enumerate() { + let actual = state.snapshot_at(k as u64).unwrap(); + assert_same(&format!("{file} iteration {k}"), expected, &actual); + let archived = record.snapshot_at(k as u64).unwrap(); + assert_same( + &format!("{file} archived iteration {k}"), + expected, + &archived, + ); + } + assert!(state.snapshot_at(state.history().iteration() + 1).is_none()); + coverage +} + +#[test] +fn static_snapshots_match_live_state() { + check("traininglang_scenario.yml", false, 1, 30); + check("traininglang_observability_scenario.yml", false, 2, 30); +} + +#[test] +fn dyna_snapshots_match_live_state() { + let mut files = vec![ + "actions_effects_scenario.yml".to_string(), + "dynamic_remove_many_assoc_scenario.yml".to_string(), + "dyna_defense_scenario.yml".to_string(), + ]; + let root = testdata("scenarios/dynamal_example_scenarios"); + for dir in ["basic", "intermediate", "advanced"] { + let mut entries: Vec = std::fs::read_dir(root.join(dir)) + .unwrap() + .map(|e| e.unwrap().file_name().to_string_lossy().into_owned()) + .collect(); + entries.sort(); + for name in entries { + files.push(format!("dynamal_example_scenarios/{dir}/{name}")); + } + } + let mut coverage = Coverage::default(); + for (i, file) in files.iter().enumerate() { + for seed in 0..3 { + coverage.add(check(file, true, i as u64 * 10 + seed, 60)); + } + } + eprintln!("dyna snapshot coverage: {coverage:?}"); + assert!( + coverage.modifications > 0, + "no model effect ran; test proves nothing" + ); + assert!(coverage.additive_steps > 0, "no step added assets"); + assert!(coverage.subtractive_steps > 0, "no step removed assets"); + assert!(coverage.reused_slots > 0, "no node reused a freed slot"); + assert!( + coverage.necessity_changes > 0, + "no surviving node changed necessity" + ); + assert!(coverage.new_pre_enabled > 0, "no new pre-enabled defense"); + assert!(coverage.new_impossible > 0, "no new impossible attack step"); + assert!( + coverage.action_enabled_steps > 0, + "no defense was enabled by an action" + ); +} + +/// `recording(k)` lists each agent's newly performed nodes, requested +/// actions first, in request order. +#[test] +fn recording_lists_requested_actions_first() { + let scenario = scenario("traininglang_scenario.yml"); + let mut sim = Simulator::new(scenario.attack_graph.clone()); + let agents = scenario + .flatten_agents(&mut StdRng::seed_from_u64(0)) + .unwrap(); + sim.reset(&scenario.sim_settings, agents, 0).unwrap(); + assert!(sim.state().unwrap().history().recording(0).is_none()); + assert!(sim.state().unwrap().history().recording(1).is_none()); + + let state = sim.state().unwrap(); + let (attacker, runtime) = state.attackers.iter().next().unwrap(); + let mut requested = sorted(&runtime.action_surface); + requested.reverse(); + let attacker = attacker.clone(); + let actions = HashMap::from([(attacker.clone(), requested.clone())]); + let outcome = sim.step(&actions).unwrap(); + + let state = sim.state().unwrap(); + let recording: &BTreeMap> = + state.history().recording(1).unwrap(); + let recorded = &recording[&attacker]; + let new_nodes = &outcome.attackers[&attacker].step_performed_nodes; + let direct: Vec = requested + .iter() + .copied() + .filter(|id| new_nodes.contains(id)) + .collect(); + assert!(!direct.is_empty()); + assert_eq!(&recorded[..direct.len()], direct.as_slice()); + assert_eq!( + sorted(&recorded.iter().copied().collect()), + sorted(new_nodes) + ); + // Every agent gets an entry, also one that took no action. + assert_eq!( + recording.len(), + state.attackers.len() + state.defenders.len() + ); +} diff --git a/core/malsim-core/tests/incremental_attack_surface.rs b/core/malsim-core/tests/incremental_attack_surface.rs new file mode 100644 index 00000000..1d5ab458 --- /dev/null +++ b/core/malsim-core/tests/incremental_attack_surface.rs @@ -0,0 +1,434 @@ +//! `update_attack_surface` must always give exactly what a full +//! `get_attack_surface` rebuild gives. Two sweeps check that: +//! +//! - `simulated_surfaces_match_full_rebuild`: every test scenario, under +//! many combinations of settings and seeds, played by random attackers +//! and defenders on the plain and dyna simulators. Every attacker's +//! surface is compared with a full rebuild after every step. +//! - `random_updates_match_full_rebuild`: `update_attack_surface` called +//! directly on real graphs with random performed nodes and defenses - +//! also ones no attacker could reach - and random actionability, +//! necessity and impossible steps. + +use std::collections::HashMap; +use std::path::{Path, PathBuf}; +use std::sync::atomic::{AtomicUsize, Ordering}; + +use malsim_core::attack_surface::{get_attack_surface, update_attack_surface}; +use malsim_core::dyna_simulator::DynaSimulator; +use malsim_core::graph_state::{compute_initial_graph_state, TtcMode}; +use malsim_core::graph_utils::sort_by_node_id; +use malsim_core::scenario::Scenario; +use malsim_core::settings::{FlatAgentSettings, FlatDefenderSettings, MalSimulatorSettings}; +use malsim_core::simulator::{SimState, Simulator}; +use malsim_core::types::{NodeDenseMap, NodeSet}; +use maltoolbox_attackgraph::{AttackGraph, AttackGraphNodeId}; +use maltoolbox_language::graph::attack_step::AttackStepType; +use rand::rngs::StdRng; +use rand::seq::{IndexedRandom, SliceRandom}; +use rand::{RngExt, SeedableRng}; + +/// Coverage counters for `simulated_surfaces_match_full_rebuild`, so a +/// pass can't hide a sweep that never hit the interesting cases. +static EPISODES: AtomicUsize = AtomicUsize::new(0); +static RESET_FAILURES: AtomicUsize = AtomicUsize::new(0); +static STEPS: AtomicUsize = AtomicUsize::new(0); +static STEPS_WITH_NEW_DEFENSES: AtomicUsize = AtomicUsize::new(0); +static STEPS_WITH_SURFACE_REMOVALS: AtomicUsize = AtomicUsize::new(0); +static STEPS_WITH_MODEL_EFFECTS: AtomicUsize = AtomicUsize::new(0); + +fn bump(counter: &AtomicUsize) { + counter.fetch_add(1, Ordering::Relaxed); +} + +fn scenario_dir() -> PathBuf { + PathBuf::from(env!("CARGO_MANIFEST_DIR")).join("../../tests/testdata/scenarios") +} + +fn scenario_files(dir: &Path, out: &mut Vec) { + let mut entries: Vec = std::fs::read_dir(dir) + .unwrap() + .map(|e| e.unwrap().path()) + .collect(); + entries.sort(); + for path in entries { + if path.is_dir() { + scenario_files(&path, out); + } else if path.extension().is_some_and(|e| e == "yml" || e == "yaml") { + out.push(path); + } + } +} + +/// Every test scenario that loads offline (some need the network or are +/// deliberately broken), with whether it is a dyna-MAL scenario. +fn scenarios() -> Vec<(PathBuf, bool)> { + let mut files = Vec::new(); + scenario_files(&scenario_dir(), &mut files); + let loaded: Vec<(PathBuf, bool)> = files + .into_iter() + .filter(|path| !path.to_string_lossy().contains("git_url")) + .filter(|path| Scenario::load_from_file(path).is_ok()) + .map(|path| { + let name = path.to_string_lossy(); + let dyna = name.contains("dynamal") || name.contains("dynamic_"); + (path, dyna) + }) + .collect(); + loaded +} + +fn sorted( + graph: &AttackGraph, + ids: impl IntoIterator, +) -> Vec { + let mut ids: Vec = ids.into_iter().collect(); + sort_by_node_id(graph, &mut ids); + ids +} + +/// Asserts every attacker's surface equals a full rebuild. +fn check_surfaces(graph: &AttackGraph, state: &SimState, label: &str, step: u64) { + for (name, runtime) in &state.attackers { + let mut expected = get_attack_surface( + graph, + state.settings.attack_surface.skip_compromised, + state.settings.attack_surface.skip_unnecessary, + runtime.actionable_steps.as_ref(), + &runtime.performed_nodes, + None, + &state.graph_state.impossible_attack_steps, + &state.enabled_defenses, + &state.graph_state.necessity_per_node, + ) + .unwrap(); + if runtime.iteration == 0 && !state.settings.compromise_entrypoints_at_start { + expected.extend(runtime.entry_points.iter().copied()); + } + assert_eq!( + runtime.action_surface, expected, + "{label}: attacker {name} at step {step}" + ); + } +} + +enum Driver { + Static(Simulator), + Dyna(DynaSimulator), +} + +impl Driver { + fn state(&self) -> &SimState { + match self { + Driver::Static(s) => s.state().unwrap(), + Driver::Dyna(s) => s.state().unwrap(), + } + } + + /// Steps; returns whether a model effect ran. + fn step(&mut self, actions: &HashMap>) -> bool { + match self { + Driver::Static(s) => { + s.step(actions).unwrap(); + false + } + Driver::Dyna(s) => !s.step(actions).unwrap().step_modification_record.is_empty(), + } + } +} + +/// Random actions: attackers mostly pick from their surface (one or +/// several nodes), sometimes an arbitrary node or nothing; defenders +/// sometimes enable one or more defenses from their surface. On dyna +/// runs every agent picks at most one node: a model effect of one chosen +/// node can delete another, which the simulator rejects. +fn random_actions( + graph: &AttackGraph, + state: &SimState, + rng: &mut StdRng, + dyna: bool, +) -> HashMap> { + let all_nodes = sorted(graph, graph.nodes.keys()); + let mut actions = HashMap::new(); + for (name, runtime) in &state.attackers { + let surface = sorted(graph, runtime.action_surface.iter().copied()); + let mut chosen = Vec::new(); + let roll: f64 = rng.random(); + if roll < 0.75 { + let count = if !dyna && rng.random::() < 0.2 { + 3 + } else { + 1 + }; + chosen.extend(surface.sample(rng, count).copied()); + } else if roll < 0.9 { + chosen.extend(all_nodes.choose(rng).copied()); + } + actions.insert(name.clone(), chosen); + } + for (name, runtime) in &state.defenders { + let surface = sorted(graph, runtime.action_surface.iter().copied()); + let mut chosen = Vec::new(); + let roll: f64 = rng.random(); + if roll < 0.25 { + chosen.extend(surface.choose(rng).copied()); + } else if roll < 0.3 && !dyna { + chosen.extend(surface.sample(rng, 3).copied()); + } + actions.insert(name.clone(), chosen); + } + actions +} + +fn run_case(path: &Path, dyna: bool, settings: MalSimulatorSettings, seed: u64, steps: u64) { + let label = format!( + "{} dyna={dyna} seed={seed} {settings:?}", + path.file_name().unwrap().to_string_lossy() + ); + let scenario = Scenario::load_from_file(path).unwrap(); + let mut driver = if dyna { + let mut sim = DynaSimulator::new(scenario.attack_graph.clone(), scenario.model.clone()); + sim.restore_model().unwrap(); + Driver::Dyna(sim) + } else { + Driver::Static(Simulator::new(scenario.attack_graph.clone())) + }; + let mut agents = scenario + .flatten_agents(&mut StdRng::seed_from_u64(seed)) + .unwrap(); + // Without a defender no defense is ever enabled mid-episode. + if !agents + .iter() + .any(|(_, a)| matches!(a, FlatAgentSettings::Defender(_))) + { + agents.push(( + "TestDefender".to_string(), + FlatAgentSettings::Defender(FlatDefenderSettings { + actionable_steps: None, + observable_steps: None, + false_positive_rates: None, + false_negative_rates: None, + }), + )); + } + let reset = match &mut driver { + Driver::Static(sim) => sim.reset(&settings, agents, seed).map(|_| ()), + Driver::Dyna(sim) => sim.reset(&settings, agents, seed).map(|_| ()), + }; + if reset.is_err() { + // Some settings combinations are invalid for some scenarios. + bump(&RESET_FAILURES); + return; + } + bump(&EPISODES); + + let graph_rc = scenario.attack_graph.clone(); + check_surfaces(&graph_rc.borrow(), driver.state(), &label, 0); + let mut rng = StdRng::seed_from_u64(seed ^ 0xa77ac); + for step in 1..=steps { + if driver.state().done() { + break; + } + let actions = random_actions(&graph_rc.borrow(), driver.state(), &mut rng, dyna); + let defenses_before = driver.state().enabled_defenses.len(); + let surfaces_before: Vec = driver + .state() + .attackers + .values() + .map(|a| a.action_surface.clone()) + .collect(); + let effects = driver.step(&actions); + bump(&STEPS); + if effects { + bump(&STEPS_WITH_MODEL_EFFECTS); + } + if driver.state().enabled_defenses.len() > defenses_before { + bump(&STEPS_WITH_NEW_DEFENSES); + } + if driver + .state() + .attackers + .values() + .zip(&surfaces_before) + .any(|(a, before)| before.iter().any(|n| !a.action_surface.contains(n))) + { + bump(&STEPS_WITH_SURFACE_REMOVALS); + } + check_surfaces(&graph_rc.borrow(), driver.state(), &label, step); + } +} + +#[test] +fn simulated_surfaces_match_full_rebuild() { + let scenarios = scenarios(); + let mut cases = 0; + for (path, dyna) in &scenarios { + let base = Scenario::load_from_file(path).unwrap().sim_settings; + for bits in 0..32u32 { + let mut settings = base; + settings.attack_surface.skip_compromised = bits & 1 != 0; + settings.attack_surface.skip_unnecessary = bits & 2 != 0; + settings.compromise_entrypoints_at_start = bits & 4 != 0; + settings.ttc_mode = if bits & 8 != 0 { + TtcMode::PerStepSample + } else { + TtcMode::Disabled + }; + settings.run_defense_step_bernoullis = bits & 16 != 0; + settings.run_attack_step_bernoullis = bits & 16 != 0; + for seed in 0..3 { + // Every scenario on the plain simulator; dyna scenarios + // also on the dyna one. + run_case(path, false, settings, seed, 100); + if *dyna { + run_case(path, true, settings, seed, 100); + } + cases += 1; + } + } + } + let get = |c: &AtomicUsize| c.load(Ordering::Relaxed); + eprintln!( + "{} scenarios, {cases} cases: {} episodes ({} invalid resets), {} steps, \ + {} with new defenses, {} with surface removals, {} with model effects", + scenarios.len(), + get(&EPISODES), + get(&RESET_FAILURES), + get(&STEPS), + get(&STEPS_WITH_NEW_DEFENSES), + get(&STEPS_WITH_SURFACE_REMOVALS), + get(&STEPS_WITH_MODEL_EFFECTS), + ); + assert!(get(&STEPS_WITH_NEW_DEFENSES) > 1000); + assert!(get(&STEPS_WITH_SURFACE_REMOVALS) > 1000); + assert!(get(&STEPS_WITH_MODEL_EFFECTS) > 100); +} + +/// Random sets over the graph, built from node-id-sorted lists so the test +/// is reproducible. +fn random_subset(nodes: &[AttackGraphNodeId], p: f64, rng: &mut StdRng) -> NodeSet { + nodes + .iter() + .copied() + .filter(|_| rng.random::() < p) + .collect() +} + +fn random_update_case(path: &Path, seed: u64) { + let label = format!( + "{} seed={seed}", + path.file_name().unwrap().to_string_lossy() + ); + let scenario = Scenario::load_from_file(path).unwrap(); + let graph = scenario.attack_graph.borrow(); + let mut rng = StdRng::seed_from_u64(seed); + let nodes = sorted(&graph, graph.nodes.keys()); + if nodes.is_empty() { + return; + } + let defenses: Vec = nodes + .iter() + .copied() + .filter(|&n| graph.nodes[n].step_type == AttackStepType::Defense) + .collect(); + + let realistic = rng.random::() < 0.5; + let (impossible, necessity): (NodeSet, NodeDenseMap) = if realistic { + let graph_state = + compute_initial_graph_state(&graph, TtcMode::Disabled, true, true, &mut rng).unwrap(); + ( + graph_state.impossible_attack_steps, + graph_state.necessity_per_node, + ) + } else { + ( + random_subset(&nodes, 0.1, &mut rng), + nodes + .iter() + .map(|&n| (n, rng.random::() < 0.8)) + .collect(), + ) + }; + let actionable = (rng.random::() < 0.3).then(|| random_subset(&nodes, 0.7, &mut rng)); + let actionable = actionable.as_ref(); + + for (skip_compromised, skip_unnecessary) in + [(false, false), (true, false), (false, true), (true, true)] + { + let mut performed = random_subset(&nodes, 0.02, &mut rng); + let mut enabled = random_subset(&defenses, 0.1, &mut rng); + let full = |performed: &NodeSet, enabled: &NodeSet| { + get_attack_surface( + &graph, + skip_compromised, + skip_unnecessary, + actionable, + performed, + None, + &impossible, + enabled, + &necessity, + ) + .unwrap() + }; + let mut surface = full(&performed, &enabled); + for round in 0..40 { + // Mostly nodes from the surface, like an attacker would pick, + // plus some arbitrary ones (entry points, effects, defenses, + // already performed nodes, ...). + let surface_nodes = sorted(&graph, surface.iter().copied()); + let count = rng.random_range(0..4); + let mut new_performed: Vec = + surface_nodes.sample(&mut rng, count).copied().collect(); + if rng.random::() < 0.3 { + let count = rng.random_range(1..4); + new_performed.extend(nodes.sample(&mut rng, count)); + } + new_performed.shuffle(&mut rng); + let new_defenses: Vec = if rng.random::() < 0.3 { + let count = rng.random_range(1..3); + defenses.sample(&mut rng, count).copied().collect() + } else { + Vec::new() + }; + performed.extend(new_performed.iter().copied()); + enabled.extend(new_defenses.iter().copied()); + + let before = surface.clone(); + let (added, removed) = update_attack_surface( + &graph, + &mut surface, + skip_compromised, + skip_unnecessary, + actionable, + &performed, + new_performed.iter().copied(), + &impossible, + &enabled, + new_defenses.iter().copied(), + &necessity, + ) + .unwrap(); + let expected = full(&performed, &enabled); + let ctx = format!( + "{label} realistic={realistic} skip_compromised={skip_compromised} \ + skip_unnecessary={skip_unnecessary} round={round}" + ); + assert_eq!(surface, expected, "{ctx}: surface"); + let expected_added: NodeSet = expected.difference(&before).copied().collect(); + let expected_removed: NodeSet = before.difference(&expected).copied().collect(); + assert_eq!(added, expected_added, "{ctx}: added"); + assert_eq!(removed, expected_removed, "{ctx}: removed"); + } + } +} + +#[test] +fn random_updates_match_full_rebuild() { + let scenarios = scenarios(); + for (path, _) in &scenarios { + for seed in 0..25 { + random_update_case(path, seed); + } + } +} diff --git a/core/malsim-core/tests/reproducibility.rs b/core/malsim-core/tests/reproducibility.rs new file mode 100644 index 00000000..7cd13d2f --- /dev/null +++ b/core/malsim-core/tests/reproducibility.rs @@ -0,0 +1,583 @@ +//! Reproducibility (R1): the same seed and inputs must give identical +//! results - RNG draws, step outcomes, logs, effect order. +//! +//! Each case builds the scenario from scratch twice IN THE SAME PROCESS +//! (so std `RandomState`s inside mal-toolbox's graph differ between the +//! two builds), runs it with seeded `RandomAgent` policies and compares a +//! transcript of everything observable per step. Everything is rendered +//! by node *name*, since slotmap keys of two separately generated graphs +//! need not coincide. + +use std::collections::HashMap; +use std::path::PathBuf; + +use malsim_core::agent_state::AgentState; +use malsim_core::dyna_simulator::DynaSimulator; +use malsim_core::event_logger::LogEntry; +use malsim_core::graph_state::TtcMode; +use malsim_core::model_effects::{AssetOp, AssocOp, ModEffectOp}; +use malsim_core::rewards::agent_reward; +use malsim_core::run_simulation::decision_agents; +use malsim_core::scenario::{load_scenario_dict, Scenario}; +use malsim_core::simulator::{SimState, Simulator, StepOutcome}; +use maltoolbox_attackgraph::AttackGraphNodeId; +use rand::rngs::StdRng; +use rand::{RngExt, SeedableRng}; +use serde_json::{json, Value}; + +fn testdata(path: &str) -> PathBuf { + PathBuf::from(env!("CARGO_MANIFEST_DIR")) + .join("../../tests/testdata") + .join(path) +} + +/// The scenario file `name` with its `agents` replaced (if given). +fn build(name: &str, agents: Option) -> Scenario { + let mut dict = load_scenario_dict(testdata("scenarios").join(name)).unwrap(); + if let Some(agents) = agents { + dict.insert("agents".to_string(), agents); + } + Scenario::from_dict(&dict).unwrap() +} + +enum Driver { + Static(Simulator), + Dyna(DynaSimulator), +} + +impl Driver { + fn state(&self) -> &SimState { + match self { + Driver::Static(s) => s.state().unwrap(), + Driver::Dyna(s) => s.state().unwrap(), + } + } +} + +struct Names<'a>(&'a Scenario); + +impl Names<'_> { + fn node(&self, id: AttackGraphNodeId) -> String { + let graph = self.0.attack_graph.borrow(); + if graph.nodes.contains_key(id) { + graph.full_name_of(id, Some(&self.0.model.borrow())) + } else { + "".to_string() + } + } + + fn sorted(&self, ids: impl IntoIterator) -> Vec { + let mut v: Vec = ids.into_iter().map(|id| self.node(id)).collect(); + v.sort(); + v + } + + fn ordered(&self, ids: impl IntoIterator) -> Vec { + ids.into_iter().map(|id| self.node(id)).collect() + } + + fn log(&self, l: &LogEntry) -> String { + let mut context: Vec<(String, String)> = l + .context + .iter() + .map(|(k, &v)| (k.clone(), self.node(v))) + .collect(); + context.sort(); + format!( + "t={} det=({},{}) trig={} ctx={:?} fp={}", + l.timestep, + self.node(l.detector_id.0), + l.detector_id.1, + self.node(l.trigger), + context, + l.false_positive + ) + } + + fn mod_op(&self, op: &ModEffectOp) -> String { + match op { + ModEffectOp::Asset(AssetOp::Added { asset }) => format!("+asset {asset:?}"), + ModEffectOp::Asset(AssetOp::Removed { asset, .. }) => format!("-asset {asset:?}"), + ModEffectOp::Assoc(AssocOp::Added { + left, + field_name, + right, + }) => format!("+assoc {left:?} {field_name} {right:?}"), + ModEffectOp::Assoc(AssocOp::Removed { + left, + field_name, + right, + }) => format!("-assoc {left:?} {field_name} {right:?}"), + } + } +} + +/// Everything observable about the state after a reset or a step. +fn snapshot( + scenario: &Scenario, + state: &SimState, + outcome: &StepOutcome, + step: u64, + out: &mut Vec, +) { + let names = Names(scenario); + out.push(format!( + "enabled_defenses {:?}", + names.sorted(state.enabled_defenses.iter().copied()) + )); + out.push(format!( + "step_enabled_defenses {:?}", + names.sorted(outcome.step_enabled_defenses.iter().copied()) + )); + for (name, a) in &state.attackers { + let o = &outcome.attackers[name]; + out.push(format!("[{name}] iteration {}", a.iteration)); + out.push(format!( + "[{name}] step_performed {:?}", + names.sorted(o.step_performed_nodes.iter().copied()) + )); + out.push(format!( + "[{name}] step_attempted {:?}", + names.sorted(o.step_attempted_nodes.iter().copied()) + )); + out.push(format!( + "[{name}] performed {:?}", + names.sorted(a.performed_nodes.iter().copied()) + )); + out.push(format!( + "[{name}] attempted {:?}", + names.sorted(a.attempted_nodes.iter().copied()) + )); + out.push(format!( + "[{name}] action_surface {:?}", + names.sorted(a.action_surface.iter().copied()) + )); + let mut attempts: Vec<(String, u64)> = a + .num_attempts + .iter() + .map(|(&n, &c)| (names.node(n), c)) + .collect(); + attempts.sort(); + out.push(format!("[{name}] num_attempts {attempts:?}")); + let mut ttcs: Vec<(String, u64)> = a + .ttc_values + .iter() + .map(|(&n, v)| (names.node(n), v.to_bits())) + .collect(); + ttcs.sort(); + out.push(format!("[{name}] ttc_values {ttcs:?}")); + out.push(format!( + "[{name}] impossible {:?}", + names.sorted(a.impossible_steps.iter().copied()) + )); + history(&names, a, name, out); + } + for (name, d) in &state.defenders { + let o = &outcome.defenders[name]; + out.push(format!("[{name}] iteration {}", d.iteration)); + out.push(format!( + "[{name}] step_observed {:?}", + names.sorted(o.step_observed_nodes.iter().copied()) + )); + let logs: Vec = o.step_logs.iter().map(|l| names.log(l)).collect(); + out.push(format!("[{name}] step_logs {logs:#?}")); + let logs: Vec = d.logs.iter().map(|l| names.log(l)).collect(); + out.push(format!("[{name}] logs {logs:#?}")); + out.push(format!( + "[{name}] observed {:?}", + names.sorted(d.observed_nodes.iter().copied()) + )); + out.push(format!( + "[{name}] compromised {:?}", + names.sorted(d.compromised_nodes.iter().copied()) + )); + out.push(format!( + "[{name}] performed {:?}", + names.sorted(d.performed_nodes.iter().copied()) + )); + out.push(format!( + "[{name}] action_surface {:?}", + names.sorted(d.action_surface.iter().copied()) + )); + } + + // Rewards, each with an RNG seeded from the step. + let graph = scenario.attack_graph.borrow(); + let model = scenario.model.borrow(); + for settings in &scenario.agent_settings { + let agent_state = AgentState::new(settings, &graph, &model, state, outcome).unwrap(); + let mut rng = StdRng::seed_from_u64(step); + out.push(format!( + "[{}] reward {:?}", + settings.name(), + agent_reward(&agent_state, &mut rng).map(f64::to_bits) + )); + } +} + +fn history( + names: &Names<'_>, + a: &malsim_core::simulator::AttackerRuntime, + name: &str, + out: &mut Vec, +) { + out.push(format!( + "[{name}] performed_history {:?}", + names.ordered(a.performed_history.iter().copied()) + )); +} + +/// Runs `steps` steps (or until done) of a fresh instance of the scenario. +fn run( + make: &dyn Fn(u64) -> Scenario, + dyna: bool, + multi: bool, + seed: u64, + steps: u64, +) -> Vec { + let scenario = make(seed); + let mut driver = if dyna { + Driver::Dyna(DynaSimulator::new( + scenario.attack_graph.clone(), + scenario.model.clone(), + )) + } else { + Driver::Static(Simulator::new(scenario.attack_graph.clone())) + }; + if let Driver::Dyna(sim) = &mut driver { + sim.restore_model().unwrap(); + } + let agents = scenario + .flatten_agents(&mut StdRng::seed_from_u64(seed)) + .unwrap(); + match &mut driver { + Driver::Static(sim) => sim.reset(&scenario.sim_settings, agents, seed).map(|_| ()), + Driver::Dyna(sim) => sim.reset(&scenario.sim_settings, agents, seed).map(|_| ()), + } + .unwrap(); + let mut policies = decision_agents(&scenario).unwrap(); + let mut rng = StdRng::seed_from_u64(seed ^ 0x5eed); + + let mut out = Vec::new(); + let mut outcome = StepOutcome::from_reset(driver.state()); + out.push("== reset".to_string()); + snapshot(&scenario, driver.state(), &outcome, 0, &mut out); + + for step in 1..=steps { + if driver.state().done() { + break; + } + let mut actions: HashMap> = HashMap::new(); + { + let graph = scenario.attack_graph.borrow(); + let model = scenario.model.borrow(); + for settings in &scenario.agent_settings { + let agent_state = + AgentState::new(settings, &graph, &model, driver.state(), &outcome).unwrap(); + if multi { + // Several actions per agent and step, so that the + // simulator handles multi-element sets. + let chosen: Vec = agent_state + .sorted_action_surface() + .into_iter() + .filter(|_| rng.random_bool(0.5)) + .collect(); + if !chosen.is_empty() { + actions.insert(settings.name().to_string(), chosen); + } + continue; + } + let Some(policy) = policies.get_mut(settings.name()) else { + continue; + }; + if let Some(action) = policy.get_next_action(&agent_state).unwrap() { + actions.insert(settings.name().to_string(), vec![action]); + } + } + } + let names = Names(&scenario); + out.push(format!("== step {step}")); + let mut acts: Vec<(String, Vec)> = actions + .iter() + .map(|(k, v)| (k.clone(), names.ordered(v.iter().copied()))) + .collect(); + acts.sort(); + out.push(format!("actions {acts:?}")); + + let result = match &mut driver { + Driver::Static(sim) => sim.step(&actions).map(|o| (o, Vec::new())), + Driver::Dyna(sim) => sim.step(&actions).map(|o| { + let record: Vec = o + .step_modification_record + .iter() + .map(|op| names.mod_op(op)) + .collect(); + (o.step, record) + }), + }; + match result { + Ok((o, record)) => { + if dyna { + out.push(format!("modification_record {record:#?}")); + } + outcome = o; + } + // Several actions in one step can include a node that an + // earlier action's model effect removed; that is an error the + // simulator reports (deterministically). + Err(e) if multi && dyna => { + out.push(format!("step error: {e}").replace(|c: char| c.is_ascii_digit(), "#")); + break; + } + Err(e) => panic!("step {step}: {e} (actions {acts:?})"), + } + snapshot(&scenario, driver.state(), &outcome, step, &mut out); + if dyna { + let graph = scenario.attack_graph.borrow(); + let model = scenario.model.borrow(); + let mut all: Vec = graph + .nodes + .keys() + .map(|id| graph.full_name_of(id, Some(&model))) + .collect(); + all.sort(); + out.push(format!("graph_nodes {all:?}")); + } + } + out +} + +/// Runs the case twice for each of several seeds and asserts the +/// transcripts are identical. Returns the total number of steps taken. +fn assert_reproducible(label: &str, make: &dyn Fn(u64) -> Scenario, dyna: bool, steps: u64) { + for multi in [false, true] { + for seed in 0..SEEDS { + assert_one(label, make, dyna, multi, seed, steps); + } + } +} + +fn assert_one( + label: &str, + make: &dyn Fn(u64) -> Scenario, + dyna: bool, + multi: bool, + seed: u64, + steps: u64, +) { + { + let first = run(make, dyna, multi, seed, steps); + let second = run(make, dyna, multi, seed, steps); + if std::env::var("REPRO_DUMP").is_ok() { + eprintln!( + "##### {label} multi {multi} seed {seed}\n{}", + first.join("\n") + ); + } + for (i, (a, b)) in first.iter().zip(&second).enumerate() { + assert_eq!( + a, b, + "{label} multi {multi} seed {seed}: transcripts diverge at line {i}" + ); + } + assert_eq!( + first.len(), + second.len(), + "{label} multi {multi} seed {seed}: lengths" + ); + } +} + +const SEEDS: u64 = 6; + +fn random_agent(seed: u64, wait_prob: f64) -> Value { + json!({"policy": "RandomAgent", "config": {"seed": seed, "wait_prob": wait_prob}}) +} + +fn with(mut base: Value, extra: Value) -> Value { + base.as_object_mut() + .unwrap() + .extend(extra.as_object().unwrap().clone()); + base +} + +/// ttc_lang: TTC distributions (incl. attacker overrides), two attackers +/// and a defender with false positive/negative rates. +fn ttc_scenario(mode: TtcMode, seed: u64) -> Scenario { + let agents = json!({ + "AttackerA": with(random_agent(seed, 0.0), json!({ + "type": "attacker", + "entry_points": ["ComputerA:easyAccess", "Net1:easyAccess"], + "ttc_overrides": {"by_asset_type": {"Computer": {"easyConnect": "HardAndUncertain"}}}, + "rewards": {"by_asset_type": {"Computer": {"easyConnect": 3.0}}}, + "reward_mode": "SAMPLE_TTC", + })), + "AttackerB": with(random_agent(seed + 100, 0.2), json!({ + "type": "attacker", + "entry_points": ["Net1:easyAccess"], + "reward_mode": "SAMPLE_TTC", + })), + "Defender": with(random_agent(seed + 200, 0.5), json!({ + "type": "defender", + "false_positive_rates": {"by_asset_type": { + "Computer": {"easyAccess": 0.5, "easyConnect": 0.4}, + "Network": {"easyAccess": 0.3}, + "Software": {"easyScan": 0.5}, + }}, + "false_negative_rates": {"by_asset_type": { + "Computer": {"easyAccess": 0.5, "easyConnect": 0.3}, + "Network": {"easyAccess": 0.4}, + }}, + "rewards": {"by_asset_type": {"Computer": {"easyAccess": 2.0}}}, + })), + }); + let mut scenario = build("ttc_lang_scenario.yml", Some(agents)); + scenario.sim_settings.ttc_mode = mode; + scenario.sim_settings.seed = Some(seed); + scenario +} + +#[test] +fn ttc_per_step_sample_is_reproducible() { + assert_reproducible( + "ttc per-step", + &|seed| ttc_scenario(TtcMode::PerStepSample, seed), + false, + 60, + ); +} + +#[test] +fn ttc_pre_sample_is_reproducible() { + assert_reproducible( + "ttc pre-sample", + &|seed| ttc_scenario(TtcMode::PreSample, seed), + false, + 60, + ); +} + +#[test] +fn ttc_effort_based_is_reproducible() { + assert_reproducible( + "ttc effort", + &|seed| ttc_scenario(TtcMode::EffortBasedPerStepSample, seed), + false, + 60, + ); +} + +/// coreLang with false alerts, several attackers and Bernoullis. +#[test] +fn corelang_false_alerts_are_reproducible() { + let make = |seed: u64| { + let agents = json!({ + "A1": with(random_agent(seed, 0.0), json!({ + "type": "attacker", "entry_points": ["OS App:fullAccess"], + })), + "A2": with(random_agent(seed + 100, 0.1), json!({ + "type": "attacker", "entry_points": ["OS App:fullAccess", "Program 1:fullAccess"], + })), + "D": with(random_agent(seed + 200, 0.6), json!({ + "type": "defender", + "false_positive_rates": {"by_asset_name": { + "OS App": {"fullAccess": 0.5, "attemptRead": 0.5, "read": 0.3}, + "Program 1": {"fullAccess": 0.5, "attemptRead": 0.5}, + }}, + "false_negative_rates": {"by_asset_name": { + "OS App": {"fullAccess": 0.5, "read": 0.5}, + "Program 1": {"fullAccess": 0.5}, + }}, + })), + }); + let dict = json!({ + "lang_file": testdata("langs/org.mal-lang.coreLang-1.0.0.mar").to_string_lossy(), + "model_file": testdata("models/simple_test_model.yml").to_string_lossy(), + "agents": agents, + }); + let mut scenario = Scenario::from_dict(dict.as_object().unwrap()).unwrap(); + scenario.sim_settings.ttc_mode = TtcMode::PerStepSample; + scenario.sim_settings.seed = Some(seed); + scenario + }; + assert_reproducible("corelang", &make, false, 120); +} + +/// Detectors and event logging (with false positives) plus model effects. +#[test] +fn detector_scenario_is_reproducible() { + let make = |seed: u64| { + let agents = json!({ + "Attacker": with(random_agent(seed, 0.0), json!({ + "type": "attacker", "entry_points": ["Computer:0:physicalAccess"], + })), + "Defender": with(random_agent(seed + 200, 0.3), json!({"type": "defender"})), + }); + let mut scenario = build("detector_lang_scenario.yml", Some(agents)); + scenario.sim_settings.seed = Some(seed); + scenario + }; + assert_reproducible("detector (static)", &make, false, 40); + assert_reproducible("detector (dyna)", &make, true, 40); +} + +/// `scenario_file` with every agent replaced by a seeded `RandomAgent` +/// policy. +fn random_policies(scenario_file: &str, seed: u64) -> Scenario { + let mut dict = load_scenario_dict(testdata("scenarios").join(scenario_file)).unwrap(); + if let Some(Value::Object(agents)) = dict.get_mut("agents") { + for (i, agent) in agents.values_mut().enumerate() { + let agent = agent.as_object_mut().unwrap(); + agent.remove("agent_class"); + agent.insert("policy".to_string(), json!("RandomAgent")); + agent.insert( + "config".to_string(), + json!({"seed": seed * 10 + i as u64, "wait_prob": 0.1}), + ); + } + } + let mut scenario = Scenario::from_dict(&dict).unwrap(); + scenario.sim_settings.seed = Some(seed); + scenario +} + +/// Effects of `actions_effects_scenario.yml`. +#[test] +fn actions_effects_is_reproducible() { + let make = |seed: u64| { + let mut scenario = random_policies("actions_effects_scenario.yml", seed); + scenario.sim_settings.ttc_mode = TtcMode::PerStepSample; + scenario + }; + assert_reproducible("actions_effects", &make, true, 60); +} + +/// Model effects that remove many assets/associations at once. +#[test] +fn remove_many_assoc_is_reproducible() { + let make = |seed: u64| random_policies("dynamic_remove_many_assoc_scenario.yml", seed); + assert_reproducible("remove_many_assoc", &make, true, 80); +} + +/// Every dynamal example scenario, whose model effects add and remove +/// assets and associations in all sorts of ways. +#[test] +fn dynamal_examples_are_reproducible() { + let root = testdata("scenarios/dynamal_example_scenarios"); + let mut files: Vec = Vec::new(); + for dir in ["basic", "intermediate", "advanced"] { + for entry in std::fs::read_dir(root.join(dir)).unwrap() { + let path = entry.unwrap().path(); + files.push(format!( + "dynamal_example_scenarios/{dir}/{}", + path.file_name().unwrap().to_string_lossy() + )); + } + } + files.sort(); + assert!(files.len() > 20); + for file in &files { + let make = |seed: u64| random_policies(file, seed); + assert_reproducible(file, &make, true, 40); + } +} diff --git a/core/malsim-core/tests/run_simulation.rs b/core/malsim-core/tests/run_simulation.rs new file mode 100644 index 00000000..eb12f242 --- /dev/null +++ b/core/malsim-core/tests/run_simulation.rs @@ -0,0 +1,734 @@ +//! Rewards, scenario-driven policies and `run_simulation`, end to end on +//! real scenario fixtures through `malsim-core`'s public API. Rust ports +//! of `tests/test_main.py`, `tests/agents/test_path_finders.py`, +//! `tests/agents/test_ttc_avoider.py`, `test_agents.py:: +//! test_random_agent_wait_prob` and `test_mal_simulator.py`'s +//! `test_*_step_rewards_*`. + +use std::collections::{BTreeMap, HashMap}; +use std::path::PathBuf; + +use malsim_core::agent_state::{AgentState, AgentStateError}; +use malsim_core::graph_state::TtcMode; +use malsim_core::policies::{ + get_shortest_path_to, policy_from_name, DecisionAgent, KeyboardAgent, PolicyError, RandomAgent, + ShortestPathAttacker, TTCSoftMinAttacker, +}; +use malsim_core::rewards::{agent_reward, RewardError}; +use malsim_core::run_simulation::{ + decision_agents, run_simulation, run_simulation_with_agents, DecisionAgents, RunSimulationError, +}; +use malsim_core::scenario::{load_scenario_dict, Scenario}; +use malsim_core::simulator::{Simulator, StepOutcome}; +use malsim_core::ttc::TtcDist; +use malsim_core::types::NodeMap; +use maltoolbox_attackgraph::AttackGraphNodeId; +use maltoolbox_language::graph::attack_step::AttackStepType; +use rand::rngs::StdRng; +use rand::SeedableRng; +use serde_json::{json, Map, Value}; + +fn testdata(path: &str) -> PathBuf { + PathBuf::from(env!("CARGO_MANIFEST_DIR")) + .join("../../tests/testdata") + .join(path) +} + +fn load_scenario(name: &str) -> Scenario { + Scenario::load_from_file(testdata("scenarios").join(name)).unwrap() +} + +/// The scenario file `name` with its `agents` replaced. +fn load_scenario_with_agents(name: &str, agents: Value) -> Scenario { + let mut scenario_dict = load_scenario_dict(testdata("scenarios").join(name)).unwrap(); + scenario_dict.insert("agents".to_string(), agents); + Scenario::from_dict(&scenario_dict).unwrap() +} + +/// coreLang + `simple_test_model.yml` (Python's `corelang_lang_graph` and +/// `model` fixtures) with the given agents. +fn corelang_scenario(agents: Value) -> Scenario { + let scenario_dict = json!({ + "lang_file": testdata("langs/org.mal-lang.coreLang-1.0.0.mar").to_string_lossy(), + "model_file": testdata("models/simple_test_model.yml").to_string_lossy(), + "agents": agents, + }); + Scenario::from_dict(scenario_dict.as_object().unwrap()).unwrap() +} + +fn config(value: Value) -> Map { + value.as_object().unwrap().clone() +} + +/// A `MalSimulator.from_scenario` stand-in: a `Simulator` over a +/// scenario, plus the last step outcome agent states are built from. +struct Sim { + scenario: Scenario, + sim: Simulator, + outcome: StepOutcome, + rng: StdRng, +} + +impl Sim { + fn new(scenario: Scenario) -> Self { + let mut sim = Sim { + sim: Simulator::new(scenario.attack_graph.clone()), + scenario, + outcome: StepOutcome { + attackers: BTreeMap::new(), + defenders: BTreeMap::new(), + step_enabled_defenses: Default::default(), + }, + rng: StdRng::seed_from_u64(0), + }; + sim.reset(); + sim + } + + fn reset(&mut self) { + let seed = self.scenario.sim_settings.seed.unwrap_or(0); + let agents = self + .scenario + .flatten_agents(&mut StdRng::seed_from_u64(seed)) + .unwrap(); + let state = self + .sim + .reset(&self.scenario.sim_settings, agents, seed) + .unwrap(); + self.outcome = StepOutcome::from_reset(state); + } + + fn node(&self, full_name: &str) -> AttackGraphNodeId { + self.scenario + .attack_graph + .borrow() + .get_node_by_full_name(full_name) + .unwrap() + } + + fn name(&self, node: AttackGraphNodeId) -> String { + self.scenario.attack_graph.borrow().nodes[node].name.clone() + } + + fn with_state(&self, agent_name: &str, f: impl FnOnce(&AgentState<'_>) -> R) -> R { + let attack_graph = self.scenario.attack_graph.borrow(); + let model = self.scenario.model.borrow(); + let settings = self + .scenario + .agent_settings + .iter() + .find(|s| s.name() == agent_name) + .unwrap(); + let state = AgentState::new( + settings, + &attack_graph, + &model, + self.sim.state().unwrap(), + &self.outcome, + ) + .unwrap(); + f(&state) + } + + fn next_action( + &self, + agent_name: &str, + agent: &mut dyn DecisionAgent, + ) -> Result, PolicyError> { + self.with_state(agent_name, |state| agent.get_next_action(state)) + } + + fn step(&mut self, agent_name: &str, actions: Vec) { + self.outcome = self + .sim + .step(&HashMap::from([(agent_name.to_string(), actions)])) + .unwrap(); + } + + fn reward(&mut self, agent_name: &str) -> Result { + let mut rng = StdRng::seed_from_u64(0); + std::mem::swap(&mut rng, &mut self.rng); + let reward = self.with_state(agent_name, |state| agent_reward(state, &mut rng)); + self.rng = rng; + reward + } + + fn done(&self) -> bool { + self.sim.state().unwrap().done() + } +} + +fn assert_close(actual: f64, expected: f64) { + assert!((actual - expected).abs() < 1e-9, "{actual} != {expected}"); +} + +// --- Rewards --- + +const ATTACKER: &str = "Test Attacker"; + +#[test] +fn attacker_step_rewards_cumulative() { + let mut sim = Sim::new(corelang_scenario(json!({ + ATTACKER: { + "type": "attacker", + "entry_points": ["OS App:fullAccess"], + "reward_mode": "CUMULATIVE", + "rewards": {"by_asset_name": {"OS App": { + "fullAccess": 10.0, + "attemptRead": 100.0, + "accessNetworkAndConnections": 50.4, + }}}, + }, + }))); + // After reset only the entry point is compromised. + assert_close(sim.reward(ATTACKER).unwrap(), 10.0); + + sim.step(ATTACKER, vec![sim.node("OS App:attemptRead")]); + assert_close(sim.reward(ATTACKER).unwrap(), 10.0 + 100.0); + + sim.step( + ATTACKER, + vec![sim.node("OS App:accessNetworkAndConnections")], + ); + assert_close(sim.reward(ATTACKER).unwrap(), 10.0 + 100.0 + 50.4); +} + +#[test] +fn attacker_step_rewards_one_off() { + let mut sim = Sim::new(corelang_scenario(json!({ + ATTACKER: { + "type": "attacker", + "entry_points": ["OS App:fullAccess"], + "reward_mode": "ONE_OFF", + "rewards": {"by_asset_type": {"Application": { + "fullAccess": 10.0, + "attemptRead": 100.0, + "accessNetworkAndConnections": 50.4, + }}}, + }, + }))); + + // TTCs are disabled, so attempts are not penalized. + sim.step(ATTACKER, vec![sim.node("OS App:attemptRead")]); + assert_close(sim.reward(ATTACKER).unwrap(), 100.0); + + sim.step( + ATTACKER, + vec![sim.node("OS App:accessNetworkAndConnections")], + ); + assert_close(sim.reward(ATTACKER).unwrap(), 50.4); + + // A step that compromises nothing is worth nothing. + sim.step(ATTACKER, vec![]); + assert_close(sim.reward(ATTACKER).unwrap(), 0.0); +} + +#[test] +fn attacker_step_rewards_penalize_attempts_when_ttcs_are_enabled() { + let mut scenario = corelang_scenario(json!({ + ATTACKER: { + "type": "attacker", + "entry_points": ["OS App:fullAccess"], + "reward_mode": "ONE_OFF", + "rewards": {"by_asset_name": {"OS App": {"attemptRead": 100.0}}}, + }, + })); + scenario.sim_settings.ttc_mode = TtcMode::PerStepSample; + scenario.sim_settings.seed = Some(1); + let mut sim = Sim::new(scenario); + + let attempt_read = sim.node("OS App:attemptRead"); + sim.step(ATTACKER, vec![attempt_read]); + let compromised = sim.outcome.attackers[ATTACKER] + .step_performed_nodes + .contains(&attempt_read); + // Only a failed attempt counts as attempted (and is penalized). + let expected = if compromised { 100.0 } else { -1.0 }; + assert_close(sim.reward(ATTACKER).unwrap(), expected); +} + +/// Runs an episode stepping on the whole action surface, checking each +/// step's reward against the TTC penalty `penalty` gives a node's +/// `TtcDist`. Returns the summed penalty. +fn check_ttc_penalties(reward_mode: &str, penalty: impl Fn(&TtcDist) -> Option) -> f64 { + // ttc_lang has "hard" steps with a TTC and "easy" ones without. + let agents = json!({ + ATTACKER: { + "type": "attacker", + "entry_points": ["Net1:easyAccess"], + "reward_mode": reward_mode, + "rewards": {"by_asset_type": {"Network": {"hardAccess": 100.0}}}, + }, + }); + let mut sim = Sim::new(load_scenario_with_agents("ttc_lang_scenario.yml", agents)); + + let mut total_penalty = 0.0; + let mut steps = 0; + while !sim.done() { + assert!(steps < 1000, "episode did not terminate"); + steps += 1; + let action_surface = sim.with_state(ATTACKER, |state| state.sorted_action_surface()); + sim.step(ATTACKER, action_surface); + + let (node_rewards, ttc_penalty, exact) = { + let graph = sim.scenario.attack_graph.borrow(); + let model = sim.scenario.model.borrow(); + let mut node_rewards = 0.0; + let mut ttc_penalty = 0.0; + let mut exact = true; + for &n in &sim.outcome.attackers[ATTACKER].step_performed_nodes { + let node = &graph.nodes[n]; + let asset = model.get_asset_by_id(node.model_asset.unwrap()).unwrap(); + if asset.asset_type == "Network" && node.name == "hardAccess" { + node_rewards += 100.0; + } + if let Some(ttc) = &node.ttc { + match penalty(&TtcDist::from_dict(ttc).unwrap()) { + Some(p) => ttc_penalty += p, + None => exact = false, + } + } + } + (node_rewards, ttc_penalty, exact) + }; + let reward = sim.reward(ATTACKER).unwrap(); + if exact { + // Penalized with the TTC value (since ttc mode is disabled) + assert!((reward - (node_rewards - ttc_penalty)).abs() < 1e-6); + } else { + assert!(reward <= node_rewards); + } + total_penalty += node_rewards - reward; + } + total_penalty +} + +#[test] +fn attacker_step_rewards_expected_ttc() { + let total_penalty = check_ttc_penalties("EXPECTED_TTC", |dist| Some(dist.expected_value())); + assert!(total_penalty > 0.0); +} + +#[test] +fn attacker_step_rewards_sample_ttc() { + // Sampled values can't be predicted here, only that they penalize. + let total_penalty = check_ttc_penalties("SAMPLE_TTC", |_| None); + assert!(total_penalty > 0.0); +} + +const DEFENDER: &str = "defender"; + +fn defender_rewards_scenario(reward_mode: &str) -> Scenario { + corelang_scenario(json!({ + DEFENDER: { + "type": "defender", + "reward_mode": reward_mode, + "rewards": {"by_asset_name": {"OS App": { + "notPresent": 100, + "fullAccess": 10.0, + "attemptRead": 105.0, + "accessNetworkAndConnections": 35.04, + }}}, + }, + // To be able to step + ATTACKER: {"type": "attacker", "entry_points": ["OS App:fullAccess"]}, + })) +} + +#[test] +fn defender_step_rewards_cumulative() { + let mut sim = Sim::new(defender_rewards_scenario("CUMULATIVE")); + + sim.step(ATTACKER, vec![sim.node("OS App:attemptRead")]); + assert_close(sim.reward(DEFENDER).unwrap(), -(10.0 + 105.0)); + + sim.step( + ATTACKER, + vec![sim.node("OS App:accessNetworkAndConnections")], + ); + assert_close(sim.reward(DEFENDER).unwrap(), -(10.0 + 105.0 + 35.04)); + + // Enabling a defense costs its reward too. + sim.step(DEFENDER, vec![sim.node("OS App:notPresent")]); + assert_close( + sim.reward(DEFENDER).unwrap(), + -(10.0 + 105.0 + 35.04 + 100.0), + ); +} + +#[test] +fn defender_step_rewards_one_off() { + let mut sim = Sim::new(defender_rewards_scenario("ONE_OFF")); + + sim.step(ATTACKER, vec![sim.node("OS App:attemptRead")]); + assert_close(sim.reward(DEFENDER).unwrap(), -105.0); + + sim.step( + ATTACKER, + vec![sim.node("OS App:accessNetworkAndConnections")], + ); + assert_close(sim.reward(DEFENDER).unwrap(), -35.04); + + sim.step(DEFENDER, vec![sim.node("OS App:notPresent")]); + assert_close(sim.reward(DEFENDER).unwrap(), -100.0); +} + +#[test] +fn defender_step_rewards_reject_ttc_reward_modes() { + let mut sim = Sim::new(defender_rewards_scenario("EXPECTED_TTC")); + assert!(matches!( + sim.reward(DEFENDER), + Err(RewardError::UnsupportedDefenderRewardMode(_)) + )); +} + +// --- Path finding --- + +#[test] +fn path_finding() { + let sim = Sim::new(load_scenario("traininglang_scenario.yml")); + let graph = sim.scenario.attack_graph.borrow(); + let data_2_read = graph.get_node_by_full_name("Data:2:read").unwrap(); + + let performed_nodes: Vec<_> = sim.sim.state().unwrap().attackers["Attacker1"] + .performed_nodes + .iter() + .copied() + .collect(); + let ttc_values = graph.nodes.keys().map(|n| (n, 1.0)).collect(); + let path = get_shortest_path_to(&graph, &performed_nodes, data_2_read, &ttc_values).unwrap(); + + let model = sim.scenario.model.borrow(); + let full_names: Vec<_> = path + .iter() + .map(|&n| graph.full_name_of(n, Some(&model))) + .collect(); + assert_eq!(full_names, ["Host:0:access", "Data:2:read"]); +} + +#[test] +fn path_finding_ttc_lang() { + let mut scenario = load_scenario("ttc_lang_scenario.yml"); + scenario.sim_settings.ttc_mode = TtcMode::ExpectedValue; + scenario.sim_settings.seed = Some(100); + let sim = Sim::new(scenario); + let graph = sim.scenario.attack_graph.borrow(); + + let entry_point = graph.get_node_by_full_name("Net1:easyAccess").unwrap(); + let goal = graph.get_node_by_full_name("DataD:read").unwrap(); + let ttc_values: NodeMap<_> = graph + .nodes + .iter() + .filter(|(_, n)| matches!(n.step_type, AttackStepType::Or | AttackStepType::And)) + .map(|(key, _)| (key, sim.sim.state().unwrap().graph_state.ttc_values[&key])) + .collect(); + assert_close(ttc_values.values().sum(), 2021.0); + + let path = get_shortest_path_to(&graph, &[entry_point], goal, &ttc_values).unwrap(); + + assert!(!path.is_empty()); + assert_eq!(path.last(), Some(&goal)); + for &node in &path { + // Should only have picked the low ttc steps + assert!(graph.nodes[node].name.contains("easy") || node == goal); + } + + // No path from a node the goal isn't reachable from, and nothing left + // to do when the goal is already compromised. + assert!( + get_shortest_path_to(&graph, &[goal], entry_point, &ttc_values) + .unwrap() + .is_empty() + ); + assert!(get_shortest_path_to(&graph, &[goal], goal, &ttc_values) + .unwrap() + .is_empty()); +} + +#[test] +fn shortest_path_attacker_reaches_its_goal_on_easy_steps() { + let mut scenario = load_scenario("ttc_lang_scenario.yml"); + scenario.sim_settings.ttc_mode = TtcMode::ExpectedValue; + scenario.sim_settings.seed = Some(100); + let mut sim = Sim::new(scenario); + let goal = sim.node("DataD:read"); + let mut attacker_agent = ShortestPathAttacker::new(&config(json!({"seed": 0}))).unwrap(); + + let mut picked = Vec::new(); + for _ in 0..10_000 { + if sim.done() { + break; + } + let action = sim.next_action("Attacker", &mut attacker_agent).unwrap(); + let action = action.expect("a path to the goal"); + assert!(sim.name(action).contains("easy") || action == goal); + picked.push(action); + sim.step("Attacker", vec![action]); + } + assert!(sim.done()); + assert_eq!(picked.last(), Some(&goal)); +} + +#[test] +fn shortest_path_attacker_needs_goals_and_ttc_values() { + let goalless = json!({"Attacker": { + "type": "attacker", "entry_points": ["ComputerA:easyAccess"], + }}); + let mut scenario = load_scenario_with_agents("ttc_lang_scenario.yml", goalless); + scenario.sim_settings.ttc_mode = TtcMode::ExpectedValue; + let sim = Sim::new(scenario); + let mut attacker_agent = ShortestPathAttacker::new(&Map::new()).unwrap(); + assert!(matches!( + sim.next_action("Attacker", &mut attacker_agent), + Err(PolicyError::NoGoals) + )); + + // TTC values only exist for PRE_SAMPLE and EXPECTED_VALUE. + let sim = Sim::new(load_scenario("ttc_lang_scenario.yml")); + assert!(matches!( + sim.next_action("Attacker", &mut attacker_agent), + Err(PolicyError::AgentState( + AgentStateError::TtcValuesUnavailable(TtcMode::Disabled) + )) + )); +} + +// --- TTC soft min --- + +const TTC_AVOIDER: &str = "TTCAvoidingAttacker"; + +fn ttc_avoider_sim(seed: u64) -> Sim { + let agents = json!({TTC_AVOIDER: { + "type": "attacker", + "entry_points": ["Net1:easyAccess"], + "goals": ["DataD:read"], + }}); + let mut scenario = load_scenario_with_agents("ttc_lang_scenario.yml", agents); + scenario.sim_settings.seed = Some(seed); + scenario.sim_settings.ttc_mode = TtcMode::PreSample; + scenario.sim_settings.attack_surface.skip_unnecessary = false; + Sim::new(scenario) +} + +#[test] +fn ttc_avoider() { + // `TTCSoftMinAttacker` samples from a softmax, not an argmin, so a + // "hard" step whose pre-sampled TTC came out low has a real chance of + // being drawn. As in the Python test, the simulator and policy seeds + // pin a run where that doesn't happen (with these TTCs, 18 of the + // policy seeds 0..20 pick only easy steps). + let mut sim = ttc_avoider_sim(2); + let goal = sim.node("DataD:read"); + let mut attacker_agent = TTCSoftMinAttacker::new(&config(json!({"seed": 0}))).unwrap(); + + while !sim.done() { + // Run the simulation until agents are terminated/truncated + let attacker_node = sim.next_action(TTC_AVOIDER, &mut attacker_agent).unwrap(); + let attacker_node = attacker_node.expect("an action"); + + // Should always pick the easy path or the goal + assert!(sim.name(attacker_node).contains("easy") || attacker_node == goal); + sim.step(TTC_AVOIDER, vec![attacker_node]); + } +} + +#[test] +fn ttc_avoider_low_sharpness() { + // TTC Avoider with low beta/sharpness explores both easy and hard paths. + let num_trials = 20; + let mut easy_count = 0; + let mut hard_count = 0; + + for trial in 0..num_trials { + let mut sim = ttc_avoider_sim(trial); + let mut attacker_agent = + TTCSoftMinAttacker::new(&config(json!({"beta": 0.1, "seed": trial}))).unwrap(); + + let mut trial_picked_easy = false; + let mut trial_picked_hard = false; + while !sim.done() { + let attacker_node = sim.next_action(TTC_AVOIDER, &mut attacker_agent).unwrap(); + let attacker_node = attacker_node.expect("an action"); + + trial_picked_easy |= sim.name(attacker_node).contains("easy"); + trial_picked_hard |= sim.name(attacker_node).contains("hard"); + sim.step(TTC_AVOIDER, vec![attacker_node]); + } + easy_count += u64::from(trial_picked_easy); + hard_count += u64::from(trial_picked_hard); + } + + let min_trials = num_trials * 8 / 10; + assert!(easy_count >= min_trials, "{easy_count}/{num_trials}"); + assert!(hard_count >= min_trials, "{hard_count}/{num_trials}"); +} + +// --- Random agent from a scenario --- + +#[test] +fn random_agent_wait_prob() { + let mut sim = Sim::new(load_scenario("simple_random_defender.yml")); + let defender_name = "Defender"; + let defender_config = sim + .scenario + .defender_settings() + .next() + .unwrap() + .config + .clone(); + let wait_prob = defender_config["wait_prob"].as_f64().unwrap(); + let mut defender_ai = RandomAgent::new(&defender_config).unwrap(); + + let mut num_actions = 0; + let mut wait_count = 0; + for _ in 0..100 { + sim.reset(); + while !sim.with_state(defender_name, |s| s.action_surface().is_empty()) { + let action = sim.next_action(defender_name, &mut defender_ai).unwrap(); + num_actions += 1; + wait_count += u32::from(action.is_none()); + sim.step(defender_name, action.into_iter().collect()); + } + } + + let wait_freq = f64::from(wait_count) / f64::from(num_actions); + assert!((wait_freq - wait_prob).abs() < 0.01, "{wait_freq}"); +} + +// --- run_simulation --- + +/// Every recorded action must be a node of the scenario's graph. +fn assert_actions_are_graph_nodes( + scenario: &Scenario, + agent_actions: &BTreeMap>, +) { + let graph = scenario.attack_graph.borrow(); + for actions in agent_actions.values() { + assert!(actions.iter().all(|&n| graph.nodes.contains_key(n))); + } +} + +#[test] +fn run_simulation_with_defender_agent() { + // Make sure we can run simulation with defender agent registered in + // scenario + let scenario = load_scenario("bfs_vs_bfs_scenario.yml"); + let mut sim = Simulator::new(scenario.attack_graph.clone()); + let agent_actions = run_simulation(&mut sim, &scenario, None).unwrap(); + + assert!(sim.state().unwrap().done()); + assert!(!agent_actions["attacker1"].is_empty()); + assert!(!agent_actions["defender1"].is_empty()); + assert_actions_are_graph_nodes(&scenario, &agent_actions); + + // Every attacker action was compromised (or at least attempted), and + // every defender action enabled a defense. + let state = sim.state().unwrap(); + let attacker = &state.attackers["attacker1"]; + for action in &agent_actions["attacker1"] { + assert!( + attacker.performed_nodes.contains(action) || attacker.attempted_nodes.contains(action) + ); + } + for action in &agent_actions["defender1"] { + assert!(state.enabled_defenses.contains(action)); + } +} + +#[test] +fn run_simulation_without_defender_agent() { + // Make sure we can run simulation without defender agent registered in + // scenario + let scenario = load_scenario("no_defender_agent_scenario.yml"); + let mut sim = Simulator::new(scenario.attack_graph.clone()); + let agent_actions = run_simulation(&mut sim, &scenario, None).unwrap(); + + assert!(sim.state().unwrap().done()); + assert_eq!( + agent_actions.keys().collect::>(), + ["attacker1"], + "only the attacker acts" + ); + assert_actions_are_graph_nodes(&scenario, &agent_actions); +} + +#[test] +fn run_simulation_is_reproducible_for_a_seed() { + let run = || { + let mut scenario = load_scenario("bfs_vs_bfs_scenario.yml"); + scenario.sim_settings.seed = Some(5); + scenario.sim_settings.ttc_mode = TtcMode::PerStepSample; + let mut sim = Simulator::new(scenario.attack_graph.clone()); + let agent_actions = run_simulation(&mut sim, &scenario, None).unwrap(); + let graph = scenario.attack_graph.borrow(); + agent_actions + .into_iter() + .map(|(name, actions)| { + let ids: Vec = actions.iter().map(|&n| graph.nodes[n].id).collect(); + (name, ids) + }) + .collect::>() + }; + assert_eq!(run(), run()); +} + +#[test] +fn run_simulation_stops_at_max_steps() { + let scenario = load_scenario("bfs_vs_bfs_scenario.yml"); + let mut sim = Simulator::new(scenario.attack_graph.clone()); + let agent_actions = run_simulation(&mut sim, &scenario, Some(3)).unwrap(); + + assert!(!sim.state().unwrap().done()); + assert_eq!(sim.state().unwrap().attackers["attacker1"].iteration, 3); + assert_eq!(agent_actions["attacker1"].len(), 3); +} + +#[test] +fn run_simulation_agents_without_policy_wait() { + // `agent_class: null` for the attacker: nothing ever happens, so the + // run only ends at `max_steps`. + let scenario = load_scenario("simple_ai_scenario_with_heuristic_defender.yml"); + assert!(!decision_agents(&scenario) + .unwrap() + .contains_key("Attacker1")); + let mut sim = Simulator::new(scenario.attack_graph.clone()); + let agent_actions = run_simulation(&mut sim, &scenario, Some(5)).unwrap(); + assert!(!agent_actions.contains_key("Attacker1")); +} + +#[test] +fn run_simulation_with_keyboard_agent() { + // Python patches `input()` to return an empty line; an agent fed empty + // lines always waits. + let scenario = load_scenario("no_defender_agent_scenario.yml"); + let mut sim = Simulator::new(scenario.attack_graph.clone()); + let input: &[u8] = b"\n\n\n"; + let mut agents = DecisionAgents::new(); + agents.insert( + "attacker1".to_string(), + Box::new(KeyboardAgent::with_io(input, std::io::sink())), + ); + let agent_actions = + run_simulation_with_agents(&mut sim, &scenario, &mut agents, Some(3)).unwrap(); + assert!(agent_actions.is_empty()); +} + +#[test] +fn run_simulation_rejects_unknown_policies_and_foreign_simulators() { + // The loader carries unknown policy names; instantiating them fails. + let scenario = load_scenario("wrong_agent_classes_scenario.yml"); + let mut sim = Simulator::new(scenario.attack_graph.clone()); + assert!(matches!( + run_simulation(&mut sim, &scenario, None), + Err(RunSimulationError::Policy(PolicyError::UnknownPolicy(_))) + )); + assert!(policy_from_name("BreadthFirstAttacker", &Map::new()).is_ok()); + + let scenario = load_scenario("bfs_vs_bfs_scenario.yml"); + let other = load_scenario("bfs_vs_bfs_scenario.yml"); + let mut sim = Simulator::new(other.attack_graph.clone()); + assert!(matches!( + run_simulation(&mut sim, &scenario, None), + Err(RunSimulationError::GraphMismatch) + )); +} diff --git a/core/malsim-core/tests/scenario_parity.rs b/core/malsim-core/tests/scenario_parity.rs new file mode 100644 index 00000000..f2dda9a9 --- /dev/null +++ b/core/malsim-core/tests/scenario_parity.rs @@ -0,0 +1,316 @@ +//! Phase C6 (`PORTING_NOTES.md` §7, §11): the Rust scenario loader must +//! resolve every fixture in `tests/testdata/scenarios` to the same shape +//! the Python `Scenario` does. +//! +//! The Python shape is committed as `tests/testdata/scenario_parity.json` +//! (generated by `tests/scenario_parity.py`, kept current by +//! `tests/test_scenario_parity.py`). This test builds the same shape from +//! the Rust `Scenario` and compares, so Rust and Python are checked +//! against one shared oracle without either calling the other. + +use malsim_core::types::NodeMap; +use std::collections::BTreeMap; +use std::path::{Path, PathBuf}; + +use malsim_core::scenario::{ + flatten_attacker_settings, flatten_defender_settings, AttackerSettings, DefenderSettings, + EntryPoints, NodePropertyRule, RuleValue, Scenario, +}; +use maltoolbox_attackgraph::{AttackGraph, AttackGraphNodeId}; +use maltoolbox_model::Model; +use serde_json::{json, Map, Value}; + +/// Fixtures where the two loaders are *expected* to disagree, with the +/// Python error type recorded in the golden file. Each is logged in +/// `PORTING_NOTES.md` §12. +const EXPECTED_DIVERGENCES: [(&str, &str); 1] = [ + // Unknown policy names: Python raises `LookupError`, Rust carries the + // name opaquely (§2.6, §12 C3). + ("wrong_agent_classes_scenario.yml", "LookupError"), +]; + +fn repo_root() -> PathBuf { + PathBuf::from(env!("CARGO_MANIFEST_DIR")) + .join("../..") + .canonicalize() + .unwrap() +} + +/// `os.path.relpath(os.path.realpath(path), REPO_ROOT)`. +fn repo_relative(path: &Path) -> Value { + let real = path.canonicalize().unwrap(); + json!(real.strip_prefix(repo_root()).unwrap().to_string_lossy()) +} + +struct Ctx<'a> { + graph: &'a AttackGraph, + model: &'a Model, +} + +impl Ctx<'_> { + fn name(&self, id: AttackGraphNodeId) -> String { + self.graph.full_name_of(id, Some(self.model)) + } + + fn sorted_names<'b>(&self, ids: impl IntoIterator) -> Value { + let mut names: Vec = ids.into_iter().map(|&id| self.name(id)).collect(); + names.sort(); + json!(names) + } + + fn rule_per_node(&self, rule: Option<&NodePropertyRule>) -> Value { + match rule { + None => Value::Null, + Some(rule) => self.by_name( + rule.per_node(self.graph, self.model) + .into_iter() + .map(|(id, v)| (id, v.to_rule_value())), + ), + } + } + + fn by_name(&self, entries: impl IntoIterator) -> Value { + let sorted: BTreeMap = entries + .into_iter() + .map(|(id, v)| (self.name(id), v)) + .collect(); + Value::Object(sorted.into_iter().collect()) + } + + fn optional_names<'b>( + &self, + ids: Option>, + ) -> Value { + ids.map_or(Value::Null, |ids| self.sorted_names(ids)) + } + + fn rate_map(&self, rates: Option<&NodeMap>) -> Value { + match rates { + None => Value::Null, + Some(rates) => json!({ + "len": rates.len(), + "nonzero": self.by_name( + rates.iter().filter(|(_, r)| **r != 0.0).map(|(&id, &r)| (id, json!(r))) + ), + }), + } + } + + fn attacker( + &self, + settings: &AttackerSettings, + agent: &mut Map, + ) { + let (entry_points, single_set) = match &settings.entry_points { + EntryPoints::Single(eps) => (self.sorted_names(eps), eps.clone()), + EntryPoints::Multiple(sets) => ( + Value::Array(sets.iter().map(|eps| self.sorted_names(eps)).collect()), + Default::default(), + ), + }; + agent.insert("entry_points".into(), entry_points); + agent.insert("goals".into(), self.sorted_names(&settings.goals)); + agent.insert( + "rewards".into(), + self.rule_per_node(settings.rewards.as_ref()), + ); + agent.insert( + "actionable_steps".into(), + self.rule_per_node(settings.actionable_steps.as_ref()), + ); + agent.insert( + "ttc_dists".into(), + self.rule_per_node(settings.ttc_dists.as_ref()), + ); + + let flat = flatten_attacker_settings(settings, self.graph, self.model, &single_set); + agent.insert( + "flat".into(), + json!({ + "actionable_steps": self.optional_names(flat.actionable_steps.as_ref()), + "ttc_dists": flat.ttc_dists.map_or(Value::Null, |dists| { + self.by_name(dists.into_iter().map(|(id, d)| (id, d.to_dict()))) + }), + }), + ); + } + + fn defender(&self, settings: &DefenderSettings, agent: &mut Map) { + agent.insert( + "rewards".into(), + self.rule_per_node(settings.rewards.as_ref()), + ); + agent.insert( + "actionable_steps".into(), + self.rule_per_node(settings.actionable_steps.as_ref()), + ); + agent.insert( + "observable_steps".into(), + self.rule_per_node(settings.observable_steps.as_ref()), + ); + agent.insert( + "false_positive_rates".into(), + self.rule_per_node(settings.false_positive_rates.as_ref()), + ); + agent.insert( + "false_negative_rates".into(), + self.rule_per_node(settings.false_negative_rates.as_ref()), + ); + + let flat = flatten_defender_settings(settings, self.graph, self.model); + agent.insert( + "flat".into(), + json!({ + "actionable_steps": self.optional_names(flat.actionable_steps.as_ref()), + "observable_steps": self.optional_names(flat.observable_steps.as_ref()), + "false_positive_rates": self.rate_map(flat.false_positive_rates.as_ref()), + "false_negative_rates": self.rate_map(flat.false_negative_rates.as_ref()), + }), + ); + } +} + +/// Rust counterpart of `tests/scenario_parity.py::scenario_shape`. +fn scenario_shape(scenario: &Scenario) -> Value { + let graph = scenario.attack_graph.borrow(); + let model = scenario.model.borrow(); + let ctx = Ctx { + graph: &graph, + model: &model, + }; + + let mut agents = Vec::new(); + for settings in &scenario.agent_settings { + let mut agent = Map::new(); + agent.insert("name".into(), json!(settings.name())); + agent.insert("type".into(), json!(settings.agent_type().value())); + match settings { + malsim_core::scenario::AgentSettings::Attacker(a) => { + agent.insert("policy".into(), json!(a.policy)); + agent.insert("reward_mode".into(), json!(a.reward_mode.name())); + ctx.attacker(a, &mut agent); + } + malsim_core::scenario::AgentSettings::Defender(d) => { + agent.insert("policy".into(), json!(d.policy)); + agent.insert("reward_mode".into(), json!(d.reward_mode.name())); + ctx.defender(d, &mut agent); + } + } + agents.push(Value::Object(agent)); + } + + let s = &scenario.sim_settings; + json!({ + "lang_file": repo_relative(Path::new(&scenario.lang_file)), + "model_file": scenario.model_file.as_deref().map_or(Value::Null, repo_relative), + "num_nodes": graph.nodes.len(), + "sim_settings": { + "ttc_mode": s.ttc_mode.name(), + "run_defense_step_bernoullis": s.run_defense_step_bernoullis, + "run_attack_step_bernoullis": s.run_attack_step_bernoullis, + "skip_compromised": s.attack_surface.skip_compromised, + "skip_unnecessary": s.attack_surface.skip_unnecessary, + "compromise_entrypoints_at_start": s.compromise_entrypoints_at_start, + "seed": s.seed, + "uncompromise_untraversable_steps": s.uncompromise_untraversable_steps, + }, + "agents": agents, + }) +} + +/// JSON numbers compare by value: Python writes a YAML `2` as `2`, Rust's +/// `f64` rules as `2.0`. +fn normalize(value: &Value) -> Value { + match value { + Value::Number(n) => json!(n.as_f64().unwrap()), + Value::Array(items) => Value::Array(items.iter().map(normalize).collect()), + Value::Object(map) => { + Value::Object(map.iter().map(|(k, v)| (k.clone(), normalize(v))).collect()) + } + other => other.clone(), + } +} + +/// The JSON path of the first difference, for a readable failure. +fn first_difference(path: &str, expected: &Value, actual: &Value) -> Option { + match (expected, actual) { + (Value::Object(e), Value::Object(a)) => { + for key in e.keys().chain(a.keys().filter(|k| !e.contains_key(*k))) { + let (ev, av) = ( + e.get(key).unwrap_or(&Value::Null), + a.get(key).unwrap_or(&Value::Null), + ); + if !e.contains_key(key) || !a.contains_key(key) { + return Some(format!( + "{path}.{key}: python={ev} rust={av} (key missing on one side)" + )); + } + if let Some(diff) = first_difference(&format!("{path}.{key}"), ev, av) { + return Some(diff); + } + } + None + } + (Value::Array(e), Value::Array(a)) if e.len() == a.len() => e + .iter() + .zip(a) + .enumerate() + .find_map(|(i, (ev, av))| first_difference(&format!("{path}[{i}]"), ev, av)), + _ if expected == actual => None, + _ => Some(format!("{path}: python={expected} rust={actual}")), + } +} + +#[test] +fn rust_scenario_loader_matches_python_golden_shapes() { + let root = repo_root(); + let golden: Map = serde_json::from_str( + &std::fs::read_to_string(root.join("tests/testdata/scenario_parity.json")).unwrap(), + ) + .unwrap(); + assert!(golden.len() > 30, "golden file looks truncated"); + + let scenarios_dir = root.join("tests/testdata/scenarios"); + let mut failures = Vec::new(); + for (fixture, expected) in &golden { + let loaded = Scenario::load_from_file(scenarios_dir.join(fixture)); + let divergence = EXPECTED_DIVERGENCES.iter().find(|(f, _)| f == fixture); + + match (expected.get("error"), loaded) { + (Some(error), result) => match divergence { + Some((_, python_error)) => { + assert_eq!(error, python_error, "{fixture}: divergence entry is stale"); + if let Err(e) = result { + failures.push(format!( + "{fixture}: expected Rust to load it (known divergence), got {e}" + )); + } + } + None => { + if result.is_ok() { + failures.push(format!("{fixture}: Python raises {error}, Rust loaded it")); + } + } + }, + (None, Err(e)) => { + failures.push(format!("{fixture}: Python loads it, Rust failed: {e}")) + } + (None, Ok(scenario)) => { + assert!( + divergence.is_none(), + "{fixture}: listed as divergence but Python loads it" + ); + let actual = normalize(&scenario_shape(&scenario)); + if let Some(diff) = first_difference(fixture, &normalize(expected), &actual) { + failures.push(diff); + } + } + } + } + assert!( + failures.is_empty(), + "{} fixture(s) differ:\n{}", + failures.len(), + failures.join("\n") + ); +} diff --git a/core/malsim-core/tests/scenario_simulator.rs b/core/malsim-core/tests/scenario_simulator.rs new file mode 100644 index 00000000..75fd8e60 --- /dev/null +++ b/core/malsim-core/tests/scenario_simulator.rs @@ -0,0 +1,407 @@ +//! Phase C5 smoke test (`PORTING_NOTES.md` §7): loads real scenario +//! fixtures with the Rust-only scenario loader, builds a `Simulator` from +//! them and steps it with hand-picked actions - using only +//! `malsim-core`'s public API, with no Python or PyO3 anywhere in the +//! dependency graph. This is the embedding-Rust-program story end to end. + +use std::collections::HashMap; +use std::path::PathBuf; + +use malsim_core::dyna_simulator::DynaSimulator; +use malsim_core::graph_utils::sort_by_node_id; +use malsim_core::scenario::Scenario; +use malsim_core::simulator::Simulator; +use malsim_core::types::NodeSet; +use maltoolbox_attackgraph::AttackGraphNodeId; +use rand::rngs::StdRng; +use rand::SeedableRng; + +fn scenario_path(name: &str) -> PathBuf { + PathBuf::from(env!("CARGO_MANIFEST_DIR")) + .join("../../tests/testdata/scenarios") + .join(name) +} + +fn full_name(scenario: &Scenario, id: AttackGraphNodeId) -> String { + scenario + .attack_graph + .borrow() + .full_name_of(id, Some(&scenario.model.borrow())) +} + +fn node(scenario: &Scenario, full_name: &str) -> AttackGraphNodeId { + scenario + .attack_graph + .borrow() + .get_node_by_full_name(full_name) + .unwrap() +} + +/// A deterministic stand-in for a policy: the action-surface node with the +/// smallest full name. +fn first_by_name( + scenario: &Scenario, + surface: impl IntoIterator, +) -> Option { + surface + .into_iter() + .min_by_key(|&id| full_name(scenario, id)) +} + +#[test] +fn load_scenario_reset_and_step() { + let scenario = Scenario::load_from_file(scenario_path("traininglang_scenario.yml")).unwrap(); + let mut sim = Simulator::new(scenario.attack_graph.clone()); + let agents = scenario + .flatten_agents(&mut StdRng::seed_from_u64(1)) + .unwrap(); + + let state = sim.reset(&scenario.sim_settings, agents, 1).unwrap(); + + // Entry points are compromised at start (default settings) and the + // attacker has somewhere to go next. + let attacker = &state.attackers["Attacker1"]; + assert!(attacker + .performed_nodes + .contains(&node(&scenario, "User:3:phishing"))); + assert!(attacker + .performed_nodes + .contains(&node(&scenario, "Host:0:connect"))); + assert!(!attacker.action_surface.is_empty()); + assert_eq!(state.attacker_is_terminated("Attacker1"), Some(false)); + assert!(state.defenders.contains_key("Defender1")); + + // Attacker steps on a node from its surface; the defender does nothing. + let target = first_by_name(&scenario, attacker.action_surface.iter().copied()).unwrap(); + let actions = HashMap::from([("Attacker1".to_string(), vec![target])]); + let outcome = sim.step(&actions).unwrap(); + + // TTCs are disabled by default, so the attempt always succeeds. + assert!(outcome.attackers["Attacker1"] + .step_performed_nodes + .contains(&target)); + let state = sim.state().unwrap(); + assert!(state.attackers["Attacker1"] + .performed_nodes + .contains(&target)); + assert_eq!(state.attackers["Attacker1"].iteration, 1); + assert_eq!(state.defenders["Defender1"].iteration, 1); + assert!(state.defenders["Defender1"] + .compromised_nodes + .contains(&target)); +} + +#[test] +fn defender_action_enables_defense() { + let scenario = Scenario::load_from_file(scenario_path("traininglang_scenario.yml")).unwrap(); + let mut sim = Simulator::new(scenario.attack_graph.clone()); + let agents = scenario + .flatten_agents(&mut StdRng::seed_from_u64(2)) + .unwrap(); + let state = sim.reset(&scenario.sim_settings, agents, 2).unwrap(); + + let defense = first_by_name( + &scenario, + state.defenders["Defender1"].action_surface.iter().copied(), + ) + .expect("defender has a defense to enable"); + let actions = HashMap::from([("Defender1".to_string(), vec![defense])]); + let outcome = sim.step(&actions).unwrap(); + + assert!(outcome.step_enabled_defenses.contains(&defense)); + let state = sim.state().unwrap(); + assert!(state.enabled_defenses.contains(&defense)); + assert!(state.defenders["Defender1"] + .performed_nodes + .contains(&defense)); + assert!(!state.defenders["Defender1"] + .action_surface + .contains(&defense)); +} + +#[test] +fn run_until_attacker_terminates() { + // A full episode: the attacker always takes its first surface node by + // name until it runs out of moves, with a step bound as a safety net. + let scenario = Scenario::load_from_file(scenario_path("traininglang_scenario.yml")).unwrap(); + let mut sim = Simulator::new(scenario.attack_graph.clone()); + let agents = scenario + .flatten_agents(&mut StdRng::seed_from_u64(3)) + .unwrap(); + sim.reset(&scenario.sim_settings, agents, 3).unwrap(); + + let max_steps = scenario.attack_graph.borrow().nodes.len(); + let mut steps = 0; + while sim.state().unwrap().attacker_is_terminated("Attacker1") == Some(false) { + assert!(steps < max_steps, "attacker did not terminate"); + let surface = sim.state().unwrap().attackers["Attacker1"] + .action_surface + .clone(); + let target = first_by_name(&scenario, surface).unwrap(); + sim.step(&HashMap::from([("Attacker1".to_string(), vec![target])])) + .unwrap(); + steps += 1; + } + assert!(steps > 0); + assert!(sim.state().unwrap().defender_is_terminated()); +} + +/// The `Wiper-:activate` node a fired wiperLang model effect adds. +fn wiper_activate(scenario: &Scenario) -> Option { + let graph = scenario.attack_graph.borrow(); + let model = scenario.model.borrow(); + graph.nodes.keys().find(|&id| { + let name = graph.full_name_of(id, Some(&model)); + name.starts_with("Wiper-") && name.ends_with(":activate") + }) +} + +#[test] +fn dyna_scenario_reset_and_step() { + // The DynaMalSimulator flavor, mirroring `test_dyna_mal_simulator.py:: + // test_inherited_query_methods_follow_graph_mutated_by_model_effects` + // stepping on `InfectedDevice:infect` fires wiperLang's model effect, + // which adds a Wiper asset (and its attack steps) at runtime through + // the shared graph/model handles. + let scenario = Scenario::load_from_file(scenario_path("wiper_scenario.yml")).unwrap(); + let nodes_before = scenario.attack_graph.borrow().nodes.len(); + let infect = node(&scenario, "InfectedDevice:infect"); + let mut sim = DynaSimulator::new(scenario.attack_graph.clone(), scenario.model.clone()); + + for seed in 0..2 { + // Restore before flattening, as `Scenario::flatten_agents` documents. + sim.restore_model().unwrap(); + let agents = scenario + .flatten_agents(&mut StdRng::seed_from_u64(seed)) + .unwrap(); + sim.reset(&scenario.sim_settings, agents, seed).unwrap(); + // Every reset restores the pristine model/graph. + assert_eq!(scenario.attack_graph.borrow().nodes.len(), nodes_before); + assert_eq!(wiper_activate(&scenario), None); + + let attacker = "WiperController".to_string(); + let outcome = sim + .step(&HashMap::from([(attacker.clone(), vec![infect])])) + .unwrap(); + assert!(!outcome.step_modification_record.is_empty()); + assert!(scenario.attack_graph.borrow().nodes.len() > nodes_before); + + // The new node is on the attacker's surface and can be compromised. + // Asset ids keep counting across resets (Wiper-7, Wiper-8, ...), the + // same as on the Python side, so look the new node up by pattern. + let activate = wiper_activate(&scenario).expect("the effect added a Wiper asset"); + assert!(sim.state().unwrap().attackers[&attacker] + .action_surface + .contains(&activate)); + let outcome = sim + .step(&HashMap::from([(attacker.clone(), vec![activate])])) + .unwrap(); + assert!(outcome.step.attackers[&attacker] + .step_performed_nodes + .contains(&activate)); + assert!(sim.state().unwrap().attackers[&attacker] + .performed_nodes + .contains(&activate)); + } +} + +/// A `dynamic_remove_add.mal` scenario whose pristine model has `Object:1` +/// associated to `Start:0`: stepping `Start:0:remove` deletes `Object:1`, +/// and a restore brings it back with regenerated nodes. Mirrors +/// `test_dyna_mal_simulator.py::_restorable_object_scenario`. +fn restorable_object_scenario(agents: serde_json::Value) -> Scenario { + let lang_file = PathBuf::from(env!("CARGO_MANIFEST_DIR")) + .join("../../tests/testdata/langs/dynamic_remove_add.mal"); + let scenario_dict = serde_json::json!({ + "lang_file": lang_file.to_string_lossy(), + "model": { + "metadata": { + "name": "restore_model", + "langVersion": "1.0.0", + "langID": "org.mal-lang.dynamicRemoveAdd", + "malVersion": "0.1.0-SNAPSHOT", + "MAL-Toolbox Version": "2.10.0", + "info": "", + }, + "assets": { + "0": {"name": "Start:0", "type": "Start", + "associated_assets": {"objects": {"1": "Object:1"}}}, + "1": {"name": "Object:1", "type": "Object", + "associated_assets": {"start": {"0": "Start:0"}}}, + }, + }, + "agents": agents, + }); + Scenario::from_dict(scenario_dict.as_object().unwrap()).unwrap() +} + +#[test] +fn dyna_reset_restored_nodes_keep_rule_settings() { + // Rust twin of `test_dyna_mal_simulator.py:: + // test_reset_restored_nodes_keep_rule_settings`: `Start:0:remove` + // deletes `Object:1`, the next reset restores it with regenerated + // nodes, and the defender's observability rule must cover them again. + let scenario = restorable_object_scenario(serde_json::json!({ + "Attacker": {"type": "attacker", "entry_points": ["Start:0:access"]}, + "Defender": {"type": "defender", + "observable_steps": {"by_asset_type": {"Object": ["addStart"]}}}, + })); + let mut sim = DynaSimulator::new(scenario.attack_graph.clone(), scenario.model.clone()); + let mut rng = StdRng::seed_from_u64(0); + let attacker = "Attacker".to_string(); + + let mut reset = |sim: &mut DynaSimulator| { + sim.restore_model().unwrap(); + let agents = scenario.flatten_agents(&mut rng).unwrap(); + sim.reset(&scenario.sim_settings, agents, 0).unwrap(); + }; + let step = |sim: &mut DynaSimulator, full_name: &str| { + let target = node(&scenario, full_name); + sim.step(&HashMap::from([(attacker.clone(), vec![target])])) + .unwrap(); + target + }; + let defender_observes_add_start = |sim: &mut DynaSimulator| { + step(sim, "Start:0:add"); + let add_start = step(sim, "Object:1:addStart"); + let state = sim.state().unwrap(); + assert!(state.attackers[&attacker] + .performed_nodes + .contains(&add_start)); + state.defenders["Defender"] + .observed_nodes + .contains(&add_start) + }; + + reset(&mut sim); + assert!(defender_observes_add_start(&mut sim)); + + reset(&mut sim); + step(&mut sim, "Start:0:remove"); + assert!(scenario + .model + .borrow() + .get_asset_by_name("Object:1") + .is_none()); + + reset(&mut sim); + assert!(defender_observes_add_start(&mut sim)); +} + +#[test] +fn dyna_reset_re_resolves_entry_points_and_goals_on_restored_nodes() { + // Rust twin of `test_dyna_mal_simulator.py:: + // test_reset_re_resolves_entry_points_and_goals_on_restored_nodes`: + // an entry point and a goal on `Object:1` must point at the regenerated + // nodes after the asset is removed and restored, for both entry-point + // shapes. + for entry_points in [ + serde_json::json!(["Start:0:access", "Object:1:addStartAssoc"]), + serde_json::json!([["Start:0:access", "Object:1:addStartAssoc"]]), + ] { + let scenario = restorable_object_scenario(serde_json::json!({ + "Attacker": {"type": "attacker", "entry_points": entry_points, + "goals": ["Object:1:addStart"]}, + })); + let mut sim = DynaSimulator::new(scenario.attack_graph.clone(), scenario.model.clone()); + let mut rng = StdRng::seed_from_u64(0); + let attacker = "Attacker".to_string(); + let mut reset = |sim: &mut DynaSimulator| { + sim.restore_model().unwrap(); + let agents = scenario.flatten_agents(&mut rng).unwrap(); + sim.reset(&scenario.sim_settings, agents, 0).unwrap(); + }; + let step = |sim: &mut DynaSimulator, full_name: &str| { + let target = node(&scenario, full_name); + sim.step(&HashMap::from([(attacker.clone(), vec![target])])) + .unwrap(); + target + }; + + reset(&mut sim); + step(&mut sim, "Start:0:remove"); + assert!(scenario + .model + .borrow() + .get_asset_by_name("Object:1") + .is_none()); + + reset(&mut sim); + let entry_point = node(&scenario, "Object:1:addStartAssoc"); + let goal = node(&scenario, "Object:1:addStart"); + let runtime = &sim.state().unwrap().attackers[&attacker]; + assert!(runtime.entry_points.contains(&entry_point)); + assert!(runtime.performed_nodes.contains(&entry_point)); + assert_eq!(runtime.goals, [goal].into_iter().collect::()); + + // The episode runs to the restored goal. + step(&mut sim, "Start:0:add"); + step(&mut sim, "Object:1:addStart"); + assert_eq!( + sim.state().unwrap().attacker_is_terminated(&attacker), + Some(true) + ); + } +} + +#[test] +fn performed_history_follows_action_order() { + // ttc_lang has plenty of nodes on the attacker's surface. + let scenario = Scenario::load_from_file(scenario_path("ttc_lang_scenario.yml")).unwrap(); + let mut sim = Simulator::new(scenario.attack_graph.clone()); + let agents = scenario + .flatten_agents(&mut StdRng::seed_from_u64(1)) + .unwrap(); + let state = sim.reset(&scenario.sim_settings, agents, 1).unwrap(); + let by_id = |ids: &NodeSet| { + let mut ids: Vec<_> = ids.iter().copied().collect(); + sort_by_node_id(&scenario.attack_graph.borrow(), &mut ids); + ids + }; + + // After reset the history holds exactly the pre-compromised nodes, each + // once (entry points in node-id order, each followed by its new effects). + let attacker = &state.attackers["Attacker"]; + let reset_history = attacker.performed_history.clone(); + assert_eq!( + reset_history.iter().copied().collect::(), + attacker.performed_nodes + ); + assert_eq!(reset_history.len(), attacker.performed_nodes.len()); + assert_eq!(reset_history[0], by_id(&attacker.entry_points)[0]); + + // Step 1: two surface nodes, in reverse node-id order. + let surface = by_id(&attacker.action_surface); + assert!(surface.len() >= 2, "need two actions to compare order"); + let actions = vec![surface[1], surface[0]]; + sim.step(&HashMap::from([("Attacker".to_string(), actions.clone())])) + .unwrap(); + let history = sim.state().unwrap().attackers["Attacker"] + .performed_history + .clone(); + // The reset history is a prefix and the actions follow in order. + assert_eq!(&history[..reset_history.len()], &reset_history[..]); + let pos = |n: AttackGraphNodeId| history.iter().position(|&h| h == n).unwrap(); + assert_eq!(history[reset_history.len()], actions[0]); + assert!(pos(actions[0]) < pos(actions[1])); + + // Step 2: the history keeps growing, never repeating a node. + let next = by_id(&sim.state().unwrap().attackers["Attacker"].action_surface)[0]; + sim.step(&HashMap::from([("Attacker".to_string(), vec![next])])) + .unwrap(); + let runtime = &sim.state().unwrap().attackers["Attacker"]; + assert_eq!(&runtime.performed_history[..history.len()], &history[..]); + assert_eq!(runtime.performed_history[history.len()], next); + assert_eq!( + runtime.performed_history.len(), + runtime.performed_nodes.len() + ); + assert_eq!( + runtime + .performed_history + .iter() + .copied() + .collect::(), + runtime.performed_nodes + ); +} diff --git a/malsim/dyna_mal_simulator/attacker_step.py b/malsim/dyna_mal_simulator/attacker_step.py deleted file mode 100644 index 3d76c457..00000000 --- a/malsim/dyna_mal_simulator/attacker_step.py +++ /dev/null @@ -1,138 +0,0 @@ -from __future__ import annotations -from typing import TYPE_CHECKING -import logging - -import numpy as np - -from maltoolbox.attackgraph import AttackGraphNode - -from malsim.config.sim_settings import TTCMode -from malsim.dyna_mal_simulator.model_effects import execute_model_effects -from malsim.dyna_mal_simulator.simulator_state import DynaMalSimulatorState -from malsim.mal_simulator.graph_utils import ( - node_is_traversable, -) -from malsim.mal_simulator.attacker_step import ( - attacker_step_effects, -) -from malsim.mal_simulator.state_query import node_ttc_value -from malsim.mal_simulator.ttc_utils import TTCDist - -if TYPE_CHECKING: - from malsim.mal_simulator.attacker_state import AttackerState - -logger = logging.getLogger(__name__) - - -def dyna_attacker_step( - sim_state: DynaMalSimulatorState, - agent: AttackerState, - nodes: list[AttackGraphNode], - rng: np.random.Generator, -) -> tuple[list[AttackGraphNode], list[AttackGraphNode], DynaMalSimulatorState]: - """Compromise attack step nodes with attacker - - Args: - agent - the agent to compromise nodes with - nodes - the nodes to compromise - - Returns: two lists with compromised, attempted nodes - """ - - logger.debug('Stepping with agent %s', agent.name) - successful_compromises: list[AttackGraphNode] = [] - attempted_compromises: list[AttackGraphNode] = [] - - for node in nodes: - assert node == sim_state.attack_graph.nodes[node.id], ( - f'{agent.name} tried to enable a node that is not part ' - 'of this simulators attack_graph. Make sure the node ' - 'comes from the agents action surface.' - ) - - if node in agent.settings.entry_points: - # Entrypoints can always be compromised - # TODO: should this actually be the case? - can_compromise = True - else: - # Otherwise attacker is limited by attack surface and traversability - can_compromise = node in agent.action_surface and node_is_traversable( - sim_state, agent.performed_nodes, node - ) - - if can_compromise: - if dyna_attempt_attacker_step( - sim_state, rng, sim_state.settings.ttc_mode, agent, node - ): - successful_compromises.append(node) - logger.info( - 'Attacker agent "%s" compromised "%s"', agent.name, node.full_name - ) - # Run effects as a compromise of performing `node` - sim_state = execute_model_effects(sim_state, node, rng) - step_effects = attacker_step_effects(sim_state, agent, node) - for effect_node in step_effects: - sim_state = execute_model_effects(sim_state, effect_node, rng) - successful_compromises += step_effects - else: - logger.info( - 'Attacker agent "%s" attempted "%s" (attempt %d).', - agent.name, - node.full_name, - agent.num_attempts[node], - ) - attempted_compromises.append(node) - - else: - logger.warning('Attacker could not compromise %s', node.full_name) - - return successful_compromises, attempted_compromises, sim_state - - -def dyna_attempt_attacker_step( - sim_state: DynaMalSimulatorState, - rng: np.random.Generator, - ttc_mode: TTCMode, - agent: AttackerState, - node: AttackGraphNode, -) -> bool: - """Attempt a step with a TTC distribution. - - Return True if the attempt was successful. - """ - - # # New nodes may be created during the simulation, - # # so we need to check if the node is in the num_attempts dict - # if node not in agent.num_attempts: - # num_attempts = 0 - # else: - # num_attempts = agent.num_attempts[node] + 1 - num_attempts = agent.num_attempts.get(node, 0) + 1 - - if agent.settings.ttc_dists and node in agent.settings.ttc_dists: - # If this agent has custom ttc distribution set for this node, use it - ttc_dist = agent.settings.ttc_dists[node] - else: - ttc_dist = TTCDist.from_node(node) - - if ttc_mode == TTCMode.DISABLED: - # Always suceed if disabled TTCs - return True - - elif ttc_mode == TTCMode.EFFORT_BASED_PER_STEP_SAMPLE: - # Run trial to decide success if config says so (SANDOR mode) - return ttc_dist.attempt_ttc_with_effort(num_attempts, rng) - - elif ttc_mode == TTCMode.PER_STEP_SAMPLE: - # Sample ttc value every time if config says so (ANDREI mode) - _node_ttc_value = ttc_dist.sample_value(rng) - return _node_ttc_value <= 1 - - # Compare attempts to ttc expected value in EXPECTED_VALUE mode - # or presampled ttcs in PRE_SAMPLE mode - elif ttc_mode in (TTCMode.EXPECTED_VALUE, TTCMode.PRE_SAMPLE): - _node_ttc_value = node_ttc_value(agent, node) - return num_attempts + 1 >= _node_ttc_value - - else: - raise ValueError(f'Invalid TTC mode: {ttc_mode}') diff --git a/malsim/dyna_mal_simulator/defender_step.py b/malsim/dyna_mal_simulator/defender_step.py deleted file mode 100644 index 7ac7d7e4..00000000 --- a/malsim/dyna_mal_simulator/defender_step.py +++ /dev/null @@ -1,61 +0,0 @@ -from __future__ import annotations -from typing import TYPE_CHECKING -from maltoolbox.attackgraph import AttackGraphNode -import logging - -import numpy as np - - -from malsim.dyna_mal_simulator.model_effects import execute_model_effects -from malsim.dyna_mal_simulator.simulator_state import DynaMalSimulatorState - -if TYPE_CHECKING: - from malsim.mal_simulator.defender_state import DefenderState - -logger = logging.getLogger(__name__) - - -def dyna_defender_step( - sim_state: DynaMalSimulatorState, - agent: DefenderState, - nodes: list[AttackGraphNode], - rng: np.random.Generator, -) -> tuple[list[AttackGraphNode], DynaMalSimulatorState]: - """Enable defense step nodes with defender. - - Args: - agent - the agent to activate defense nodes with - nodes - the defense step nodes to enable - - Returns a tuple of a list and a set, `enabled_defenses` - """ - - logger.debug('Stepping with %s', agent.name) - enabled_defenses: list[AttackGraphNode] = [] - - for node in nodes: - assert node == sim_state.attack_graph.nodes[node.id], ( - f'{agent.name} tried to enable a node that is not part ' - 'of this simulators attack_graph. Make sure the node ' - 'comes from the agents action surface.' - ) - - if node not in agent.action_surface: - logger.warning( - 'Defender agent "%s" tried to step through "%s"(%d), ' - 'which is not part of its defense surface. Defender ' - 'step will skip!', - agent.name, - node.full_name, - node.id, - ) - else: - enabled_defenses.append(node) - sim_state = execute_model_effects(sim_state, node, rng) - logger.info( - 'Defender agent "%s" enabled "%s"', - agent.name, - node.full_name, - ) - - return enabled_defenses, sim_state diff --git a/malsim/dyna_mal_simulator/graph_state.py b/malsim/dyna_mal_simulator/graph_state.py deleted file mode 100644 index 34c66460..00000000 --- a/malsim/dyna_mal_simulator/graph_state.py +++ /dev/null @@ -1,58 +0,0 @@ -"""Dataclass and function to store and create graph state used in the simulator""" - -from __future__ import annotations -from collections.abc import Set - -from numpy.random import Generator -from maltoolbox.attackgraph import AttackGraph, AttackGraphNode -from malsim.mal_simulator.ttc_utils import ( - attack_step_ttc_values, - get_pre_enabled_defenses, - get_impossible_attack_steps, -) -from malsim.mal_simulator.graph_processing import calculate_necessity - -from malsim.mal_simulator.graph_state import GraphState - -from malsim.config.sim_settings import MalSimulatorSettings - - -def add_new_nodes_to_graph_state( - graph_state: GraphState, - sim_settings: MalSimulatorSettings, - attack_graph: AttackGraph, - new_nodes: set[AttackGraphNode], - rng: Generator, -) -> tuple[GraphState, Set[AttackGraphNode]]: - """Update graph state based on new nodes added to the attack graph.""" - - new_attack_steps = {node for node in new_nodes if node.type in ('and', 'or')} - new_defense_steps = [node for node in new_nodes if node.type == 'defense'] - - # TTC (Time to compromise) for each attack step - # will only be set if TTCMode PRE_SAMLE/EXPECTED_VALUE is used - new_attack_step_ttc_values = attack_step_ttc_values( - new_attack_steps, rng=rng, ttc_mode=sim_settings.ttc_mode - ) - # These steps will be enabled from the start of the simulation - # depending on if bernoullis are sampled or not - new_steps_enabled_defenses = get_pre_enabled_defenses( - new_defense_steps, sim_settings.run_defense_step_bernoullis, rng - ) - enabled_defenses = graph_state.pre_enabled_defenses | new_steps_enabled_defenses - if sim_settings.run_attack_step_bernoullis: - # These steps will not be traversable - new_impossible_attack_steps = get_impossible_attack_steps(new_attack_steps, rng) - else: - new_impossible_attack_steps = set() - - necessity_per_node = calculate_necessity(attack_graph, enabled_defenses) - - return GraphState( - ttc_values={**graph_state.ttc_values, **new_attack_step_ttc_values}, - pre_enabled_defenses=graph_state.pre_enabled_defenses - | new_steps_enabled_defenses, - impossible_attack_steps=graph_state.impossible_attack_steps - | new_impossible_attack_steps, - necessity_per_node=necessity_per_node, - ), new_steps_enabled_defenses diff --git a/malsim/dyna_mal_simulator/model_effects.py b/malsim/dyna_mal_simulator/model_effects.py deleted file mode 100644 index 788acaba..00000000 --- a/malsim/dyna_mal_simulator/model_effects.py +++ /dev/null @@ -1,346 +0,0 @@ -"""Functions relating to modifications of the instance model based on the -model effects of attack steps. -""" - -import logging -from collections.abc import Callable - -import numpy as np -from maltoolbox.attackgraph import AttackGraphNode -from maltoolbox.model import ModelAsset -from maltoolbox.model import Model -from malsim.dyna_mal_simulator.graph_state import add_new_nodes_to_graph_state -from malsim.dyna_mal_simulator.process_assoc_traversal import ( - _apply_quantity_filter, - parse_addition, - parse_removal, - sample_size, - traverse_association_chain, -) -from malsim.dyna_mal_simulator.simulator_state import ( - DynaMalSimulatorState, - AssetOp, - AssocOp, -) -from maltoolbox.language.language_graph_model_effect import ( - LanguageGraphModelEffect, - ModelEffectType, - DynTarget, -) - -logger = logging.getLogger(__name__) - - -def target_op( - node: AttackGraphNode, model_effect: LanguageGraphModelEffect, dyn_target: DynTarget -) -> Callable[ - [set[ModelAsset], DynTarget, Model, np.random.Generator], list[AssetOp | AssocOp] -]: - - def add_asset( - base: set[ModelAsset], target: DynTarget, model: Model, rng: np.random.Generator - ) -> list[AssetOp | AssocOp]: - assert node.model_asset, 'AttackGraph needs to have the model object available.' - modification_record: list[AssetOp | AssocOp] = [] - addition_info, quantity = parse_addition( - node=node, - instigating_assets=base, - assoc_traversals=target.assoc_traversal, - rng=rng, - ) - size = sample_size(quantity, rng) - for left_asset, field_name, right_asset in addition_info: - for _ in range(size): - if isinstance(right_asset, ModelAsset): - if not (right_asset == node.model_asset): - raise ValueError( - 'Cannot add an existing asset' - f' ({right_asset.name}) to the model.' - ' It already exists in the model!' - ) - else: - raise ValueError( - f'Cannot add `self` ({right_asset.name}) to the model.' - ' It already exists in the model!' - ) - else: - new_asset = model.add_asset( - asset_type=right_asset.name, - name=f'{right_asset.name}-{model.next_id}', - ) - added_asset_op = AssetOp( - type=ModelEffectType.ADDITIVE, asset=new_asset - ) - try: - left_asset.add_associated_assets(field_name, {new_asset}) - added_assoc_op = AssocOp( - type=ModelEffectType.ADDITIVE, - assoc=(left_asset, field_name, new_asset), - ) - modification_record.extend([added_asset_op, added_assoc_op]) - except ValueError as exception: - logger.error( - f'Failed to add a {field_name} asset to ' - f'{left_asset.name}: {exception}\n' - f'Skipping addition of {field_name}.' - ) - model.remove_asset(new_asset) - continue - return modification_record - - def remove_asset( - base: set[ModelAsset], target: DynTarget, model: Model, rng: np.random.Generator - ) -> list[AssetOp | AssocOp]: - modification_record: list[AssetOp | AssocOp] = [] - removal_info, quantity = parse_removal( - node=node, - instigating_assets=base, - assoc_traversals=target.assoc_traversal, - rng=rng, - ) - if quantity is not None: - # raise ValueError("Quantity filtering not supported for asset removal.") - removal_info = _apply_quantity_filter(removal_info, quantity, rng) - removal_assets: set[ModelAsset] = set() - for _left_asset, _field_name, right_asset in removal_info: - # Snapshot both the dict and its sets: remove_associated_assets() - # mutates the live associated_assets sets in place, and we still - # need the pre-removal members to build the modification record. - associations_snapshot = { - field_name: set(assoc_assets) - for field_name, assoc_assets in right_asset.associated_assets.items() - } - for field_name, assoc_assets in associations_snapshot.items(): - try: - right_asset.remove_associated_assets(field_name, assoc_assets) - modification_record.extend( - [ - AssocOp( - type=ModelEffectType.SUBTRACTIVE, - assoc=(right_asset, field_name, assoc_asset), - ) - for assoc_asset in assoc_assets - ] - ) - except ValueError as exception: - logger.error( - f'Failed to remove associations for {right_asset.name} through' - f' {field_name}: {exception}\n' - f'Skipping removal of {field_name}.' - ) - continue - removal_assets.add(right_asset) - - for removal_asset in removal_assets: - model.remove_asset(removal_asset) - modification_record.append( - AssetOp(type=ModelEffectType.SUBTRACTIVE, asset=removal_asset) - ) - return modification_record - - def add_assoc( - base: set[ModelAsset], target: DynTarget, model: Model, rng: np.random.Generator - ) -> list[AssetOp | AssocOp]: - assert node.model_asset, 'AttackGraph needs to have the model object available.' - modification_record: list[AssetOp | AssocOp] = [] - addition_info, quantity = parse_addition( - node=node, - instigating_assets={node.model_asset}, - assoc_traversals=target.assoc_traversal, - rng=rng, - ) - if quantity is not None: - addition_info = _apply_quantity_filter(addition_info, quantity, rng) - for left_asset, field_name, right_asset in addition_info: - if isinstance(right_asset, ModelAsset): - # Check if the association already exists - if right_asset in left_asset.associated_assets.get(field_name, ()): - # Skip recording the addition otherwise modification_record - # will get out of sync with the model state and reset() will fail - continue - left_asset.add_associated_assets(field_name, {right_asset}) - modification_record.append( - AssocOp( - type=ModelEffectType.ADDITIVE, - assoc=(left_asset, field_name, right_asset), - ) - ) - else: - for assoc_candidate in base: - if assoc_candidate in left_asset.associated_assets.get( - field_name, () - ): - # Already associated, see comment above. - continue - try: - left_asset.add_associated_assets(field_name, {assoc_candidate}) - modification_record.append( - AssocOp( - type=ModelEffectType.ADDITIVE, - assoc=( - left_asset, - field_name, - assoc_candidate, - ), - ) - ) - except ValueError as exception: - logger.error( - f'Failed to associate the {assoc_candidate.name} asset to ' - f'the {left_asset.name} asset: {exception} Skipping ' - 'addition of association.' - ) - continue - return modification_record - - def remove_assoc( - base: set[ModelAsset], target: DynTarget, model: Model, rng: np.random.Generator - ) -> list[AssetOp | AssocOp]: - modification_record: list[AssetOp | AssocOp] = [] - removal_info, quantity = parse_removal( - node=node, - instigating_assets=base, - assoc_traversals=target.assoc_traversal, - rng=rng, - ) - if quantity is not None: - # raise ValueError("Quantity filtering not supported for assoc removal.") - removal_info = _apply_quantity_filter(removal_info, quantity, rng) - for left_asset, field_name, right_asset in removal_info: - if field_name in left_asset.associated_assets: - left_asset.remove_associated_assets(field_name, {right_asset}) - modification_record.append( - AssocOp( - type=ModelEffectType.SUBTRACTIVE, - assoc=( - left_asset, - field_name, - right_asset, - ), - ) - ) - else: - logger.error( - f'Asset {left_asset.name} is not associated to any ' - f"'{field_name}'. Skipping removal of " - f'association to {field_name}.' - ) - continue - return modification_record - - if ( - model_effect.model_effect_type == ModelEffectType.ADDITIVE - and not dyn_target.assoc_op - ): - return add_asset - elif ( - model_effect.model_effect_type == ModelEffectType.SUBTRACTIVE - and not dyn_target.assoc_op - ): - return remove_asset - elif ( - model_effect.model_effect_type == ModelEffectType.ADDITIVE - and dyn_target.assoc_op - ): - return add_assoc - elif ( - model_effect.model_effect_type == ModelEffectType.SUBTRACTIVE - and dyn_target.assoc_op - ): - return remove_assoc - else: - raise ValueError( - f'Invalid combination of model effect type ' - f'{model_effect.model_effect_type} and target association operation ' - f'{dyn_target.assoc_op}.' - ) - - -def _apply_model_effect( - node: AttackGraphNode, - model_effect: LanguageGraphModelEffect, - model: Model, - rng: np.random.Generator, -) -> list[AssetOp | AssocOp]: - """Applies a modification to the instance model based on the provided model - effect. - """ - assert node.model_asset, 'AttackGraph needs to have the model object available.' - modification_record = [] - base_set = traverse_association_chain({node.model_asset}, model_effect.base, rng) - for dyn_target in model_effect.targets: - if len(dyn_target.assoc_traversal) == 0: - raise ValueError( - f'Target association traversal cannot be empty for step ' - f'{node.full_name}.' - ) - - target_op_fn = target_op(node, model_effect, dyn_target) - op_result = target_op_fn(base_set, dyn_target, model, rng) - modification_record.extend(op_result) - return modification_record - - -def execute_model_effects( - sim_state: DynaMalSimulatorState, action: AttackGraphNode, rng: np.random.Generator -) -> DynaMalSimulatorState: - """Execute the model effects of an attack step and return the effects that - were applied. - - Args: - sim_state: The current simulator state. - agent: The attacker agent performing the action. - action: The attack step node being executed. - - Returns: - A list of the effects that were applied as a result of the action. - """ - assert sim_state.attack_graph.model, ( - 'AttackGraph needs to have the model object available.' - ) - modification_record = [] - for model_effect in action.additive_model_effects or []: - modification_record.extend( - _apply_model_effect(action, model_effect, sim_state.attack_graph.model, rng) - ) - for model_effect in action.subtractive_model_effects or []: - modification_record.extend( - _apply_model_effect(action, model_effect, sim_state.attack_graph.model, rng) - ) - logger.debug('Partially regenerating attack graph after applying model effects.') - new_nodes = sim_state.attack_graph.partially_regenerate_graph( - new_assets={ - op.asset - for op in modification_record - if isinstance(op, AssetOp) and op.type == ModelEffectType.ADDITIVE - }, - removed_assets={ - op.asset - for op in modification_record - if isinstance(op, AssetOp) and op.type == ModelEffectType.SUBTRACTIVE - }, - new_associations={ - op.assoc - for op in modification_record - if isinstance(op, AssocOp) and op.type == ModelEffectType.ADDITIVE - }, - removed_associations={ - op.assoc - for op in modification_record - if isinstance(op, AssocOp) and op.type == ModelEffectType.SUBTRACTIVE - }, - ) - new_graph_state, new_enabled_defenses = add_new_nodes_to_graph_state( - sim_state.graph_state, - sim_state.settings, - sim_state.attack_graph, - new_nodes, - rng, - ) - return DynaMalSimulatorState( - attack_graph=sim_state.attack_graph, - settings=sim_state.settings, - graph_state=new_graph_state, - enabled_defenses=sim_state.enabled_defenses | new_enabled_defenses, - modification_record=sim_state.modification_record + modification_record, - ) diff --git a/malsim/dyna_mal_simulator/model_state.py b/malsim/dyna_mal_simulator/model_state.py deleted file mode 100644 index a9bf184d..00000000 --- a/malsim/dyna_mal_simulator/model_state.py +++ /dev/null @@ -1,90 +0,0 @@ -"""Restoring the instance model (and the attack graph derived from it) to a -previous state. -""" - -from typing import Any - -from maltoolbox.attackgraph import AttackGraph -from maltoolbox.model import Model, ModelAsset - - -def reconcile_model_to_snapshot( - model: Model, snapshot: dict[str, Any] -) -> tuple[ - set[ModelAsset], - set[ModelAsset], - set[tuple[ModelAsset, str, ModelAsset]], - set[tuple[ModelAsset, str, ModelAsset]], -]: - """Mutate `model` in place to match `snapshot` exactly and return the - (new_assets, removed_assets, new_associations, removed_associations) - that were applied, suitable as-is for - AttackGraph.partially_regenerate_graph(). - """ - new_assets: set[ModelAsset] = set() - removed_assets: set[ModelAsset] = set() - - current_ids = set(model.assets.keys()) - target_ids = set(snapshot['assets'].keys()) - - for asset_id in current_ids - target_ids: - asset = model.assets[asset_id] - removed_assets.add(asset) - model.remove_asset(asset) - - for asset_id in target_ids - current_ids: - asset_dict = snapshot['assets'][asset_id] - new_asset = model.add_asset( - asset_type=asset_dict['type'], - name=asset_dict['name'], - asset_id=asset_id, - ) - new_assets.add(new_asset) - - new_associations: set[tuple[ModelAsset, str, ModelAsset]] = set() - removed_associations: set[tuple[ModelAsset, str, ModelAsset]] = set() - - for asset_id, asset_dict in snapshot['assets'].items(): - asset = model.assets[asset_id] - current_associations = { - field_name: frozenset(other.id for other in others) - for field_name, others in asset.associated_assets.items() - } - target_associations = { - field_name: frozenset(other_ids) - for field_name, other_ids in asset_dict['associated_assets'].items() - } - for field_name in set(current_associations) | set(target_associations): - current_ids_for_field = current_associations.get(field_name, frozenset()) - target_ids_for_field = target_associations.get(field_name, frozenset()) - - ids_to_remove = current_ids_for_field - target_ids_for_field - if ids_to_remove: - to_remove = {model.assets[i] for i in ids_to_remove} - asset.remove_associated_assets(field_name, to_remove) - removed_associations.update( - (asset, field_name, other) for other in to_remove - ) - - ids_to_add = target_ids_for_field - current_ids_for_field - if ids_to_add: - to_add = {model.assets[i] for i in ids_to_add} - asset.add_associated_assets(field_name, to_add) - new_associations.update((asset, field_name, other) for other in to_add) - - return new_assets, removed_assets, new_associations, removed_associations - - -def reset_model_effects( - attack_graph: AttackGraph, model_snapshot: dict[str, Any] -) -> None: - assert attack_graph.model, 'AttackGraph needs to have the model object available.' - new_assets, removed_assets, new_associations, removed_associations = ( - reconcile_model_to_snapshot(attack_graph.model, model_snapshot) - ) - attack_graph.partially_regenerate_graph( - new_assets=new_assets, - removed_assets=removed_assets, - new_associations=new_associations, - removed_associations=removed_associations, - ) diff --git a/malsim/dyna_mal_simulator/process_assoc_traversal.py b/malsim/dyna_mal_simulator/process_assoc_traversal.py deleted file mode 100644 index bf1b43f5..00000000 --- a/malsim/dyna_mal_simulator/process_assoc_traversal.py +++ /dev/null @@ -1,318 +0,0 @@ -"""Functions for processing association traversal of model effects -in the DynaMalSimulator. -""" - -import logging -from collections.abc import Callable -from typing import TypeVar - -from maltoolbox.language.language_graph_model_effect import ( - AssocTraversal, - GlobAssocTraversal, - AssocSet, -) -from maltoolbox.language import LanguageGraphAsset -from maltoolbox.model import ModelAsset -import numpy as np -from maltoolbox.attackgraph import AttackGraphNode -from maltoolbox.language.language_graph_model_effect import AssocTraversalChain -from maltoolbox.language.language_graph_model_effect import ( - SetOperation, -) - -logger = logging.getLogger(__name__) - -AssetTypeOrInstance = TypeVar('AssetTypeOrInstance', bound=object) - -Quantity = int | tuple[int, int] | None - -T = TypeVar('T', bound=object) - - -def sample_size( - quantity: Quantity, rng: np.random.Generator, max_size: int | float = float('inf') -) -> int: - if quantity is None: - size = 1 - elif isinstance(quantity, int): - size = int(min(quantity, max_size)) - else: - high = int(min(quantity[1], max_size)) - low = int(min(quantity[0], high)) - size = int(rng.integers(low, high, endpoint=True)) - return size - - -def _apply_quantity_filter( - objects: set[T], - quantity: Quantity, - rng: np.random.Generator, -) -> set[T]: - size = sample_size(quantity, rng, len(objects)) - sorted_objects = list(objects) - indicies = np.arange(len(sorted_objects)) - chosen_indicies: list[int] = rng.choice(indicies, size=size, replace=False).tolist() - return {sorted_objects[i] for i in chosen_indicies} - - -def _assoc_traversal( - instigating_assets: set[ModelAsset], - assoc_traversal: AssocTraversal, - rng: np.random.Generator, -) -> set[ModelAsset]: - if assoc_traversal.field_name == 'self': - return instigating_assets - next_assets = set() - for asset in instigating_assets: - if assoc_traversal.field_name not in asset.associated_assets: - logger.error( - f"Asset {asset.name} doesn't have any " - f'`{assoc_traversal.field_name}` associated. ' - 'Skipping this asset in the association traversal.' - ) - continue - candidate_assets = asset.associated_assets[assoc_traversal.field_name] - if assoc_traversal.asset_filter: - candidate_assets = { - asset - for asset in candidate_assets - if asset.lg_asset.name == assoc_traversal.asset_filter.name - } - if assoc_traversal.quantity_filter: - candidate_assets = _apply_quantity_filter( - candidate_assets, assoc_traversal.quantity_filter, rng - ) - next_assets.update(candidate_assets) - return next_assets - - -def _glob_assoc_traversal( - instigating_assets: set[ModelAsset], - glob_assoc_traversal: GlobAssocTraversal, - rng: np.random.Generator, -) -> set[ModelAsset]: - next_assets = traverse_association_chain( - instigating_assets, glob_assoc_traversal.pattern, rng - ) - while True: - new_assets = next_assets | traverse_association_chain( - next_assets, glob_assoc_traversal.pattern, rng - ) - if len(new_assets.difference(next_assets)) == 0: - break - next_assets = new_assets - if glob_assoc_traversal.quantity_filter: - next_assets = _apply_quantity_filter( - next_assets, glob_assoc_traversal.quantity_filter, rng - ) - return next_assets - - -def _assoc_set_traversal( - starting_assets: set[ModelAsset], assoc_set: AssocSet, rng: np.random.Generator -) -> set[ModelAsset]: - candidate_assets = set() - left = traverse_association_chain(starting_assets, assoc_set.left, rng) - right = traverse_association_chain(starting_assets, assoc_set.right, rng) - if assoc_set.set_op == SetOperation.UNION: - candidate_assets = left | right - elif assoc_set.set_op == SetOperation.DIFFERENCE: - candidate_assets = left - right - elif assoc_set.set_op == SetOperation.INTERSECTION: - candidate_assets = left & right - else: - raise ValueError( - f'Unknown set operation {assoc_set.set_op} in association set traversal.' - ) - if assoc_set.quantity_filter: - candidate_assets = _apply_quantity_filter( - candidate_assets, assoc_set.quantity_filter, rng - ) - return candidate_assets - - -def traverse_association_chain( - instigating_assets: set[ModelAsset], - assoc_traversals: AssocTraversalChain, - rng: np.random.Generator, -) -> set[ModelAsset]: - """Traverse the association chain starting from the given asset.""" - - current_assets = instigating_assets - for assoc in assoc_traversals: - if isinstance(assoc, AssocTraversal): - current_assets = _assoc_traversal(current_assets, assoc, rng) - elif isinstance(assoc, GlobAssocTraversal): - current_assets = _glob_assoc_traversal(current_assets, assoc, rng) - elif isinstance(assoc, AssocSet): - current_assets = _assoc_set_traversal(current_assets, assoc, rng) - else: - raise ValueError(f'Unknown association traversal type: {type(assoc)}') - return current_assets - - -def _resolve_terminal_traversal( - instigating_assets: set[ModelAsset], - assoc_traversals: AssocTraversalChain, - rng: np.random.Generator, - terminate: Callable[ - [set[ModelAsset], AssocTraversal], - tuple[set[tuple[ModelAsset, str, AssetTypeOrInstance]], Quantity], - ], -) -> tuple[set[tuple[ModelAsset, str, AssetTypeOrInstance]], Quantity]: - """Traverse `assoc_traversals` like `traverse_association_chain`, except - the very last `AssocTraversal` reached (however deep inside nested - `GlobAssocTraversal`/`AssocSet` structures) is resolved via `terminate` - instead of being traversed to an asset set. - - Used by `parse_addition` and `parse_removal`, which only differ in what - `terminate` does with the final `AssocTraversal`. - """ - if not assoc_traversals: - raise ValueError( - 'Association traversal chain did not terminate in an AssocTraversal.' - ) - current_assets = traverse_association_chain( - instigating_assets, assoc_traversals[:-1], rng - ) - last = assoc_traversals[-1] - if isinstance(last, AssocTraversal): - return terminate(current_assets, last) - elif isinstance(last, GlobAssocTraversal): - # `*` is a repeated application of the pattern, so the termination - # is whatever the pattern itself terminates in. - return _resolve_terminal_traversal(current_assets, last.pattern, rng, terminate) - elif isinstance(last, AssocSet): - left, left_quantity = _resolve_terminal_traversal( - current_assets, last.left, rng, terminate - ) - right, right_quantity = _resolve_terminal_traversal( - current_assets, last.right, rng, terminate - ) - if left_quantity is not None or right_quantity is not None: - raise NotImplementedError( - 'Quantity filtering is not supported set operations in terminal fields.' - ) - terminal: set[tuple[ModelAsset, str, AssetTypeOrInstance]] = set() - if last.set_op == SetOperation.UNION: - terminal = left | right - elif last.set_op == SetOperation.DIFFERENCE: - terminal = left - right - elif last.set_op == SetOperation.INTERSECTION: - terminal = left & right - else: - raise ValueError( - f'Unknown set operation {last.set_op} in association set traversal.' - ) - return terminal, None - else: - raise ValueError(f'Unknown association traversal type: {type(last)}') - - -def parse_addition( - node: AttackGraphNode, - instigating_assets: set[ModelAsset], - assoc_traversals: AssocTraversalChain, - rng: np.random.Generator, -) -> tuple[set[tuple[ModelAsset, str, LanguageGraphAsset | ModelAsset]], Quantity]: - - def _parse_terminating_expr( - node: AttackGraphNode, - instigating_assets: set[ModelAsset], - assoc_traversal: AssocTraversal, - ) -> tuple[set[tuple[ModelAsset, str, LanguageGraphAsset | ModelAsset]], Quantity]: - additions: set[tuple[ModelAsset, str, LanguageGraphAsset | ModelAsset]] = set() - for asset in instigating_assets: - if assoc_traversal.field_name == 'self': - assert node.model_asset, ( - f'Node {node.name} does not have a model asset, ' - 'so cannot traverse to self.' - ) - - if len(asset.lg_asset.associations_to(node.model_asset.lg_asset)) == 0: - raise ValueError( - f'{asset.name} does not have a valid association' - f" to the node's model asset {node.model_asset.name}," - ' so cannot traverse to self.' - ) - for field_name, associated_assets in asset.associated_assets.items(): - if node.model_asset in associated_assets: - additions.add((asset, field_name, node.model_asset)) - - if assoc_traversal.asset_filter: - additions.add( - (asset, assoc_traversal.field_name, assoc_traversal.asset_filter) - ) - else: - assoc = asset.lg_asset.associations[assoc_traversal.field_name] - additions.add( - ( - asset, - assoc_traversal.field_name, - assoc.get_field(assoc_traversal.field_name).asset, - ) - ) - - if assoc_traversal.quantity_filter is not None: - pass - # additions = _apply_quantity_filter( - # additions, assoc_traversal.quantity_filter, rng - # ) - - return additions, assoc_traversal.quantity_filter - - return _resolve_terminal_traversal( - instigating_assets, - assoc_traversals, - rng, - lambda assets, assoc: _parse_terminating_expr(node, assets, assoc), - ) - - -def parse_removal( - node: AttackGraphNode, - instigating_assets: set[ModelAsset], - assoc_traversals: AssocTraversalChain, - rng: np.random.Generator, -) -> tuple[set[tuple[ModelAsset, str, ModelAsset]], Quantity]: - - def _parse_terminating_expr( - node: AttackGraphNode, - instigating_assets: set[ModelAsset], - assoc_traversal: AssocTraversal, - ) -> tuple[set[tuple[ModelAsset, str, ModelAsset]], Quantity]: - removals: set[tuple[ModelAsset, str, ModelAsset]] = set() - for asset in instigating_assets: - if assoc_traversal.field_name == 'self': - assert node.model_asset, ( - f'Node {node.name} does not have a model asset, ' - 'so cannot traverse to self.' - ) - for field_name, associated_assets in asset.associated_assets.items(): - if node.model_asset in associated_assets: - removals.add((asset, field_name, node.model_asset)) - - removal_candidates = asset.associated_assets.get( - assoc_traversal.field_name, set() - ) - if assoc_traversal.asset_filter: - removal_candidates = { - asset - for asset in removal_candidates - if asset.lg_asset.name == assoc_traversal.asset_filter.name - } - for removal_candidate in removal_candidates: - removals.add((asset, assoc_traversal.field_name, removal_candidate)) - if assoc_traversal.quantity_filter is not None: - pass - # removals = _apply_quantity_filter( - # removals, assoc_traversal.quantity_filter, rng - # ) - return removals, assoc_traversal.quantity_filter - - return _resolve_terminal_traversal( - instigating_assets, - assoc_traversals, - rng, - lambda assets, assoc: _parse_terminating_expr(node, assets, assoc), - ) diff --git a/malsim/dyna_mal_simulator/simulator_state.py b/malsim/dyna_mal_simulator/simulator_state.py deleted file mode 100644 index f0930a7c..00000000 --- a/malsim/dyna_mal_simulator/simulator_state.py +++ /dev/null @@ -1,60 +0,0 @@ -from collections.abc import Set -from dataclasses import dataclass - -from maltoolbox.attackgraph import AttackGraph, AttackGraphNode - -from malsim.config.sim_settings import MalSimulatorSettings -from malsim.mal_simulator.graph_state import GraphState -from maltoolbox.language.language_graph_model_effect import ModelEffectType -from maltoolbox.model import ModelAsset - -from malsim.mal_simulator.simulator_state import MalSimulatorState - - -@dataclass(frozen=True) -class AssetOp: - """Represents a change to an asset in the model.""" - - type: ModelEffectType - asset: ModelAsset - - -@dataclass(frozen=True) -class AssocOp: - """Represents a change to the way two assets are associated in the model.""" - - type: ModelEffectType - assoc: tuple[ModelAsset, str, ModelAsset] - - -@dataclass(frozen=True) -class DynaMalSimulatorState(MalSimulatorState): - modification_record: list[AssetOp | AssocOp] - - -def create_simulator_state( - attack_graph: AttackGraph, - graph_state: GraphState, - sim_settings: MalSimulatorSettings, -) -> DynaMalSimulatorState: - return DynaMalSimulatorState( - attack_graph, - sim_settings, - graph_state, - enabled_defenses=graph_state.pre_enabled_defenses, - modification_record=[], - ) - - -def update_simulator_state( - sim_state: DynaMalSimulatorState, - enabled_defenses: Set[AttackGraphNode], - model_effects: list[AssetOp | AssocOp], -) -> DynaMalSimulatorState: - return DynaMalSimulatorState( - sim_state.attack_graph, - sim_state.settings, - sim_state.graph_state, - enabled_defenses=enabled_defenses | sim_state.enabled_defenses, - modification_record=sim_state.modification_record + model_effects, - ) diff --git a/malsim/mal_simulator/attack_surface.py b/malsim/mal_simulator/attack_surface.py deleted file mode 100644 index 15e7ae29..00000000 --- a/malsim/mal_simulator/attack_surface.py +++ /dev/null @@ -1,92 +0,0 @@ -from __future__ import annotations -from typing import TYPE_CHECKING -from collections import deque -from collections.abc import MutableSet, Set - -from malsim.config.node_property_rule import NodePropertyRule -from malsim.config.sim_settings import AttackSurfaceSettings -from malsim.mal_simulator.graph_utils import ( - node_is_actionable, - node_is_necessary, - node_is_traversable, -) - -if TYPE_CHECKING: - from maltoolbox.attackgraph import AttackGraphNode - from malsim.mal_simulator.simulator_state import MalSimulatorState - - -def get_effects_of_attack_step( - sim_state: MalSimulatorState, - attack_step: AttackGraphNode, - performed_nodes: Set[AttackGraphNode], -) -> Set[AttackGraphNode]: - """Get nodes performed as a consequence of `attack_step` being compromised""" - performed = set(performed_nodes) | {attack_step} - effects: MutableSet[AttackGraphNode] = set() - potential_effects = deque( - n for n in attack_step.children if n.causal_mode == 'effect' - ) - while potential_effects: - effect = potential_effects.popleft() - has_visited = performed | set(effects) - if effect not in has_visited and node_is_traversable( - sim_state, has_visited, effect - ): - effects.add(effect) - potential_effects += ( - n for n in effect.children if n.causal_mode == 'effect' - ) - return frozenset(effects) - - -def get_attack_surface( - settings: AttackSurfaceSettings, - sim_state: MalSimulatorState, - actionability: NodePropertyRule[bool] | None, - performed_nodes: Set[AttackGraphNode], - from_nodes: Set[AttackGraphNode] | None = None, -) -> Set[AttackGraphNode]: - """ - Calculate the attack surface of the attacker. - If from_nodes are provided only calculate the attack surface - stemming from those nodes, otherwise use all performed_nodes. - The attack surface includes all of the traversable children nodes. - - Arguments: - agent_name - the agent to get attack surface for - performed_nodes - the nodes the agent has performed - from_nodes - the nodes to calculate the attack surface from - - """ - - from_nodes = from_nodes if from_nodes is not None else performed_nodes - - skip_compromised = settings.skip_compromised - skip_unnecessary = settings.skip_unnecessary - - def uncompromised(node: AttackGraphNode) -> bool: - return node not in performed_nodes - - def necessary(node: AttackGraphNode) -> bool: - return node_is_necessary(sim_state, node) - - def actionable(node: AttackGraphNode) -> bool: - return node_is_actionable(actionability, node) - - def traversable(node: AttackGraphNode) -> bool: - return node_is_traversable(sim_state, performed_nodes, node) - - def in_attack_surface(node: AttackGraphNode) -> bool: - # Nodes marked as effects are not actions/attacks - is_action = node.causal_mode != 'effect' - return ( - is_action - and (uncompromised(node) if skip_compromised else True) - and (necessary(node) if skip_unnecessary else True) - and actionable(node) - and traversable(node) - ) - - from_node_children = {node for parent in from_nodes for node in parent.children} - return frozenset(filter(in_attack_surface, from_node_children)) diff --git a/malsim/mal_simulator/attacker_state_factories.py b/malsim/mal_simulator/attacker_state_factories.py deleted file mode 100644 index 5e59c1a2..00000000 --- a/malsim/mal_simulator/attacker_state_factories.py +++ /dev/null @@ -1,223 +0,0 @@ -"""Creation/manipulation of attacker state""" - -from __future__ import annotations -from collections.abc import MutableSet, Set, Mapping -from typing import TYPE_CHECKING - -from malsim.config.node_property_rule import NodePropertyRule -from malsim.mal_simulator.attack_surface import ( - get_attack_surface, - get_effects_of_attack_step, -) -from malsim.mal_simulator.attacker_state import AttackerState -from malsim.mal_simulator.node_getters import ( - full_name_dict_to_node_dict, - full_name_or_node_to_node, - full_names_or_nodes_to_nodes, -) -from malsim.mal_simulator.ttc_utils import ( - TTCDist, - attack_step_ttc_values, - get_impossible_attack_steps, -) -from malsim.config.agent_settings import AttackerSettings -from malsim.config.sim_settings import ( - AttackSurfaceSettings, - MalSimulatorSettings, - TTCMode, -) - -if TYPE_CHECKING: - from maltoolbox.attackgraph import AttackGraph, AttackGraphNode - from malsim.mal_simulator.simulator_state import MalSimulatorState - import numpy as np - - -def create_attacker_state( - sim_state: MalSimulatorState, - attack_surface_settings: AttackSurfaceSettings, - attacker_settings: AttackerSettings[AttackGraphNode], - name: str, - entry_points: Set[AttackGraphNode], - new_performed_nodes: Set[AttackGraphNode], - ttc_values: Mapping[AttackGraphNode, float], - impossible_steps: Set[AttackGraphNode], - new_attempted_nodes: Set[AttackGraphNode] = frozenset(), - previous_state: AttackerState | None = None, -) -> AttackerState: - """ - Update a previous attacker state based on what the agent compromised - """ - - previous_performed_nodes = ( - previous_state.performed_nodes if previous_state else set() - ) - previous_performed_nodes_order = ( - previous_state.performed_nodes_order if previous_state else {} - ) - previous_attempted_nodes = ( - previous_state.attempted_nodes if previous_state else set() - ) - previous_num_attempts = ( - previous_state.num_attempts - if previous_state - else dict.fromkeys(sim_state.attack_graph.attack_steps, 0) - ) - - performed_nodes = previous_performed_nodes | new_performed_nodes - performed_nodes_order = dict(previous_performed_nodes_order) - attempted_nodes = previous_attempted_nodes | new_attempted_nodes - num_attempts = dict(previous_num_attempts) - for node in new_attempted_nodes: - num_attempts[node] += 1 - - action_surface = get_attack_surface( - settings=attack_surface_settings, - sim_state=sim_state, - actionability=attacker_settings.actionable_steps, - performed_nodes=performed_nodes, - ) - - if not previous_state and not sim_state.settings.compromise_entrypoints_at_start: - action_surface |= entry_points - - iteration = previous_state.iteration if previous_state else 0 - if new_performed_nodes: - performed_nodes_order[iteration] = frozenset(new_performed_nodes) - - return AttackerState( - name, - entry_points=entry_points, - sim_state=sim_state, - iteration=iteration + 1, - performed_nodes_order=performed_nodes_order, - settings=attacker_settings, - ttc_values=ttc_values, - impossible_steps=impossible_steps, - performed_nodes=frozenset(performed_nodes), - attempted_nodes=frozenset(attempted_nodes), - action_surface=action_surface, - num_attempts=num_attempts, - previous_state=previous_state, - goals=attacker_settings.goals, - ) - - -def get_entrypoint_compromises( - sim_state: MalSimulatorState, - entry_points: Set[AttackGraphNode], -) -> Set[AttackGraphNode]: - """Compromise entry points and return compromised nodes including effects""" - step_compromised_nodes: MutableSet[AttackGraphNode] = set() - for entry_point in entry_points: - step_compromised_nodes.add(entry_point) - # Perform effects of entry point compromises - step_compromised_nodes |= get_effects_of_attack_step( - sim_state, entry_point, step_compromised_nodes - ) - return step_compromised_nodes - - -def get_entry_points( - sim_state: MalSimulatorState, - attacker_settings: AttackerSettings[AttackGraphNode], - rng: np.random.Generator, -) -> Set[AttackGraphNode]: - """ - Get entry points as set of AttackGraphNodes from attacker settings. - If multiple sets of entry points are given, sample one set from the options. - """ - - if isinstance(attacker_settings.entry_points, Set): - return frozenset( - full_names_or_nodes_to_nodes( - sim_state.attack_graph, attacker_settings.entry_points - ) - ) - else: - # Multiple potential entry point sets given - # - sample one set of entry points from the options - chosen_entry_points = rng.choice(list(attacker_settings.entry_points)) - return set( - full_names_or_nodes_to_nodes(sim_state.attack_graph, chosen_entry_points) # type: ignore - ) - - -def initial_attacker_state( - sim_state: MalSimulatorState, - sim_settings: MalSimulatorSettings, - attacker_settings: AttackerSettings[AttackGraphNode], - rng: np.random.Generator, -) -> AttackerState: - """Create an attacker state from attacker settings""" - - ttc_values, impossible_steps = ( - attacker_overriding_ttc_settings( - sim_state.attack_graph, - attacker_settings.ttc_dists, - sim_settings.ttc_mode, - rng, - ) - if attacker_settings.ttc_dists - else ( - sim_state.graph_state.ttc_values, - sim_state.graph_state.impossible_attack_steps, - ) - ) - entry_points = get_entry_points(sim_state, attacker_settings, rng) - new_compromised_nodes: Set[AttackGraphNode] = set() - - if sim_state.settings.compromise_entrypoints_at_start: - new_compromised_nodes = get_entrypoint_compromises(sim_state, entry_points) - - return create_attacker_state( - sim_state=sim_state, - attack_surface_settings=sim_settings.attack_surface, - attacker_settings=attacker_settings, - name=attacker_settings.name, - ttc_values=ttc_values, - impossible_steps=impossible_steps, - new_performed_nodes=new_compromised_nodes, - entry_points=entry_points, - ) - - -def attacker_overriding_ttc_settings( - attack_graph: AttackGraph, - ttc_overrides_rule: NodePropertyRule[TTCDist], - ttc_mode: TTCMode, - rng: np.random.Generator, -) -> tuple[ - Mapping[AttackGraphNode, float], - Set[AttackGraphNode], -]: - """ - Get overriding TTC distributions, TTC values, and impossible attack steps - from attacker settings if they exist. - - Returns three separate collections: - - a dict of TTC distributions - - a dict of TTC values - - a set of impossible steps - """ - - ttc_overrides_names = ttc_overrides_rule.per_node(attack_graph) - - # Convert names to TTCDist objects and map from AttackGraphNode - # objects instead of from full names - ttc_overrides = { - full_name_or_node_to_node(attack_graph, node): TTCDist.from_name(name) - for node, name in ttc_overrides_names.items() - } - ttc_value_overrides = attack_step_ttc_values( - ttc_overrides.keys(), - rng, - ttc_mode, - ttc_dists=full_name_dict_to_node_dict(attack_graph, ttc_overrides), - ) - impossible_step_overrides = get_impossible_attack_steps( - ttc_overrides.keys(), - rng, - ttc_dists=full_name_dict_to_node_dict(attack_graph, ttc_overrides), - ) - return ttc_value_overrides, impossible_step_overrides diff --git a/malsim/mal_simulator/attacker_step.py b/malsim/mal_simulator/attacker_step.py deleted file mode 100644 index 98bcfbe5..00000000 --- a/malsim/mal_simulator/attacker_step.py +++ /dev/null @@ -1,171 +0,0 @@ -from __future__ import annotations -from typing import TYPE_CHECKING -import logging - -import numpy as np - -from maltoolbox.attackgraph import AttackGraphNode - -from malsim.mal_simulator.attack_surface import get_effects_of_attack_step -from malsim.mal_simulator.graph_utils import ( - node_is_traversable, -) -from malsim.mal_simulator.state_query import node_ttc_value -from malsim.config.sim_settings import TTCMode -from malsim.mal_simulator.ttc_utils import ( - TTCDist, -) -from malsim.mal_simulator.simulator_state import MalSimulatorState - -if TYPE_CHECKING: - from malsim.mal_simulator.attacker_state import AttackerState - -logger = logging.getLogger(__name__) - - -def attacker_is_terminated(attacker_state: AttackerState) -> bool: - """Check if attacker is terminated - Can be overridden by subclass for custom termination condition. - - Args: - - attacker_state: the attacker state to check for termination - """ - - if len(attacker_state.action_surface) == 0: - # Attacker is terminated if it has no more actions to take - logger.info( - 'Attacker "%s" action surface is empty, terminate', attacker_state.name - ) - return True - goals = attacker_state.settings.goals - if goals: - # Attacker is terminated if it has goals and all goals are met - return goals & attacker_state.performed_nodes == goals - # Otherwise not terminated - return False - - -def attacker_step_effects( - sim_state: MalSimulatorState, agent: AttackerState, action: AttackGraphNode -) -> list[AttackGraphNode]: - """Perform the effects of an action performed by attacker. - Return the effects that descend from `action` and can be compromised. - """ - # Find effects based on each action - performed_effects = [] - effects_by_action = get_effects_of_attack_step( - sim_state, action, agent.performed_nodes - ) - for effect in effects_by_action: - performed_effects.append(effect) - logger.info( - 'Attacker agent "%s" compromised "%s" as an effect of compromising %s.', - agent.name, - effect.full_name, - action.full_name, - ) - return performed_effects - - -def attempt_attacker_step( - sim_state: MalSimulatorState, - rng: np.random.Generator, - ttc_mode: TTCMode, - agent: AttackerState, - node: AttackGraphNode, -) -> bool: - """Attempt a step with a TTC distribution. - - Return True if the attempt was successful. - """ - - num_attempts = agent.num_attempts[node] + 1 - - if agent.settings.ttc_dists and node in agent.settings.ttc_dists: - # If this agent has custom ttc distribution set for this node, use it - ttc_dist = agent.settings.ttc_dists[node] - else: - ttc_dist = TTCDist.from_node(node) - - if ttc_mode == TTCMode.DISABLED: - # Always suceed if disabled TTCs - return True - - elif ttc_mode == TTCMode.EFFORT_BASED_PER_STEP_SAMPLE: - # Run trial to decide success if config says so (SANDOR mode) - return ttc_dist.attempt_ttc_with_effort(num_attempts, rng) - - elif ttc_mode == TTCMode.PER_STEP_SAMPLE: - # Sample ttc value every time if config says so (ANDREI mode) - _node_ttc_value = ttc_dist.sample_value(rng) - return _node_ttc_value <= 1 - - # Compare attempts to ttc expected value in EXPECTED_VALUE mode - # or presampled ttcs in PRE_SAMPLE mode - elif ttc_mode in (TTCMode.EXPECTED_VALUE, TTCMode.PRE_SAMPLE): - _node_ttc_value = node_ttc_value(agent, node) - return num_attempts + 1 >= _node_ttc_value - - else: - raise ValueError(f'Invalid TTC mode: {ttc_mode}') - - -def attacker_step( - sim_state: MalSimulatorState, - agent: AttackerState, - nodes: list[AttackGraphNode], - rng: np.random.Generator, -) -> tuple[list[AttackGraphNode], list[AttackGraphNode]]: - """Compromise attack step nodes with attacker - - Args: - agent - the agent to compromise nodes with - nodes - the nodes to compromise - - Returns: two lists with compromised, attempted nodes - """ - - logger.debug('Stepping with agent %s', agent.name) - successful_compromises: list[AttackGraphNode] = [] - attempted_compromises: list[AttackGraphNode] = [] - - for node in nodes: - assert node == sim_state.attack_graph.nodes[node.id], ( - f'{agent.name} tried to enable a node that is not part ' - 'of this simulators attack_graph. Make sure the node ' - 'comes from the agents action surface.' - ) - - if node in agent.settings.entry_points: - # Entrypoints can always be compromised - # TODO: should this actually be the case? - can_compromise = True - else: - # Otherwise attacker is limited by attack surface and traversability - can_compromise = node in agent.action_surface and node_is_traversable( - sim_state, agent.performed_nodes, node - ) - - if can_compromise: - if attempt_attacker_step( - sim_state, rng, sim_state.settings.ttc_mode, agent, node - ): - successful_compromises.append(node) - logger.info( - 'Attacker agent "%s" compromised "%s"', agent.name, node.full_name - ) - # Run effects as a compromise of performing `node` - successful_compromises += attacker_step_effects(sim_state, agent, node) - else: - logger.info( - 'Attacker agent "%s" attempted "%s" (attempt %d).', - agent.name, - node.full_name, - agent.num_attempts[node], - ) - attempted_compromises.append(node) - - else: - logger.warning('Attacker could not compromise %s', node.full_name) - - return successful_compromises, attempted_compromises diff --git a/malsim/mal_simulator/defender_state_factories.py b/malsim/mal_simulator/defender_state_factories.py deleted file mode 100644 index 13396374..00000000 --- a/malsim/mal_simulator/defender_state_factories.py +++ /dev/null @@ -1,102 +0,0 @@ -"""Creation/manipulation of defender state""" - -from collections.abc import Set - -import numpy as np -from maltoolbox.attackgraph import AttackGraphNode - -from malsim.config.agent_settings import DefenderSettings -from malsim.mal_simulator.defender_state import DefenderState -from malsim.mal_simulator.defense_surface import get_defense_surface -from malsim.mal_simulator.observability import observed_nodes -from malsim.mal_simulator.event_logger import collect_logs, collect_false_positives -from malsim.mal_simulator.simulator_state import MalSimulatorState - - -def create_defender_state( - sim_state: MalSimulatorState, - name: str, - rng: np.random.Generator, - defender_settings: DefenderSettings, - new_compromised_nodes: Set[AttackGraphNode] = frozenset(), - new_enabled_defenses: Set[AttackGraphNode] = frozenset(), - previous_state: DefenderState | None = None, -) -> DefenderState: - """ - Update a previous defender state based on what steps - were enabled/compromised during last step - """ - previous_enabled_defenses = ( - previous_state.performed_nodes if previous_state else set() - ) - previous_compromised_nodes = ( - previous_state.compromised_nodes if previous_state else set() - ) - previous_performed_nodes = ( - previous_state.performed_nodes if previous_state else set() - ) - previous_observed_nodes = previous_state.observed_nodes if previous_state else set() - performed_nodes_order = ( - dict(previous_state.performed_nodes_order) if previous_state else {} - ) - - action_surface = ( - get_defense_surface(sim_state, defender_settings.actionable_steps) - - previous_performed_nodes - ) - - iteration = previous_state.iteration if previous_state else 0 - if new_enabled_defenses: - performed_nodes_order[iteration] = frozenset(new_enabled_defenses) - - new_observed_nodes = observed_nodes( - defender_settings.observable_steps, - defender_settings.false_positive_rates, - defender_settings.false_negative_rates, - sim_state, - rng, - new_compromised_nodes, - ) - - logs = collect_logs( - previous_state.iteration if previous_state else 0, - new_compromised_nodes, - previous_compromised_nodes, - rng, - ) + collect_false_positives( - previous_state.iteration if previous_state else 0, - sim_state.attack_graph.detectors, - rng, - ) - - return DefenderState( - name, - sim_state=sim_state, - settings=defender_settings, - performed_nodes=frozenset(previous_enabled_defenses | new_enabled_defenses), - compromised_nodes=frozenset(previous_compromised_nodes | new_compromised_nodes), - observed_nodes=frozenset(previous_observed_nodes | new_observed_nodes), - action_surface=frozenset(action_surface), - iteration=iteration + 1, - performed_nodes_order=performed_nodes_order, - previous_state=previous_state, - logs=tuple(previous_state.logs + logs) if previous_state else tuple(logs), - ) - - -def initial_defender_state( - sim_state: MalSimulatorState, - defender_settings: DefenderSettings, - pre_compromised_nodes: Set[AttackGraphNode], - pre_enabled_defenses: Set[AttackGraphNode], - rng: np.random.Generator, -) -> DefenderState: - """Create a defender state from defender settings""" - return create_defender_state( - sim_state=sim_state, - name=defender_settings.name, - new_compromised_nodes=pre_compromised_nodes, - new_enabled_defenses=pre_enabled_defenses, - rng=rng, - defender_settings=defender_settings, - ) diff --git a/malsim/mal_simulator/defender_step.py b/malsim/mal_simulator/defender_step.py deleted file mode 100644 index 896a4f54..00000000 --- a/malsim/mal_simulator/defender_step.py +++ /dev/null @@ -1,69 +0,0 @@ -from __future__ import annotations -from typing import TYPE_CHECKING -from maltoolbox.attackgraph import AttackGraphNode -import logging - - -from malsim.mal_simulator.agent_states import attacker_states -from malsim.mal_simulator.attacker_step import attacker_is_terminated -from malsim.mal_simulator.simulator_state import MalSimulatorState - -if TYPE_CHECKING: - from malsim.mal_simulator.agent_states import AgentStates - from malsim.mal_simulator.defender_state import DefenderState - -logger = logging.getLogger(__name__) - - -def defender_is_terminated(agent_states: AgentStates) -> bool: - """Check if defender is terminated - Can be overridden by subclass for custom termination condition. - """ - # Defender is terminated if all attackers are terminated - return all( - attacker_is_terminated(a) for a in attacker_states(agent_states).values() - ) - - -def defender_step( - sim_state: MalSimulatorState, - agent: DefenderState, - nodes: list[AttackGraphNode], -) -> list[AttackGraphNode]: - """Enable defense step nodes with defender. - - Args: - agent - the agent to activate defense nodes with - nodes - the defense step nodes to enable - - Returns a tuple of a list and a set, `enabled_defenses` - """ - - logger.debug('Stepping with %s', agent.name) - enabled_defenses: list[AttackGraphNode] = [] - - for node in nodes: - assert node == sim_state.attack_graph.nodes[node.id], ( - f'{agent.name} tried to enable a node that is not part ' - 'of this simulators attack_graph. Make sure the node ' - 'comes from the agents action surface.' - ) - - if node not in agent.action_surface: - logger.warning( - 'Defender agent "%s" tried to step through "%s"(%d), ' - 'which is not part of its defense surface. Defender ' - 'step will skip!', - agent.name, - node.full_name, - node.id, - ) - else: - enabled_defenses.append(node) - logger.info( - 'Defender agent "%s" enabled "%s"', - agent.name, - node.full_name, - ) - - return enabled_defenses diff --git a/malsim/mal_simulator/defense_surface.py b/malsim/mal_simulator/defense_surface.py deleted file mode 100644 index 111326b6..00000000 --- a/malsim/mal_simulator/defense_surface.py +++ /dev/null @@ -1,31 +0,0 @@ -from __future__ import annotations -from collections.abc import Set -from typing import TYPE_CHECKING - -from malsim.config.node_property_rule import NodePropertyRule -from malsim.mal_simulator.graph_utils import node_is_actionable, node_is_blocked - -if TYPE_CHECKING: - from maltoolbox.attackgraph import AttackGraphNode - from malsim.mal_simulator.simulator_state import MalSimulatorState - - -def get_defense_surface( - sim_state: MalSimulatorState, - agent_actionability_rule: NodePropertyRule[bool] | None, -) -> Set[AttackGraphNode]: - """Get the defense surface. - All non-suppressed defense steps that are not already enabled. - - Arguments: - graph - the attack graph - """ - - return { - node - for node in sim_state.attack_graph.defense_steps - if node_is_actionable(agent_actionability_rule, node) - and not node_is_blocked(sim_state, node) - and node not in sim_state.enabled_defenses - and 'suppress' not in node.tags - } diff --git a/malsim/mal_simulator/event_logger.py b/malsim/mal_simulator/event_logger.py deleted file mode 100644 index 64f02bfe..00000000 --- a/malsim/mal_simulator/event_logger.py +++ /dev/null @@ -1,141 +0,0 @@ -"""EventLog functionality which uses detectors to generate logs of compromised nodes""" - -from collections.abc import Iterable -from dataclasses import dataclass -import logging -import numpy as np - -from maltoolbox.attackgraph import AttackGraphNode, Detector - -logger = logging.getLogger(__name__) - - -@dataclass(frozen=True) -class LogEntry: - """A single log entry produced by a detector.""" - - timestep: int - detector_name: str - detector: Detector - trigger: AttackGraphNode - context: dict[str, AttackGraphNode] - false_positive: bool = False - - -def collect_false_positives( - iteration: int, - detectors: Iterable[Detector], - rng: np.random.Generator, -) -> tuple[LogEntry, ...]: - """Generates logs for false positives from given detectors.""" - logs = [] - for detector in detectors: - if detector.fprate and detector.fprate >= rng.random(): - logs.append( - LogEntry( - timestep=iteration, - detector_name=str(detector.name), - detector=detector, - trigger=detector.node, - context=get_random_context(detector), - false_positive=True, - ) - ) - logging.debug( - 'Detector %s false positive on %s', - detector.name, - detector.node.full_name, - ) - else: - logging.debug( - 'Detector %s true negative on %s', - detector.name, - detector.node.full_name, - ) - - return tuple(logs) - - -def collect_logs( - iteration: int, - step_compromised_nodes: Iterable[AttackGraphNode], - previous_compromised_nodes: Iterable[AttackGraphNode], - rng: np.random.Generator, -) -> tuple[LogEntry, ...]: - """Generates logs from an agents latest step. - - Args: - attack_state: The attacker state from which to collect logs. - """ - logs = [] - for attack_step in step_compromised_nodes: - for detector in attack_step.detectors.values(): - labeled_steps = get_context( - detector, - previous_compromised_nodes, - ) - assert attack_step.model_asset is not None, 'Attack step has no model asset' - if not detector.tprate or detector.tprate >= rng.random(): - logs.append( - LogEntry( - timestep=iteration, - detector_name=str(detector.name), - detector=detector, - trigger=attack_step, - context=labeled_steps, - ) - ) - logging.debug( - 'Detector %s true positive on %s', - detector.name, - attack_step.full_name, - ) - else: - logging.debug( - 'Detector %s false negative on %s', - detector.name, - attack_step.full_name, - ) - - return tuple(logs) - - -def get_context( - detector: Detector, - previous_compromised_nodes: Iterable[AttackGraphNode], -) -> dict[str, AttackGraphNode]: - """Finds node that satisfies the context of a detector - among the previously compromised nodes. - - Args: - detector: The detector whose context nodes we want to find. - previous_compromised_nodes: nodes that have been previously compromised. - - Returns: - A dictionary where keys are the detector context labels and values are the - nodes that have been selected to populate the context. - """ - context_nodes = {} - - for label, potential_context_nodes in detector.potential_context.items(): - node = next( - node - for node in potential_context_nodes - if node in previous_compromised_nodes - ) - - if node is not None: - context_nodes[label] = node - - return context_nodes - - -def get_random_context(detector: Detector) -> dict[str, AttackGraphNode]: - """Finds random nodes that satisfies the context of a detector""" - context_nodes = {} - - for label, potential_context_nodes in detector.potential_context.items(): - node = next(node for node in potential_context_nodes) - if node is not None: - context_nodes[label] = node - return context_nodes diff --git a/malsim/mal_simulator/false_alerts.py b/malsim/mal_simulator/false_alerts.py deleted file mode 100644 index 9c7a656d..00000000 --- a/malsim/mal_simulator/false_alerts.py +++ /dev/null @@ -1,59 +0,0 @@ -"""Functions to generate false negatives/positives in the simulator""" - -from __future__ import annotations -from collections.abc import Set - -import numpy as np -from maltoolbox.attackgraph import AttackGraph, AttackGraphNode -from malsim.config.node_property_rule import NodePropertyRule - - -def node_false_negative_rate( - node: AttackGraphNode, - false_negative_rates_rule: NodePropertyRule[float] | None = None, -) -> float: - if false_negative_rates_rule: - return false_negative_rates_rule.value(node, 0.0) - return 0.0 - - -def generate_false_negatives( - false_negative_rate_rule: NodePropertyRule[float] | None, - observed_nodes: Set[AttackGraphNode], - rng: np.random.Generator, -) -> Set[AttackGraphNode]: - """Return a set of false negative attack steps from observed nodes""" - if false_negative_rate_rule: - return { - node - for node in observed_nodes - if rng.random() < node_false_negative_rate(node, false_negative_rate_rule) - } - else: - return set() - - -def node_false_positive_rate( - node: AttackGraphNode, - false_positive_rates_rule: NodePropertyRule[float] | None = None, -) -> float: - if false_positive_rates_rule: - # FPR from agent settings - return float(false_positive_rates_rule.value(node, 0.0)) - return 0.0 - - -def generate_false_positives( - false_positive_rates_rule: NodePropertyRule[float] | None, - attack_graph: AttackGraph, - rng: np.random.Generator, -) -> Set[AttackGraphNode]: - """Return a set of false positive attack steps from attack graph""" - if false_positive_rates_rule: - return { - node - for node in attack_graph.attack_steps - if rng.random() < node_false_positive_rate(node, false_positive_rates_rule) - } - else: - return set() diff --git a/malsim/mal_simulator/graph_processing.py b/malsim/mal_simulator/graph_processing.py deleted file mode 100644 index 5499cfd8..00000000 --- a/malsim/mal_simulator/graph_processing.py +++ /dev/null @@ -1,271 +0,0 @@ -""" -MAL Simulator Attack Graph Processing Submodule - -This submodule is meant to process the attack graph before running the -simulation. -Currently it adds the following information to nodes: -- Viability (deprecated) = Determine if a node can be traversed under any circumstances - or if the model structure, defenses or ttcs makes it unviable. -- Necessity = Determine if a node is necessary for the attacker or if the - model structure means it is not needed (it behaves as if it were already - compromised) to compromise children attack steps. -""" - -from __future__ import annotations -from collections.abc import Mapping, Set -import logging -from maltoolbox.attackgraph import AttackGraph, AttackGraphNode - -logger = logging.getLogger(__name__) - - -def _propagate_necessity_from_node( - node: AttackGraphNode, necessity_per_node: dict[AttackGraphNode, bool] -) -> Set[AttackGraphNode]: - """ - Update necessity of children of node given as parameter. Propagate - recursively via children as long as changes occur. Return all nodes which - have changed necessity. - - Arguments: - node - the attack graph node from which to propagate - the necessity status - necessity_per_node - each nodes necessity status - - Returns: - changed_nodes - set of nodes that have changed necessity - """ - changed_nodes = set() - for child in node.children: - is_necessary = evaluate_necessity(child, necessity_per_node, frozenset()) - if is_necessary != necessity_per_node[child]: - necessity_per_node[child] = is_necessary - changed_nodes |= {child} | _propagate_necessity_from_node( - child, necessity_per_node - ) - return frozenset(changed_nodes) - - -def evaluate_necessity( - node: AttackGraphNode, - necessity_per_node: Mapping[AttackGraphNode, bool], - enabled_defenses: Set[AttackGraphNode], -) -> bool: - """ - Arguments: - node - the node to evaluate necessity for. - necessary_nodes - set of all necessary nodes - enabled_defenses - set of all enabled defenses - """ - - match node.type: - case 'exist': - assert isinstance(node.existence_status, bool), ( - f'Existence status not defined for {node.full_name}.' - ) - return not node.existence_status - case 'notExist': - assert isinstance(node.existence_status, bool), ( - f'Existence status not defined for {node.full_name}.' - ) - return bool(node.existence_status) - case 'defense': - return node in enabled_defenses - case 'or': - return ( - all(necessity_per_node[parent] for parent in node.parents) - or not node.parents - ) - case 'and': - return ( - any(necessity_per_node[parent] for parent in node.parents) - or not node.parents - ) - case _: - msg = ( - 'Evaluate necessity was provided node "%s"(%d) which ' - 'is of unknown type "%s"' - ) - logger.error(msg, node.full_name, node.id, node.type) - raise ValueError(msg % (node.full_name, node.id, node.type)) - - -def calculate_necessity( - graph: AttackGraph, - enabled_defenses: Set[AttackGraphNode], -) -> Mapping[AttackGraphNode, bool]: - """Calculate necessity for an attack graph - - arguments: - graph - graph with nodes to calculate necessity for - enabled_defenses - defenses that are enabled, affects necessity - - Returns necessity per node in a dict - """ - - necessity_per_node = dict.fromkeys(graph.nodes.values(), True) - for node in graph.nodes.values(): - if node.type in ['exist', 'notExist', 'defense']: - necessity_per_node[node] = evaluate_necessity( - node, necessity_per_node, enabled_defenses - ) - if not necessity_per_node[node]: - _propagate_necessity_from_node(node, necessity_per_node) - return necessity_per_node - - -def _propagate_viability_from_node( - node: AttackGraphNode, - viability_per_node: dict[AttackGraphNode, bool], - impossible_attack_steps: Set[AttackGraphNode], -) -> Set[AttackGraphNode]: - """ - Update viability of children of node given as parameter. Propagate - recursively via children as long as changes occur. Return all nodes which - have changed viability. - - Arguments: - node - the node from which to propagate viability - viability_per_node - dict of node viabilities - ttc_values - dict with ttc values of nodes - - Returns: - changed_nodes - set of nodes that have changed viability as a result - """ - changed_nodes = set() - for child in node.children: - is_viable = evaluate_viability( - child, viability_per_node, frozenset(), impossible_attack_steps - ) - if is_viable != viability_per_node[child]: - viability_per_node[child] = is_viable - changed_nodes |= {child} | _propagate_viability_from_node( - child, viability_per_node, impossible_attack_steps - ) - return frozenset(changed_nodes) - - -def evaluate_viability( - node: AttackGraphNode, - viability_per_node: dict[AttackGraphNode, bool], - enabled_defenses: Set[AttackGraphNode], - impossible_attack_steps: Set[AttackGraphNode], -) -> bool: - """ - Arguments: - node - the node to evaluate viability for - viable_nodes - set of all viable nodes - enabled_defenses - set of all enabled defenses - ttc_values - a dictionary containing the ttc values of each node - """ - if node in impossible_attack_steps: - # Impossible step is not viable - return False - - match node.type: - case 'exist': - assert isinstance(node.existence_status, bool), ( - f'Existence status not defined for {node.full_name}.' - ) - return node.existence_status - case 'notExist': - assert isinstance(node.existence_status, bool), ( - f'Existence status not defined for {node.full_name}.' - ) - return not node.existence_status - case 'defense': - return node not in enabled_defenses - case 'or': - return ( - any(viability_per_node[parent] for parent in node.parents) - or not node.parents - ) - case 'and': - return ( - all(viability_per_node[parent] for parent in node.parents) - or not node.parents - ) - case _: - msg = ( - 'Evaluate viability was provided node "%s"(%d) which ' - 'is of unknown type "%s"' - ) - logger.error(msg, node.full_name, node.id, node.type) - raise ValueError(msg % (node.full_name, node.id, node.type)) - - -def calculate_viability( - graph: AttackGraph, - enabled_defenses: Set[AttackGraphNode], - impossible_attack_steps: Set[AttackGraphNode], -) -> Mapping[AttackGraphNode, bool]: - """Calculate viability for an attack graph - - graph - graph with nodes to calculate viability for - enabled_defenses - defenses that are enabled, affects viability - ttc_values - ttc values per node, affects viability - - Returns viability per node in a dict - """ - viability_per_node = dict.fromkeys(graph.nodes.values(), True) - for node in graph.nodes.values(): - viability_per_node[node] = evaluate_viability( - node, viability_per_node, enabled_defenses, impossible_attack_steps - ) - if not viability_per_node[node]: - _propagate_viability_from_node( - node, viability_per_node, impossible_attack_steps - ) - return viability_per_node - - -def make_node_unviable( - node: AttackGraphNode, - viability_per_node: dict[AttackGraphNode, bool], - impossible_attacksteps: Set[AttackGraphNode], -) -> tuple[dict[AttackGraphNode, bool], Set[AttackGraphNode]]: - """Make a node unviable - - Return the new viability dict and nodes made unviable as - a result of making `node` unviable - """ - viability_per_node[node] = False - nodes_made_unviable = _propagate_viability_from_node( - node, viability_per_node, impossible_attacksteps - ) - return viability_per_node, nodes_made_unviable - - -def prune_unviable_and_unnecessary_nodes( - graph: AttackGraph, - viability_per_node: dict[AttackGraphNode, bool], - necessity_per_node: dict[AttackGraphNode, bool], -) -> None: - """ - Arguments: - graph - the attack graph for which we wish to remove the - the nodes which are not viable or necessary. - viable_nodes - set of all viable nodes - necessary_nodes - set of all necessary nodes - - """ - logger.debug('Prune unviable and unnecessary nodes from the attack graph.') - - nodes_to_remove = set() - - for node in graph.nodes.values(): - if node.type in ('or', 'and') and ( - not viability_per_node[node] or not necessity_per_node[node] - ): - nodes_to_remove.add(node) - - # Do the removal separatly so we don't remove - # nodes from a set we are looping over - for node in nodes_to_remove: - logger.debug( - 'Remove %s node "%s"(%d) from attack graph.', - 'unviable' if necessity_per_node[node] else 'unnecessary', - node.full_name, - node.id, - ) - graph.remove_node(node) diff --git a/malsim/mal_simulator/graph_state.py b/malsim/mal_simulator/graph_state.py deleted file mode 100644 index 7cf4a363..00000000 --- a/malsim/mal_simulator/graph_state.py +++ /dev/null @@ -1,61 +0,0 @@ -"""Dataclass and function to store and create graph state used in the simulator""" - -from __future__ import annotations -from collections.abc import Set, Mapping - -from dataclasses import dataclass -from numpy.random import Generator -from maltoolbox.attackgraph import AttackGraph, AttackGraphNode -from malsim.mal_simulator.ttc_utils import ( - attack_step_ttc_values, - get_pre_enabled_defenses, - get_impossible_attack_steps, -) -from malsim.mal_simulator.graph_processing import calculate_necessity - - -from malsim.config.sim_settings import MalSimulatorSettings - - -@dataclass -class GraphState: - """Dataclass containing simulator specific graph state""" - - ttc_values: Mapping[AttackGraphNode, float] - pre_enabled_defenses: Set[AttackGraphNode] - impossible_attack_steps: Set[AttackGraphNode] - necessity_per_node: Mapping[AttackGraphNode, bool] - - -def compute_initial_graph_state( - graph: AttackGraph, settings: MalSimulatorSettings, rng: Generator -) -> GraphState: - """Compute attack graph initial state based on probabilities and settings - - Compute ttc values, enabled defenses, impossible attack steps - and necessity for an attack graph based on given settings. - """ - - # TTC (Time to compromise) for each attack step - # will only be set if TTCMode PRE_SAMLE/EXPECTED_VALUE is used - ttc_values = attack_step_ttc_values( - graph.attack_steps, rng=rng, ttc_mode=settings.ttc_mode - ) - # These steps will be enabled from the start of the simulation - # depending on if bernoullis are sampled or not - enabled_defenses = get_pre_enabled_defenses( - graph.defense_steps, settings.run_defense_step_bernoullis, rng - ) - impossible_attack_steps: Set[AttackGraphNode] = frozenset() - if settings.run_attack_step_bernoullis: - # These steps will not be traversable - impossible_attack_steps = get_impossible_attack_steps(graph.attack_steps, rng) - - necessity_per_node = calculate_necessity(graph, enabled_defenses) - - return GraphState( - ttc_values=ttc_values, - pre_enabled_defenses=enabled_defenses, - impossible_attack_steps=impossible_attack_steps, - necessity_per_node=necessity_per_node, - ) diff --git a/malsim/mal_simulator/observability.py b/malsim/mal_simulator/observability.py deleted file mode 100644 index 062e94e3..00000000 --- a/malsim/mal_simulator/observability.py +++ /dev/null @@ -1,51 +0,0 @@ -from __future__ import annotations -from collections.abc import Set - -import numpy as np - -from maltoolbox.attackgraph import AttackGraphNode - -from malsim.config.node_property_rule import NodePropertyRule -from malsim.mal_simulator.simulator_state import MalSimulatorState -from malsim.mal_simulator.false_alerts import ( - generate_false_negatives, - generate_false_positives, -) - - -def node_is_observable( - agent_observability_rule: NodePropertyRule[bool], - node: AttackGraphNode, -) -> bool: - return bool(agent_observability_rule.value(node, False)) - - -def observed_nodes( - observable_steps_rule: NodePropertyRule[bool] | None, - false_positive_rates_rule: NodePropertyRule[float] | None, - false_negative_rates_rule: NodePropertyRule[float] | None, - sim_state: MalSimulatorState, - rng: np.random.Generator, - compromised_nodes: Set[AttackGraphNode], -) -> Set[AttackGraphNode]: - """Generate set of observed compromised nodes - From set of compromised nodes, generate observed nodes for an agent - in regards to observability, false negatives and false positives. - """ - observable_steps = ( - {n for n in compromised_nodes if node_is_observable(observable_steps_rule, n)} - if observable_steps_rule - else compromised_nodes - ) - false_negatives = generate_false_negatives( - false_negative_rates_rule, - compromised_nodes, - rng, - ) - false_positives = generate_false_positives( - false_positive_rates_rule, - sim_state.attack_graph, - rng, - ) - - return (observable_steps - false_negatives) | false_positives diff --git a/malsim/mal_simulator/reset_agent.py b/malsim/mal_simulator/reset_agent.py deleted file mode 100644 index ea39afb0..00000000 --- a/malsim/mal_simulator/reset_agent.py +++ /dev/null @@ -1,80 +0,0 @@ -from collections.abc import Mapping, Set - - -import numpy as np - -from maltoolbox.attackgraph import AttackGraphNode - -from malsim.config.sim_settings import MalSimulatorSettings -from .attacker_state import AttackerState -from malsim.mal_simulator.attacker_state_factories import initial_attacker_state -from .defender_state import DefenderState -from malsim.mal_simulator.defender_state_factories import initial_defender_state -from malsim.mal_simulator.simulator_state import MalSimulatorState -from malsim.config.agent_settings import ( - AgentSettings, - get_defender_settings, - get_attacker_settings, -) -from malsim.mal_simulator.agent_states import AgentStates - - -def reset_attackers( - sim_state: MalSimulatorState, - sim_settings: MalSimulatorSettings, - agent_settings: AgentSettings, - rng: np.random.Generator, -) -> Mapping[str, AttackerState]: - """Recreate all attacker agent states""" - - return { - name: initial_attacker_state( - sim_state, - sim_settings=sim_settings, - attacker_settings=attacker_settings, - rng=rng, - ) - for name, attacker_settings in get_attacker_settings(agent_settings).items() - } - - -def reset_defenders( - sim_state: MalSimulatorState, - agent_settings: AgentSettings, - pre_compromised_nodes: Set[AttackGraphNode], - rng: np.random.Generator, -) -> Mapping[str, DefenderState]: - """Recreate all defender agent states""" - return { - name: initial_defender_state( - sim_state, - defender_settings, - pre_compromised_nodes, - sim_state.graph_state.pre_enabled_defenses, - rng, - ) - for name, defender_settings in get_defender_settings(agent_settings).items() - } - - -def reset_agents( - sim_state: MalSimulatorState, - sim_settings: MalSimulatorSettings, - agent_settings: AgentSettings, - rng: np.random.Generator, -) -> AgentStates: - """Reset agent states to a fresh start""" - - attacker_states = reset_attackers(sim_state, sim_settings, agent_settings, rng) - - pre_compromised_nodes: Set[AttackGraphNode] = { - node - for state in attacker_states.values() - for node in state.step_performed_nodes - } - - defender_states = reset_defenders( - sim_state, agent_settings, pre_compromised_nodes, rng - ) - - return {**attacker_states, **defender_states} diff --git a/malsim/mal_simulator/simulator_static_data.py b/malsim/mal_simulator/simulator_static_data.py deleted file mode 100644 index 7a4c7b15..00000000 --- a/malsim/mal_simulator/simulator_static_data.py +++ /dev/null @@ -1,14 +0,0 @@ -from typing import NamedTuple -from maltoolbox.attackgraph import AttackGraph, AttackGraphNode - -from malsim.config.sim_settings import MalSimulatorSettings - - -class MALSimulatorStaticData(NamedTuple): - attack_graph: AttackGraph - sim_settings: MalSimulatorSettings - rewards: dict[str, float] | dict[AttackGraphNode, float] | None = None - false_positive_rates: dict[str, float] | dict[AttackGraphNode, float] | None = None - false_negative_rates: dict[str, float] | dict[AttackGraphNode, float] | None = None - node_actionabilities: dict[str, bool] | dict[AttackGraphNode, bool] | None = None - node_observabilities: dict[str, bool] | dict[AttackGraphNode, bool] | None = None diff --git a/py-bindings/Cargo.lock b/py-bindings/Cargo.lock new file mode 100644 index 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new file mode 100644 index 00000000..072db471 --- /dev/null +++ b/py-bindings/malsim-pyo3/Cargo.toml @@ -0,0 +1,20 @@ +[package] +name = "malsim-pyo3" +version.workspace = true +edition.workspace = true +license.workspace = true +publish.workspace = true + +[lib] +name = "_native" +crate-type = ["cdylib"] +path = "src/lib.rs" + +[dependencies] +malsim-core = { path = "../../core/malsim-core" } +maltoolbox-attackgraph = { workspace = true } +# Already in the dependency tree transitively via malsim-core (same pinned +# version) - added directly here because Phase B3's Model capsule +# extraction needs to name `maltoolbox_model::Model` itself. +maltoolbox-model = { workspace = true } +pyo3 = { workspace = true, features = ["extension-module"] } diff --git a/py-bindings/malsim-pyo3/src/dyna_simulator.rs b/py-bindings/malsim-pyo3/src/dyna_simulator.rs new file mode 100644 index 00000000..6058c637 --- /dev/null +++ b/py-bindings/malsim-pyo3/src/dyna_simulator.rs @@ -0,0 +1,160 @@ +//! `malsim._native.DynaSimulator` - the pyclass backing +//! `DynaMalSimulator.reset()`/`.step()`: a thin wrapper around +//! `malsim_core::dyna_simulator::DynaSimulator`, reusing `simulator.rs`'s +//! dict conversions (`PORTING_NOTES.md` §6 Phase B4/B5, §11). + +use pyo3::prelude::*; +use pyo3::types::{PyAny, PyDict}; + +use malsim_core::dyna_simulator::DynaSimulator as CoreDynaSimulator; + +use crate::simulator::{ + build_reset_output, build_step_output, extract_actions, not_reset_err, parse_agents, + parse_settings, sim_err_to_py, +}; +use crate::state::{ + current_sim_state_to_py, no_state_err, recording_to_py, snapshot_to_py, terminated_to_py, + Episode, EpisodeSlot, +}; +use crate::{extract_shared_graph, extract_shared_model}; +use malsim_core::simulator::SimState; + +/// Python handle on a `malsim_core::dyna_simulator::DynaSimulator` +/// (dynamic instance model/attack graph). `model` must be the +/// `maltoolbox.Model` `graph` was built from; its state at construction is +/// the snapshot every reset restores (mirroring `DynaMalSimulator.__init__`). +#[pyclass(name = "DynaSimulator", module = "malsim._native", unsendable)] +pub struct DynaSimulator { + inner: CoreDynaSimulator, + /// The current episode's archive slot - see `state::EpisodeSlot`. + episode: EpisodeSlot, +} + +const CLASS_NAME: &str = "DynaSimulator"; + +#[pymethods] +impl DynaSimulator { + #[new] + fn new(graph: &Bound<'_, PyAny>, model: &Bound<'_, PyAny>) -> PyResult { + Ok(DynaSimulator { + inner: CoreDynaSimulator::new( + extract_shared_graph(graph)?, + extract_shared_model(model)?, + ), + episode: EpisodeSlot::new(), + }) + } + + /// Restores the shared `Model`/`AttackGraph` to the construction-time + /// snapshot - see `CoreDynaSimulator::restore_model`. Python's + /// `dyna_reset` calls this before flattening agent settings, so nodes + /// removed by the previous episode's model effects are back (with + /// their regenerated ids) when the rules are resolved. + fn restore_model_native(&mut self) -> PyResult<()> { + self.inner + .restore_model() + .map_err(|e| sim_err_to_py(e, CLASS_NAME)) + } + + /// Resets the simulator - see `CoreDynaSimulator::reset`. `agents` + /// must already be resolved against the restored graph + /// (`restore_model_native`). Same output shape as + /// `Simulator.reset_native`. + fn reset_native( + &mut self, + py: Python<'_>, + settings: &Bound<'_, PyDict>, + agents: &Bound<'_, PyDict>, + seed: u64, + ) -> PyResult> { + let graph_rc = self.inner.graph().clone(); + let settings = parse_settings(settings)?; + let agents = parse_agents(&graph_rc.borrow(), agents)?; + self.episode.end_episode(self.inner.state()); + let state = self + .inner + .reset(&settings, agents, seed) + .map_err(|e| sim_err_to_py(e, CLASS_NAME))?; + let graph = graph_rc.borrow(); + build_reset_output(py, &graph, state) + } + + /// The full current state (agents and `sim_state`), translated on + /// every call - see `state::snapshot_to_py`. + fn agent_states_native(&self, py: Python<'_>) -> PyResult> { + let state = self.live_state()?; + let graph = self.inner.graph().borrow(); + let iteration = state.history().iteration(); + snapshot_to_py( + py, + &graph, + state.snapshot_at(iteration), + state.history(), + iteration, + None, + ) + } + + /// The current `sim_state` part only - see `state::current_sim_state_to_py`. + fn sim_state_native(&self, py: Python<'_>) -> PyResult> { + let state = self.live_state()?; + current_sim_state_to_py(py, &self.inner.graph().borrow(), state) + } + + /// The episode's recording so far - see `state::recording_to_py`. + fn recording_native(&self, py: Python<'_>) -> PyResult> { + let state = self.live_state()?; + recording_to_py(py, &self.inner.graph().borrow(), state) + } + + /// Per agent name, whether that agent is terminated. + fn terminated_native(&self, py: Python<'_>) -> PyResult> { + terminated_to_py(py, self.live_state()?) + } + + /// A handle on the current episode, to read its earlier iterations + /// (also after the next reset) - see `state::Episode`. + fn episode_native(slf: &Bound<'_, Self>) -> PyResult { + let this = slf.borrow(); + this.live_state()?; + Ok(this + .episode + .handle(this.inner.graph().clone(), slf.clone().into_any().unbind())) + } + + /// Steps the simulation - see `CoreDynaSimulator::step`. Same output + /// shape as `Simulator.step_native`, plus `sim_state`'s + /// `step_modification_record` (and the full graph-state maps on a step + /// whose record is non-empty - see `build_step_output`). + fn step_native(&mut self, py: Python<'_>, actions: &Bound<'_, PyDict>) -> PyResult> { + let action_nodes = { + let state = self + .inner + .state() + .ok_or_else(|| not_reset_err(CLASS_NAME))?; + extract_actions(&self.inner.graph().borrow(), state, actions)? + }; + let outcome = self + .inner + .step(&action_nodes) + .map_err(|e| sim_err_to_py(e, CLASS_NAME))?; + let state = self + .inner + .state() + .ok_or_else(|| not_reset_err(CLASS_NAME))?; + build_step_output( + py, + &self.inner.graph().borrow(), + state, + &outcome.step, + Some(&outcome.step_modification_record), + ) + } +} + +impl DynaSimulator { + /// The live core state - an error before the first successful reset. + pub(crate) fn live_state(&self) -> PyResult<&SimState> { + self.inner.state().ok_or_else(|| no_state_err(CLASS_NAME)) + } +} diff --git a/py-bindings/malsim-pyo3/src/lib.rs b/py-bindings/malsim-pyo3/src/lib.rs new file mode 100644 index 00000000..fc007854 --- /dev/null +++ b/py-bindings/malsim-pyo3/src/lib.rs @@ -0,0 +1,174 @@ +//! PyO3 bindings from `malsim-core` to the Python package in `python/malsim`, +//! built as `malsim._native`. +//! +//! `node_count` below is a Phase A1 smoke-test function only (see +//! `PORTING_NOTES.md` §5/A1, §10): it proves that a `maltoolbox.AttackGraph` +//! Python object handed to this, a *separately compiled* extension module, +//! can still reach mal-toolbox's shared `Rc>` - not a +//! direct pyclass downcast (that doesn't work across independently-built +//! `cdylib`s, see §10), but via the `PyCapsule` mal-toolbox's +//! `PyAttackGraph::__inner_capsule__` hands out for exactly this purpose. +//! `model_asset_count` is the Phase B3 equivalent for `maltoolbox.Model` / +//! `PyModel::__inner_capsule__` (see `PORTING_NOTES.md` §6/B3). Neither is +//! meant as a real public API. + +use std::cell::RefCell; +use std::rc::Rc; + +use maltoolbox_attackgraph::AttackGraph; +use maltoolbox_model::Model; +use pyo3::prelude::*; +use pyo3::types::PyCapsule; + +mod dyna_simulator; +mod simulator; +mod state; + +/// Must match mal-toolbox's `py-bindings/maltoolbox-attackgraph-py/src/ +/// graph.rs`'s `INNER_CAPSULE_NAME` exactly - this string is the only +/// runtime check standing in for compile-time type safety across the +/// module boundary. +const INNER_CAPSULE_NAME: &std::ffi::CStr = c"maltoolbox._native.AttackGraph.inner"; + +/// Must match mal-toolbox's `py-bindings/maltoolbox-model-py/src/model.rs`'s +/// `INNER_CAPSULE_NAME` exactly - same role as `INNER_CAPSULE_NAME` above, +/// for `maltoolbox.Model` instead of `maltoolbox.AttackGraph` (Phase B3). +const MODEL_INNER_CAPSULE_NAME: &std::ffi::CStr = c"maltoolbox._native.Model.inner"; + +/// Extracts the shared `Rc>` handle from a Python +/// `maltoolbox.AttackGraph` object via its `__inner_capsule__()` method. +/// `pub(crate)` so `simulator.rs`'s `Simulator` pyclass (Phase A8) can +/// reuse it too, rather than re-implementing the same capsule handoff. +pub(crate) fn extract_shared_graph(graph: &Bound<'_, PyAny>) -> PyResult>> { + let capsule_obj = graph.call_method0("__inner_capsule__")?; + let capsule = capsule_obj.cast::()?; + let ptr = capsule.pointer_checked(Some(INNER_CAPSULE_NAME))?; + let typed_ptr = ptr.as_ptr() as *const RefCell; + // SAFETY: the capsule itself still owns the one strong reference + // `__inner_capsule__` parked via `Rc::into_raw` - its destructor will + // reclaim and drop *that* reference when the capsule is GC'd. We must + // not call `Rc::from_raw` directly on this pointer, since that would + // reclaim the capsule's own reference out from under it (a double + // free once the capsule's destructor also runs - confirmed by this + // crashing with `malloc(): unaligned tcache chunk detected` when + // tried). Instead, bump the strong count first to mint a brand new, + // independently-owned reference, then reconstruct an `Rc` from that. + unsafe { + Rc::increment_strong_count(typed_ptr); + Ok(Rc::from_raw(typed_ptr)) + } +} + +/// Reads the node count of a `maltoolbox.AttackGraph` through the shared +/// `Rc>` handle extracted above, without going through +/// any further Python-level attribute access. +#[pyfunction] +fn node_count(graph: &Bound<'_, PyAny>) -> PyResult { + let shared = extract_shared_graph(graph)?; + let count = shared.borrow().nodes.len(); + Ok(count) +} + +/// Extracts the shared `Rc>` handle from a Python +/// `maltoolbox.Model` object via its `__inner_capsule__()` method - Model +/// counterpart of `extract_shared_graph` above (Phase B3, PORTING_NOTES.md +/// §6/B3). `pub(crate)` for the same reason: future dyna native entry +/// points (B4) will need this handle too, not just this phase's smoke test. +pub(crate) fn extract_shared_model(model: &Bound<'_, PyAny>) -> PyResult>> { + let capsule_obj = model.call_method0("__inner_capsule__")?; + let capsule = capsule_obj.cast::()?; + let ptr = capsule.pointer_checked(Some(MODEL_INNER_CAPSULE_NAME))?; + let typed_ptr = ptr.as_ptr() as *const RefCell; + // SAFETY: same reasoning as `extract_shared_graph` above - the capsule + // still owns the one strong reference `__inner_capsule__` parked, so we + // bump the strong count and mint an independently-owned `Rc` rather + // than reclaiming the capsule's own reference directly. + unsafe { + Rc::increment_strong_count(typed_ptr); + Ok(Rc::from_raw(typed_ptr)) + } +} + +/// Reads the asset count of a `maltoolbox.Model` through the shared +/// `Rc>` handle extracted above, without going through any +/// further Python-level attribute access. Phase B3 smoke-test function, +/// same tier as `node_count` above - not a real public API. +#[pyfunction] +fn model_asset_count(model: &Bound<'_, PyAny>) -> PyResult { + let shared = extract_shared_model(model)?; + let count = shared.borrow().assets.len(); + Ok(count) +} + +/// Test-support-only utility (not a real public API, same tier as +/// `set_detector_rates` below): adds an asset directly through the shared +/// `Rc>` handle, bypassing `maltoolbox`'s Python-level +/// `Model.add_asset()` entirely - proves the *other* direction of Phase +/// B3's double-visibility requirement (a mutation made through the native +/// handle must be visible back on the Python side), which +/// `model_asset_count` alone (read-only) can't exercise. +#[pyfunction] +fn model_add_asset_native(model: &Bound<'_, PyAny>, asset_type: &str) -> PyResult { + let shared = extract_shared_model(model)?; + let mut model = shared.borrow_mut(); + model + .add_asset(asset_type, None, None, None, None, true) + .map_err(|e| pyo3::exceptions::PyValueError::new_err(e.to_string())) +} + +/// Test-support-only utility (not a real public API, same tier as +/// `node_count` above): directly mutates a detector's `tprate`/`fprate` +/// through the shared `Rc>`, bypassing +/// `maltoolbox`'s Python-level `node.detectors` binding entirely. +/// +/// Found necessary during Phase A9 (PORTING_NOTES.md §5/§10): `maltoolbox`'s +/// `AttackGraphNode.detectors` Python property is a *Python-side* cached +/// `dict` seeded once from the core's generation-time detector data - +/// `node.detectors['x'] = Detector(...)` mutates only that cache, never the +/// real `AttackGraphNode.detectors` field malsim-core's Rust hot path +/// reads, so tests that used this pattern to force deterministic +/// tprate/fprate for a run were silently exercising the *old* rates once +/// `collect_logs`/`collect_false_positives` moved to Rust. This function +/// mutates the real field directly instead. +#[pyfunction] +fn set_detector_rates( + graph: &Bound<'_, PyAny>, + node_id: i64, + label: &str, + tprate: Option, + fprate: Option, +) -> PyResult<()> { + let shared = extract_shared_graph(graph)?; + let mut graph = shared.borrow_mut(); + let key = *graph.id_to_node.get(&node_id).ok_or_else(|| { + pyo3::exceptions::PyValueError::new_err(format!( + "node id {node_id} is not part of this graph" + )) + })?; + let detector = graph + .nodes + .get_mut(key) + .expect("id_to_node only maps to live nodes") + .detectors + .get_mut(label) + .ok_or_else(|| { + pyo3::exceptions::PyValueError::new_err(format!( + "node {node_id} has no detector labeled \"{label}\"" + )) + })?; + detector.tprate = tprate; + detector.fprate = fprate; + Ok(()) +} + +#[pymodule] +fn _native(_py: Python<'_>, m: &Bound<'_, PyModule>) -> PyResult<()> { + m.add_function(wrap_pyfunction!(node_count, m)?)?; + m.add_function(wrap_pyfunction!(model_asset_count, m)?)?; + m.add_function(wrap_pyfunction!(model_add_asset_native, m)?)?; + m.add_function(wrap_pyfunction!(set_detector_rates, m)?)?; + m.add_class::()?; + m.add_class::()?; + m.add_class::()?; + Ok(()) +} diff --git a/py-bindings/malsim-pyo3/src/simulator.rs b/py-bindings/malsim-pyo3/src/simulator.rs new file mode 100644 index 00000000..6d6c0438 --- /dev/null +++ b/py-bindings/malsim-pyo3/src/simulator.rs @@ -0,0 +1,843 @@ +//! `malsim._native.Simulator` - the Phase A8 native simulator pyclass, +//! extended at Phase A9 to back `MalSimulator.reset()`/`.step()` (see +//! `PORTING_NOTES.md` §5 Phase A8/A9/§10) - plus the dict conversions it +//! shares with `dyna_simulator.rs`'s `DynaSimulator` pyclass (§11). +//! +//! A thin wrapper around `malsim_core::simulator::Simulator`, which owns +//! the reset/step orchestration: this module parses the flat Python dicts +//! into `MalSimulatorSettings`/`FlatAgentSettings`, calls the core +//! `Simulator`, and builds the plain-`dict` output from its `StepOutcome` +//! and `SimState`. +//! +//! Scope still deliberately smaller than the full Python +//! `MalSimulatorSettings`/`AttackerSettings`/`DefenderSettings` surface: +//! - No "multiple entry point sets, sampled at reset" support +//! (`AttackerSettings.entry_points` as a `tuple[Set, ...]`) - only a +//! single flat set of entry points per attacker. A9 resolves that +//! sampling in Python (reusing `attacker_state_factories.py:: +//! get_entry_points` unchanged) before calling `reset_native`, since the +//! sampling itself has no RNG-reproducibility stakes worth porting - see +//! PORTING_NOTES.md §10. +//! - No rewards (unchanged, pure Python, per §2.4 - not this module's job +//! regardless of phase). +//! +//! Per-agent TTC distribution overrides (`AttackerSettings.ttc_dists`) *are* +//! supported as of A9 - see `parse_ttc_dist_from_py`/ +//! `extract_optional_ttc_dist_overrides` below. +//! Every other hot-loop-relevant field (`ttc_mode`, both +//! `AttackSurfaceSettings` fields, both bernoulli toggles, +//! `compromise_entrypoints_at_start`, entry points/goals/actionable steps/ +//! observable steps/false positive and negative rates) is present, using +//! the same already-flattened id-keyed shapes (§2.4) A5-A7 established. +//! +//! All node references crossing the FFI boundary (in either direction) are +//! the stable, user-facing `AttackGraphNode.id: i64` - *not* the internal +//! `AttackGraphNodeId` slotmap key malsim-core's hot-path functions use +//! internally. `AttackGraph::id_to_node`/`graph.nodes[id].id` are the two +//! directions of that translation (see `to_node_id`/`stable_ids` below). + +use malsim_core::types::{NodeMap, NodeSet}; +use std::collections::HashMap; +use std::str::FromStr; + +use maltoolbox_attackgraph::{AttackGraph, AttackGraphNodeId}; +use pyo3::exceptions::PyValueError; +use pyo3::prelude::*; +use pyo3::types::{PyAny, PyDict}; + +use malsim_core::event_logger::LogEntry; +use malsim_core::graph_state::{GraphState, TtcMode}; +use malsim_core::model_effects::{AssetOp, AssetRef, AssocOp, ModEffectOp}; +use malsim_core::settings::{ + FlatAgentSettings, FlatAttackerSettings, FlatDefenderSettings, MalSimulatorSettings, +}; +use malsim_core::simulator::{SimState, Simulator as CoreSimulator, SimulatorError, StepOutcome}; +use malsim_core::ttc::{named_ttc_dist, DistFunction, Operation, TtcDist}; + +use crate::extract_shared_graph; +use crate::state::{ + current_sim_state_to_py, no_state_err, recording_to_py, snapshot_to_py, terminated_to_py, + Episode, EpisodeSlot, +}; + +pub(crate) fn to_py_err(e: E) -> PyErr { + PyValueError::new_err(e.to_string()) +} + +/// Maps a core `SimulatorError` to the Python exception these pyclasses +/// have always raised for that case: every error is a `ValueError` +/// carrying the wrapped error's message, except `NotReset`, which keeps a +/// `step_native`-worded message naming the pyclass (`class_name`). +pub(crate) fn sim_err_to_py(e: SimulatorError, class_name: &str) -> PyErr { + match e { + SimulatorError::NotReset => not_reset_err(class_name), + other => to_py_err(other), + } +} + +pub(crate) fn not_reset_err(class_name: &str) -> PyErr { + PyValueError::new_err(format!( + "{class_name}.step_native() called before reset_native()" + )) +} + +// --- Node id translation (stable i64 <-> AttackGraphNodeId) --- + +fn to_node_id(graph: &AttackGraph, stable_id: i64) -> PyResult { + graph.id_to_node.get(&stable_id).copied().ok_or_else(|| { + PyValueError::new_err(format!( + "node id {stable_id} is not part of this simulator's attack graph" + )) + }) +} + +/// Sorted so every id-set crossing the FFI boundary (`action_surface`, +/// `performed_nodes`, ...) has a deterministic order - `HashSet`'s default +/// hasher is seeded randomly per process, so an unsorted `Vec` collected +/// from one would give a *different* order on every run even for the +/// exact same seed and graph (found during Phase A9 - see +/// PORTING_NOTES.md §10: this silently broke run-to-run reproducibility, +/// not just parity with the old Python frozenset ordering). +/// An id in `ids` can be stale: the dyna path lets a step's own model +/// effect remove the very asset a node belongs to (including a node this +/// same step just compromised/enabled - `PORTING_NOTES.md` §0 B5), so by +/// the time output is serialized, `performed_nodes`/`action_surface`/etc. +/// can hold an id with no live node left to translate. Such an id is +/// silently dropped: the thing it refers to no longer exists in the +/// graph, so it has no stable id left to report - mirrors `collect_logs`'s +/// same-shaped fix in `malsim-core::event_logger`. +pub(crate) fn stable_ids( + graph: &AttackGraph, + ids: impl IntoIterator, +) -> Vec { + let mut ids: Vec = ids + .into_iter() + .filter_map(|id| graph.nodes.get(id).map(|n| n.id)) + .collect(); + ids.sort_unstable(); + ids +} + +pub(crate) fn id_value_map(graph: &AttackGraph, map: &NodeMap) -> HashMap { + map.iter() + .filter_map(|(&id, &v)| graph.nodes.get(id).map(|n| (n.id, v))) + .collect() +} + +// --- Input extraction helpers --- + +fn get_dict_value<'py>( + dict: &Bound<'py, PyDict>, + key: &str, +) -> PyResult>> { + Ok(match dict.get_item(key)? { + Some(v) if !v.is_none() => Some(v), + _ => None, + }) +} + +fn require_dict_value<'py>(dict: &Bound<'py, PyDict>, key: &str) -> PyResult> { + get_dict_value(dict, key)?.ok_or_else(|| { + PyValueError::new_err(format!("agent config missing required \"{key}\" field")) + }) +} + +fn extract_id_list( + graph: &AttackGraph, + obj: &Bound<'_, PyAny>, +) -> PyResult> { + let ids: Vec = obj.extract()?; + ids.into_iter().map(|id| to_node_id(graph, id)).collect() +} + +fn extract_id_set(graph: &AttackGraph, obj: &Bound<'_, PyAny>) -> PyResult { + Ok(extract_id_list(graph, obj)?.into_iter().collect()) +} + +fn extract_optional_id_set( + graph: &AttackGraph, + dict: &Bound<'_, PyDict>, + key: &str, +) -> PyResult> { + get_dict_value(dict, key)? + .map(|v| extract_id_set(graph, &v)) + .transpose() +} + +fn extract_optional_rate_map( + graph: &AttackGraph, + dict: &Bound<'_, PyDict>, + key: &str, +) -> PyResult>> { + let Some(v) = get_dict_value(dict, key)? else { + return Ok(None); + }; + let raw: HashMap = v.extract()?; + let mut out = NodeMap::with_capacity_and_hasher(raw.len(), Default::default()); + for (id, rate) in raw { + out.insert(to_node_id(graph, id)?, rate); + } + Ok(Some(out)) +} + +fn get_bool(dict: &Bound<'_, PyDict>, key: &str, default: bool) -> PyResult { + match get_dict_value(dict, key)? { + Some(v) => v.extract(), + None => Ok(default), + } +} + +/// Port of `TTCDist.from_dict`'s Python-dict parsing (§2.4: `ttc_dists` +/// overrides cross the FFI boundary as the same `to_dict()`/`from_dict()` +/// wire shape Python's `TTCDist` already uses - `{"name", "arguments"}` +/// for a leaf distribution, a named-distribution shortcut, or +/// `{"lhs", "rhs", "type"}` for a combined one) - built directly against +/// `ttc::TtcDist::new`/`with_combine`/`named_ttc_dist` rather than via +/// `serde_json::Value`, so this module doesn't need a direct `serde_json` +/// dependency (it's only transitively present via `malsim-core`). +fn parse_ttc_dist_from_py(dict: &Bound<'_, PyDict>) -> PyResult { + if let Some(name_obj) = get_dict_value(dict, "name")? { + let name: String = name_obj.extract()?; + if let Some(dist) = named_ttc_dist(&name) { + return Ok(dist); + } + let function = DistFunction::from_str(&name).map_err(|_| { + PyValueError::new_err(format!("unknown distribution function name \"{name}\"")) + })?; + let args: Vec = require_dict_value(dict, "arguments")?.extract()?; + return TtcDist::new(function, args).map_err(to_py_err); + } + + let lhs_dict = require_dict_value(dict, "lhs")?.cast::()?.clone(); + let rhs_dict = require_dict_value(dict, "rhs")?.cast::()?.clone(); + let op_str: String = require_dict_value(dict, "type")?.extract()?; + let op = Operation::from_str(&op_str).map_err(|_| { + PyValueError::new_err(format!("unknown ttc combine operation \"{op_str}\"")) + })?; + let lhs = parse_ttc_dist_from_py(&lhs_dict)?; + let rhs = parse_ttc_dist_from_py(&rhs_dict)?; + Ok(lhs.with_combine(rhs, op)) +} + +/// Parses the optional per-attacker `ttc_dists` override map +/// (`dict[node_id, ttc_dict]`, already flattened from +/// `AttackerSettings.ttc_dists: NodePropertyRule[TTCDist]` on the Python +/// side via `.per_node()` + `.to_dict()` - §2.4) into +/// `NodeMap`. +fn extract_optional_ttc_dist_overrides( + graph: &AttackGraph, + dict: &Bound<'_, PyDict>, + key: &str, +) -> PyResult>> { + let Some(v) = get_dict_value(dict, key)? else { + return Ok(None); + }; + let raw = v.cast::()?; + let mut out = NodeMap::with_capacity_and_hasher(raw.len(), Default::default()); + for (k, val) in raw.iter() { + let node_id: i64 = k.extract()?; + let dist_dict = val.cast::()?.clone(); + out.insert( + to_node_id(graph, node_id)?, + parse_ttc_dist_from_py(&dist_dict)?, + ); + } + Ok(Some(out)) +} + +fn parse_ttc_mode(s: &str) -> PyResult { + match s { + "DISABLED" => Ok(TtcMode::Disabled), + "EFFORT_BASED_PER_STEP_SAMPLE" => Ok(TtcMode::EffortBasedPerStepSample), + "PER_STEP_SAMPLE" => Ok(TtcMode::PerStepSample), + "PRE_SAMPLE" => Ok(TtcMode::PreSample), + "EXPECTED_VALUE" => Ok(TtcMode::ExpectedValue), + other => Err(PyValueError::new_err(format!( + "unknown ttc_mode \"{other}\"" + ))), + } +} + +/// Parses the flat settings dict (`native_settings.py::flatten_sim_settings`'s +/// output - no nested `attack_surface` key, `skip_compromised`/ +/// `skip_unnecessary` at the top level) into a `MalSimulatorSettings`. +/// Fields the dict doesn't carry (`seed`, `uncompromise_untraversable_steps`) +/// keep their `Default`. +pub(crate) fn parse_settings(dict: &Bound<'_, PyDict>) -> PyResult { + let ttc_mode_str = match get_dict_value(dict, "ttc_mode")? { + Some(v) => v.extract::()?, + None => "DISABLED".to_string(), + }; + let mut settings = MalSimulatorSettings { + ttc_mode: parse_ttc_mode(&ttc_mode_str)?, + ..MalSimulatorSettings::default() + }; + settings.run_defense_step_bernoullis = get_bool(dict, "run_defense_step_bernoullis", true)?; + settings.run_attack_step_bernoullis = get_bool(dict, "run_attack_step_bernoullis", true)?; + settings.attack_surface.skip_compromised = get_bool(dict, "skip_compromised", true)?; + settings.attack_surface.skip_unnecessary = get_bool(dict, "skip_unnecessary", false)?; + settings.compromise_entrypoints_at_start = + get_bool(dict, "compromise_entrypoints_at_start", true)?; + Ok(settings) +} + +/// Parses one attacker's flat dict (`native_settings.py:: +/// flatten_attacker_settings`'s output) into `FlatAttackerSettings`. +fn parse_attacker(graph: &AttackGraph, cfg: &Bound<'_, PyDict>) -> PyResult { + let entry_points = extract_id_set(graph, &require_dict_value(cfg, "entry_points")?)?; + let goals = extract_optional_id_set(graph, cfg, "goals")?.unwrap_or_default(); + let actionable_steps = extract_optional_id_set(graph, cfg, "actionable_steps")?; + let ttc_dists = extract_optional_ttc_dist_overrides(graph, cfg, "ttc_dists")?; + Ok(FlatAttackerSettings { + entry_points, + goals, + actionable_steps, + ttc_dists, + }) +} + +/// Parses one defender's flat dict (`native_settings.py:: +/// flatten_defender_settings`'s output) into `FlatDefenderSettings`. +fn parse_defender(graph: &AttackGraph, cfg: &Bound<'_, PyDict>) -> PyResult { + let actionable_steps = extract_optional_id_set(graph, cfg, "actionable_steps")?; + let observable_steps = extract_optional_id_set(graph, cfg, "observable_steps")?; + let false_positive_rates = extract_optional_rate_map(graph, cfg, "false_positive_rates")?; + let false_negative_rates = extract_optional_rate_map(graph, cfg, "false_negative_rates")?; + Ok(FlatDefenderSettings { + actionable_steps, + observable_steps, + false_positive_rates, + false_negative_rates, + }) +} + +/// Parses the `agents` dict (`name -> flat per-agent dict`) into the +/// `(name, FlatAgentSettings)` list `CoreSimulator::reset` takes. Every +/// agent's `type` is checked first (in dict order), then attackers are +/// parsed, then defenders - the same order these errors surfaced in before +/// the orchestration moved to malsim-core. +pub(crate) fn parse_agents( + graph: &AttackGraph, + agents: &Bound<'_, PyDict>, +) -> PyResult> { + let mut attacker_cfgs = Vec::new(); + let mut defender_cfgs = Vec::new(); + for (name_obj, cfg_obj) in agents.iter() { + let name: String = name_obj.extract()?; + let cfg = cfg_obj.cast::()?.clone(); + let kind: String = require_dict_value(&cfg, "type")?.extract()?; + match kind.as_str() { + "attacker" => attacker_cfgs.push((name, cfg)), + "defender" => defender_cfgs.push((name, cfg)), + other => { + return Err(PyValueError::new_err(format!( + "agent \"{name}\" has unknown type \"{other}\" (expected \"attacker\" or \"defender\")" + ))) + } + } + } + + let mut out = Vec::with_capacity(attacker_cfgs.len() + defender_cfgs.len()); + for (name, cfg) in attacker_cfgs { + let parsed = parse_attacker(graph, &cfg)?; + out.push((name, FlatAgentSettings::Attacker(parsed))); + } + for (name, cfg) in defender_cfgs { + let parsed = parse_defender(graph, &cfg)?; + out.push((name, FlatAgentSettings::Defender(parsed))); + } + Ok(out) +} + +/// Python handle on a `malsim_core::simulator::Simulator` (static instance +/// model/attack graph). The dynamic counterpart is `dyna_simulator.rs`'s +/// `DynaSimulator`. +#[pyclass(name = "Simulator", module = "malsim._native", unsendable)] +pub struct Simulator { + inner: CoreSimulator, + /// The current episode's archive slot - see `state::EpisodeSlot`. + episode: EpisodeSlot, +} + +const CLASS_NAME: &str = "Simulator"; + +#[pymethods] +impl Simulator { + #[new] + fn new(graph: &Bound<'_, PyAny>) -> PyResult { + Ok(Simulator { + inner: CoreSimulator::new(extract_shared_graph(graph)?), + episode: EpisodeSlot::new(), + }) + } + + /// Resets the simulator - see `CoreSimulator::reset`. + fn reset_native( + &mut self, + py: Python<'_>, + settings: &Bound<'_, PyDict>, + agents: &Bound<'_, PyDict>, + seed: u64, + ) -> PyResult> { + let graph_rc = self.inner.graph().clone(); + let settings = parse_settings(settings)?; + let agents = parse_agents(&graph_rc.borrow(), agents)?; + self.episode.end_episode(self.inner.state()); + let state = self + .inner + .reset(&settings, agents, seed) + .map_err(|e| sim_err_to_py(e, CLASS_NAME))?; + let graph = graph_rc.borrow(); + build_reset_output(py, &graph, state) + } + + /// The full current state (agents and `sim_state`), translated on + /// every call - see `state::snapshot_to_py`. + fn agent_states_native(&self, py: Python<'_>) -> PyResult> { + let state = self.live_state()?; + let graph = self.inner.graph().borrow(); + let iteration = state.history().iteration(); + snapshot_to_py( + py, + &graph, + state.snapshot_at(iteration), + state.history(), + iteration, + None, + ) + } + + /// The current `sim_state` part only - see `state::current_sim_state_to_py`. + fn sim_state_native(&self, py: Python<'_>) -> PyResult> { + let state = self.live_state()?; + current_sim_state_to_py(py, &self.inner.graph().borrow(), state) + } + + /// The episode's recording so far - see `state::recording_to_py`. + fn recording_native(&self, py: Python<'_>) -> PyResult> { + let state = self.live_state()?; + recording_to_py(py, &self.inner.graph().borrow(), state) + } + + /// Per agent name, whether that agent is terminated. + fn terminated_native(&self, py: Python<'_>) -> PyResult> { + terminated_to_py(py, self.live_state()?) + } + + /// A handle on the current episode, to read its earlier iterations + /// (also after the next reset) - see `state::Episode`. + fn episode_native(slf: &Bound<'_, Self>) -> PyResult { + let this = slf.borrow(); + this.live_state()?; + Ok(this + .episode + .handle(this.inner.graph().clone(), slf.clone().into_any().unbind())) + } + + /// Steps the simulation - see `CoreSimulator::step`. + fn step_native(&mut self, py: Python<'_>, actions: &Bound<'_, PyDict>) -> PyResult> { + let action_nodes = { + let state = self + .inner + .state() + .ok_or_else(|| not_reset_err(CLASS_NAME))?; + extract_actions(&self.inner.graph().borrow(), state, actions)? + }; + let outcome = self + .inner + .step(&action_nodes) + .map_err(|e| sim_err_to_py(e, CLASS_NAME))?; + let state = self + .inner + .state() + .ok_or_else(|| not_reset_err(CLASS_NAME))?; + build_step_output(py, &self.inner.graph().borrow(), state, &outcome, None) + } +} + +impl Simulator { + /// The live core state - an error before the first successful reset. + pub(crate) fn live_state(&self) -> PyResult<&SimState> { + self.inner.state().ok_or_else(|| no_state_err(CLASS_NAME)) + } +} + +/// Translates `step_native`'s `actions` dict (`name -> [stable id]`) into +/// the core `step`'s input. Mirrors `_pre_step_check`'s `KeyError` on an +/// `actions` key that names no registered agent (checked in key order, +/// before any node id is parsed); an agent missing from `actions` (or +/// mapped to `None`) takes no action. +pub(crate) fn extract_actions( + graph: &AttackGraph, + state: &SimState, + actions: &Bound<'_, PyDict>, +) -> PyResult>> { + for key_obj in actions.keys() { + let name: String = key_obj.extract()?; + if !state.attackers.contains_key(&name) && !state.defenders.contains_key(&name) { + return Err(to_py_err(SimulatorError::UnknownAgent(name))); + } + } + + let mut action_nodes: HashMap> = HashMap::new(); + for name in state.attackers.keys().chain(state.defenders.keys()) { + let nodes = match actions.get_item(name)? { + Some(v) if !v.is_none() => extract_id_list(graph, &v)?, + _ => Vec::new(), + }; + action_nodes.insert(name.clone(), nodes); + } + Ok(action_nodes) +} + +/// Writes `ttc_values`/`impossible_attack_steps`/`necessity_per_node`/ +/// `pre_enabled_defenses` into `sim_state` from `graph_state` - the full +/// episode-accumulated `GraphState`, not a delta. Shared by +/// `build_reset_output` (always) and `build_step_output` (only on a dyna +/// step that actually ran a model effect - see that function's doc +/// comment for why these can't be a delta against the previous step). +pub(crate) fn insert_graph_state_fields( + sim_state: &Bound<'_, PyDict>, + graph: &AttackGraph, + graph_state: &GraphState, +) -> PyResult<()> { + sim_state.set_item("ttc_values", id_value_map(graph, &graph_state.ttc_values))?; + sim_state.set_item( + "impossible_attack_steps", + stable_ids(graph, graph_state.impossible_attack_steps.iter().copied()), + )?; + sim_state.set_item( + "necessity_per_node", + graph_state + .necessity_per_node + .iter() + .filter_map(|(id, &v)| graph.nodes.get(id).map(|n| (n.id, v))) + .collect::>(), + )?; + sim_state.set_item( + "pre_enabled_defenses", + stable_ids(graph, graph_state.pre_enabled_defenses.iter().copied()), + )?; + Ok(()) +} + +/// Builds `reset_native`'s plain-`dict` return value - full +/// episode-initial state for every field, since there's no previous +/// step to delta against at reset. See module docs for the exact +/// shape. Deliberately no custom pyclasses in the return value (per +/// A8's own plan text: "keep it boring and inspectable"). Shared by +/// `Simulator`/`DynaSimulator`'s `reset_native`. +pub(crate) fn build_reset_output( + py: Python<'_>, + graph: &AttackGraph, + state: &SimState, +) -> PyResult> { + let sim_state = PyDict::new(py); + sim_state.set_item( + "enabled_defenses", + stable_ids(graph, state.enabled_defenses.iter().copied()), + )?; + insert_graph_state_fields(&sim_state, graph, &state.graph_state)?; + + let defenders_terminated = state.defender_is_terminated(); + + let agents = PyDict::new(py); + for (name, a) in &state.attackers { + let d = PyDict::new(py); + d.set_item("type", "attacker")?; + d.set_item( + "performed_nodes", + stable_ids(graph, a.performed_nodes.iter().copied()), + )?; + d.set_item( + "attempted_nodes", + stable_ids(graph, a.attempted_nodes.iter().copied()), + )?; + d.set_item( + "action_surface", + stable_ids(graph, a.action_surface.iter().copied()), + )?; + d.set_item("num_attempts", id_value_map(graph, &a.num_attempts))?; + d.set_item("ttc_values", id_value_map(graph, &a.ttc_values))?; + d.set_item( + "impossible_steps", + stable_ids(graph, a.impossible_steps.iter().copied()), + )?; + d.set_item("iteration", a.iteration)?; + d.set_item("terminated", state.attacker_is_terminated(name))?; + agents.set_item(name, d)?; + } + + for (name, de) in &state.defenders { + let d = PyDict::new(py); + d.set_item("type", "defender")?; + d.set_item( + "performed_nodes", + stable_ids(graph, de.performed_nodes.iter().copied()), + )?; + d.set_item( + "compromised_nodes", + stable_ids(graph, de.compromised_nodes.iter().copied()), + )?; + d.set_item( + "observed_nodes", + stable_ids(graph, de.observed_nodes.iter().copied()), + )?; + d.set_item( + "action_surface", + stable_ids(graph, de.action_surface.iter().copied()), + )?; + d.set_item("iteration", de.iteration)?; + d.set_item("terminated", defenders_terminated)?; + + let logs: Vec> = de + .logs + .iter() + .map(|log| log_entry_to_py(py, graph, log)) + .collect::>()?; + d.set_item("logs", logs)?; + + agents.set_item(name, d)?; + } + + let out = PyDict::new(py); + out.set_item("sim_state", sim_state)?; + out.set_item("agents", agents)?; + Ok(out.into()) +} + +/// Builds `step_native`'s plain-`dict` return value - unlike +/// `build_reset_output`, every monotonically-growing field is this +/// step's *delta* only (`step_*` keys - see module docs' wire-format +/// table), not the full episode-accumulated value: the accumulated +/// values still live in the core `SimState` for Rust's own internal use +/// (`get_attack_surface`, `*_is_terminated`, ...), they just no longer +/// cross the FFI boundary redundantly on every call. Episode-static +/// fields (`ttc_values`, `necessity_per_node`, +/// `impossible_attack_steps`, `pre_enabled_defenses`) are dropped +/// entirely for a plain `step_native` call (`step_modification_record` +/// is always empty there) - Python caches them from `reset_native`'s +/// output, same as before. +/// +/// `modification_record` is `Some` only for `DynaSimulator.step_native` +/// (the static `Simulator` has no model effects, so its output has no +/// `step_modification_record` key at all). +/// +/// **Not episode-static for `DynaSimulator.step_native`, though** (PORTING_NOTES.md +/// §6 Phase B5's TTC-gap fix): `dyna_attacker_step`/`dyna_defender_step` +/// grow `state.graph_state` in place via `fold_new_nodes_into_graph_state` +/// whenever a model effect creates nodes mid-episode, and +/// `necessity_per_node` is a full-graph recompute each time (not just new +/// keys) - so a true per-field delta isn't provably correct. Whenever +/// `step_modification_record` is non-empty (a model effect genuinely ran +/// this step), this resends the full current maps via +/// `insert_graph_state_fields`, same shape as `build_reset_output`; +/// Python rebuilds its `GraphState` wholesale from them that step only. +/// `action_surface`, `iteration` and `terminated` are unchanged (full +/// current value every step), matching `build_reset_output`. +pub(crate) fn build_step_output( + py: Python<'_>, + graph: &AttackGraph, + state: &SimState, + outcome: &StepOutcome, + modification_record: Option<&[ModEffectOp]>, +) -> PyResult> { + let step_enabled_defenses = &outcome.step_enabled_defenses; + // Every node any attacker compromised this step - what each + // defender's `compromised_nodes` grew by. + let step_compromised_nodes: NodeSet = outcome + .attackers + .values() + .flat_map(|a| a.step_performed_nodes.iter().copied()) + .collect(); + + let sim_state = PyDict::new(py); + sim_state.set_item( + "step_enabled_defenses", + stable_ids(graph, step_enabled_defenses.iter().copied()), + )?; + // Dyna only (§6 Phase B4, §11): this step's model-effect + // modification record, as plain dicts (`{"kind": "asset"/"assoc", + // "type": "ADDITIVE"/"SUBTRACTIVE", ...}`) - Python resolves these + // back into `AssetOp`/`AssocOp` objects and appends them onto + // `DynaMalSimulatorState.modification_record` (delta-only, same + // wire-format discipline A10 established for `logs`/etc. - see + // module docs). + if let Some(record) = modification_record { + let record_py: Vec> = record + .iter() + .map(|op| mod_effect_op_to_py(py, op)) + .collect::>()?; + sim_state.set_item("step_modification_record", record_py)?; + if !record.is_empty() { + insert_graph_state_fields(&sim_state, graph, &state.graph_state)?; + } + } + + let defenders_terminated = state.defender_is_terminated(); + + let agents = PyDict::new(py); + for (name, a) in &state.attackers { + let delta = &outcome.attackers[name]; + let (step_performed_nodes, step_attempted_nodes) = + (&delta.step_performed_nodes, &delta.step_attempted_nodes); + let d = PyDict::new(py); + d.set_item("type", "attacker")?; + d.set_item( + "step_performed_nodes", + stable_ids(graph, step_performed_nodes.iter().copied()), + )?; + d.set_item( + "step_attempted_nodes", + stable_ids(graph, step_attempted_nodes.iter().copied()), + )?; + d.set_item( + "action_surface", + stable_ids(graph, a.action_surface.iter().copied()), + )?; + d.set_item("iteration", a.iteration)?; + d.set_item("terminated", state.attacker_is_terminated(name))?; + agents.set_item(name, d)?; + } + + for (name, de) in &state.defenders { + let delta = &outcome.defenders[name]; + let (step_observed_nodes, step_logs) = (&delta.step_observed_nodes, &delta.step_logs); + let d = PyDict::new(py); + d.set_item("type", "defender")?; + d.set_item( + "step_performed_nodes", + stable_ids(graph, step_enabled_defenses.iter().copied()), + )?; + d.set_item( + "step_compromised_nodes", + stable_ids(graph, step_compromised_nodes.iter().copied()), + )?; + d.set_item( + "step_observed_nodes", + stable_ids(graph, step_observed_nodes.iter().copied()), + )?; + d.set_item( + "action_surface", + stable_ids(graph, de.action_surface.iter().copied()), + )?; + d.set_item("iteration", de.iteration)?; + d.set_item("terminated", defenders_terminated)?; + + let logs: Vec> = step_logs + .iter() + .map(|log| log_entry_to_py(py, graph, log)) + .collect::>()?; + d.set_item("step_logs", logs)?; + + agents.set_item(name, d)?; + } + + let out = PyDict::new(py); + out.set_item("sim_state", sim_state)?; + out.set_item("agents", agents)?; + Ok(out.into()) +} + +/// Writes one `AssetRef`'s `id`/`asset_type`/`name` into `d` under +/// `prefix`-qualified keys (e.g. `prefix = "asset"` -> `asset_id`/ +/// `asset_type`/`asset_name`) - shared by every `mod_effect_op_to_py` +/// branch below. +fn set_asset_ref(d: &Bound<'_, PyDict>, prefix: &str, asset: &AssetRef) -> PyResult<()> { + d.set_item(format!("{prefix}_id"), asset.id)?; + d.set_item(format!("{prefix}_type"), &asset.asset_type)?; + d.set_item(format!("{prefix}_name"), &asset.name)?; + Ok(()) +} + +/// Converts one `ModEffectOp` (Phase B1/B5) into the plain dict shape +/// that `DynaSimulator.step_native`'s `step_modification_record` crosses the FFI +/// boundary with. Every asset reference carries its `AssetRef` snapshot +/// (id + type + name) rather than a bare id - per `AssetRef`'s own doc +/// comment, the asset an op describes may already be gone from the live +/// `Model` by the time Python reads this record (e.g. a later op in the +/// same record removed it, or it's itself a removal), so Python must +/// never need to resolve these ids back through `model.assets` - see +/// `PORTING_NOTES.md` §6 Phase B5's differences-log entry. +pub(crate) fn mod_effect_op_to_py(py: Python<'_>, op: &ModEffectOp) -> PyResult> { + let d = PyDict::new(py); + match op { + ModEffectOp::Asset(AssetOp::Added { asset }) => { + d.set_item("kind", "asset")?; + d.set_item("type", "ADDITIVE")?; + set_asset_ref(&d, "asset", asset)?; + } + ModEffectOp::Asset(AssetOp::Removed { asset, .. }) => { + d.set_item("kind", "asset")?; + d.set_item("type", "SUBTRACTIVE")?; + set_asset_ref(&d, "asset", asset)?; + } + ModEffectOp::Assoc(AssocOp::Added { + left, + field_name, + right, + }) => { + d.set_item("kind", "assoc")?; + d.set_item("type", "ADDITIVE")?; + set_asset_ref(&d, "left_asset", left)?; + d.set_item("field_name", field_name)?; + set_asset_ref(&d, "right_asset", right)?; + } + ModEffectOp::Assoc(AssocOp::Removed { + left, + field_name, + right, + }) => { + d.set_item("kind", "assoc")?; + d.set_item("type", "SUBTRACTIVE")?; + set_asset_ref(&d, "left_asset", left)?; + d.set_item("field_name", field_name)?; + set_asset_ref(&d, "right_asset", right)?; + } + } + Ok(d.into()) +} + +fn stable_id_of(graph: &AttackGraph, id: AttackGraphNodeId) -> PyResult { + graph + .nodes + .get(id) + .map(|n| n.id) + .ok_or_else(|| PyValueError::new_err(format!("node {id:?} is no longer in the graph"))) +} + +pub(crate) fn log_entry_to_py( + py: Python<'_>, + graph: &AttackGraph, + log: &LogEntry, +) -> PyResult> { + let d = PyDict::new(py); + d.set_item("timestep", log.timestep)?; + let (detector_node_id, detector_label) = &log.detector_id; + // `detector_node_id`/`trigger` are expected to always be live here: + // `collect_logs` already only ever constructs a `LogEntry` from a node + // it just confirmed live, and nothing mutates the graph between there + // and here - these `stable_id_of` calls should never actually hit + // their error arm. Guarded anyway (rather than a direct `graph. + // nodes[...]` index) so a future change that violates that invariant + // surfaces as a catchable Python exception, not an uncatchable Rust + // panic across the FFI boundary - the exact failure mode + // `PORTING_NOTES.md` §0 B5 is about. + d.set_item("detector_node_id", stable_id_of(graph, *detector_node_id)?)?; + d.set_item("detector_label", detector_label)?; + d.set_item("trigger", stable_id_of(graph, log.trigger)?)?; + // Unlike `detector_node_id`/`trigger`, a context node id *can* + // legitimately go stale: it's picked from `previous_compromised_nodes` + // (history accumulated across the whole episode), so an id here may + // name a node some earlier, unrelated step's model effect already + // removed. Drop just that label rather than failing the whole log + // entry - mirrors `collect_logs`'s "nothing left to report" choice. + let context: HashMap = log + .context + .iter() + .filter_map(|(label, &id)| graph.nodes.get(id).map(|n| (label.clone(), n.id))) + .collect(); + d.set_item("context", context)?; + d.set_item("false_positive", log.false_positive)?; + Ok(d.into()) +} diff --git a/py-bindings/malsim-pyo3/src/state.rs b/py-bindings/malsim-pyo3/src/state.rs new file mode 100644 index 00000000..7964772b --- /dev/null +++ b/py-bindings/malsim-pyo3/src/state.rs @@ -0,0 +1,333 @@ +//! Translation of the simulator state into plain Python data, read on +//! demand by `MalSimulator`/`DynaMalSimulator`'s `agent_states`/ +//! `sim_state`/`recording` attributes (`PORTING_NOTES.md` §14): the Python +//! simulators keep no state of their own, so every access lands here. +//! +//! Also `Episode`, the handle a lazily-read `previous_state` resolves +//! through: it reads the live simulator while its episode is current, and +//! an archived copy of the episode once `reset` has started a new one. + +use std::cell::RefCell; +use std::rc::Rc; + +use maltoolbox_attackgraph::{AttackGraph, AttackGraphNodeId}; +use pyo3::exceptions::PyValueError; +use pyo3::prelude::*; +use pyo3::types::{PyAny, PyDict}; + +use malsim_core::history::{ + AttackerSnapshot, DefenderSnapshot, EpisodeHistory, EpisodeRecord, StateSnapshot, +}; +use malsim_core::model_effects::ModEffectOp; +use malsim_core::simulator::SimState; +use malsim_core::types::NodeSet; + +use crate::dyna_simulator::DynaSimulator; +use crate::simulator::{ + id_value_map, insert_graph_state_fields, log_entry_to_py, mod_effect_op_to_py, stable_ids, + Simulator, +}; + +/// Like `stable_ids`, but keeps the given order (and drops dead ids). +fn ordered_stable_ids( + graph: &AttackGraph, + ids: impl IntoIterator, +) -> Vec { + ids.into_iter() + .filter_map(|id| graph.nodes.get(id).map(|n| n.id)) + .collect() +} + +fn performed_nodes_order_to_py<'py>( + py: Python<'py>, + graph: &AttackGraph, + order: &std::collections::BTreeMap, +) -> PyResult> { + let d = PyDict::new(py); + for (iteration, nodes) in order { + d.set_item(iteration, stable_ids(graph, nodes.iter().copied()))?; + } + Ok(d) +} + +fn modification_record_to_py(py: Python<'_>, record: &[ModEffectOp]) -> PyResult>> { + record + .iter() + .map(|op| mod_effect_op_to_py(py, op)) + .collect() +} + +/// The `sim_state` part of the wire format: the full graph state, +/// `enabled_defenses` and the modification record so far (always empty on +/// the static path). +fn sim_state_to_py<'py>( + py: Python<'py>, + graph: &AttackGraph, + graph_state: &malsim_core::graph_state::GraphState, + enabled_defenses: &NodeSet, + modification_record: &[ModEffectOp], +) -> PyResult> { + let d = PyDict::new(py); + d.set_item( + "enabled_defenses", + stable_ids(graph, enabled_defenses.iter().copied()), + )?; + insert_graph_state_fields(&d, graph, graph_state)?; + d.set_item( + "modification_record", + modification_record_to_py(py, modification_record)?, + )?; + Ok(d) +} + +fn attacker_to_py<'py>( + py: Python<'py>, + graph: &AttackGraph, + a: &AttackerSnapshot, +) -> PyResult> { + let d = PyDict::new(py); + d.set_item("type", "attacker")?; + d.set_item( + "entry_points", + stable_ids(graph, a.entry_points.iter().copied()), + )?; + d.set_item( + "performed_nodes", + stable_ids(graph, a.performed_nodes.iter().copied()), + )?; + d.set_item( + "attempted_nodes", + stable_ids(graph, a.attempted_nodes.iter().copied()), + )?; + d.set_item( + "action_surface", + stable_ids(graph, a.action_surface.iter().copied()), + )?; + d.set_item("num_attempts", id_value_map(graph, &a.num_attempts))?; + d.set_item("ttc_values", id_value_map(graph, &a.ttc_values))?; + d.set_item( + "impossible_steps", + stable_ids(graph, a.impossible_steps.iter().copied()), + )?; + d.set_item( + "performed_nodes_order", + performed_nodes_order_to_py(py, graph, &a.performed_nodes_order)?, + )?; + d.set_item("iteration", a.iteration)?; + Ok(d) +} + +fn defender_to_py<'py>( + py: Python<'py>, + graph: &AttackGraph, + de: &DefenderSnapshot, +) -> PyResult> { + let d = PyDict::new(py); + d.set_item("type", "defender")?; + d.set_item( + "performed_nodes", + stable_ids(graph, de.performed_nodes.iter().copied()), + )?; + d.set_item( + "compromised_nodes", + stable_ids(graph, de.compromised_nodes.iter().copied()), + )?; + d.set_item( + "observed_nodes", + stable_ids(graph, de.observed_nodes.iter().copied()), + )?; + d.set_item( + "action_surface", + stable_ids(graph, de.action_surface.iter().copied()), + )?; + let logs: Vec> = de + .logs + .iter() + .map(|log| log_entry_to_py(py, graph, log)) + .collect::>()?; + d.set_item("logs", logs)?; + d.set_item( + "performed_nodes_order", + performed_nodes_order_to_py(py, graph, &de.performed_nodes_order)?, + )?; + d.set_item("iteration", de.iteration)?; + Ok(d) +} + +/// The full state at `iteration` as plain data: `{"iteration", "sim_state", +/// "agents": {name: {...}}}`, all nodes as stable ids. With `agent`, only +/// that agent is translated. +pub(crate) fn snapshot_to_py( + py: Python<'_>, + graph: &AttackGraph, + snapshot: Option, + history: &EpisodeHistory, + iteration: u64, + agent: Option<&str>, +) -> PyResult> { + let snapshot: StateSnapshot = snapshot.ok_or_else(|| { + PyValueError::new_err(format!( + "iteration {iteration} is after the episode's current iteration {}", + history.iteration() + )) + })?; + let agents = PyDict::new(py); + for (name, a) in &snapshot.attackers { + if agent.is_none_or(|n| n == name) { + agents.set_item(name, attacker_to_py(py, graph, a)?)?; + } + } + for (name, de) in &snapshot.defenders { + if agent.is_none_or(|n| n == name) { + agents.set_item(name, defender_to_py(py, graph, de)?)?; + } + } + let out = PyDict::new(py); + out.set_item("iteration", snapshot.iteration)?; + out.set_item( + "sim_state", + sim_state_to_py( + py, + graph, + &snapshot.graph_state, + &snapshot.enabled_defenses, + &snapshot.modification_record, + )?, + )?; + out.set_item("agents", agents)?; + Ok(out.into()) +} + +/// The current `sim_state` only (no agents) - see `sim_state_to_py`. +pub(crate) fn current_sim_state_to_py( + py: Python<'_>, + graph: &AttackGraph, + state: &SimState, +) -> PyResult> { + Ok(sim_state_to_py( + py, + graph, + &state.graph_state, + &state.enabled_defenses, + state.history().modification_record(), + )? + .into()) +} + +/// The whole episode's recording: `{iteration: {agent: [stable id, ...]}}` +/// for every step so far (1-based iterations, as `MalSimulator.recording` +/// keys them), each agent's nodes requested-actions-first. +pub(crate) fn recording_to_py( + py: Python<'_>, + graph: &AttackGraph, + state: &SimState, +) -> PyResult> { + let out = PyDict::new(py); + let history = state.history(); + for iteration in 1..=history.iteration() { + let Some(entry) = history.recording(iteration) else { + continue; + }; + let per_agent = PyDict::new(py); + for (name, nodes) in entry { + per_agent.set_item(name, ordered_stable_ids(graph, nodes.iter().copied()))?; + } + out.set_item(iteration, per_agent)?; + } + Ok(out.into()) +} + +/// The error a state read raises before the first successful reset. +pub(crate) fn no_state_err(class_name: &str) -> PyErr { + PyValueError::new_err(format!("{class_name} state read before reset_native()")) +} + +/// Per agent, whether it is terminated (`attacker_is_terminated`/ +/// `defender_is_terminated`). +pub(crate) fn terminated_to_py(py: Python<'_>, state: &SimState) -> PyResult> { + let out = PyDict::new(py); + for name in state.attackers.keys() { + out.set_item(name, state.attacker_is_terminated(name) == Some(true))?; + } + let defenders_terminated = state.defender_is_terminated(); + for name in state.defenders.keys() { + out.set_item(name, defenders_terminated)?; + } + Ok(out.into()) +} + +/// The archive slot of the simulator's current episode, owned by the +/// `Simulator`/`DynaSimulator` pyclass and shared with every `Episode` +/// handle given out for that episode. +pub(crate) struct EpisodeSlot { + archive: Rc>>, +} + +impl EpisodeSlot { + pub(crate) fn new() -> Self { + EpisodeSlot { + archive: Rc::new(RefCell::new(None)), + } + } + + /// Called right before a reset replaces `current`: if any `Episode` + /// handle of the ending episode is still alive, archive a copy of it + /// for them; then start a fresh slot for the next episode. + pub(crate) fn end_episode(&mut self, current: Option<&SimState>) { + if Rc::strong_count(&self.archive) > 1 { + if let Some(state) = current { + *self.archive.borrow_mut() = Some(state.episode_record()); + } + } + self.archive = Rc::new(RefCell::new(None)); + } + + pub(crate) fn handle(&self, graph: Rc>, sim: Py) -> Episode { + Episode { + archive: self.archive.clone(), + graph, + sim, + } + } +} + +/// A handle on one episode of a simulator, to read its state at an +/// iteration of that episode (`snapshot_native`). Python's lazy +/// `previous_state` keeps one. +#[pyclass(name = "Episode", module = "malsim._native", unsendable)] +pub struct Episode { + archive: Rc>>, + graph: Rc>, + sim: Py, +} + +#[pymethods] +impl Episode { + /// The state at `iteration` of this episode (0 is right after reset), + /// same shape as `Simulator.agent_states_native`. With `agent`, only + /// that agent is included. + #[pyo3(signature = (iteration, agent=None))] + fn snapshot_native( + &self, + py: Python<'_>, + iteration: u64, + agent: Option<&str>, + ) -> PyResult> { + let graph = self.graph.borrow(); + if let Some(record) = self.archive.borrow().as_ref() { + let snapshot = record.snapshot_at(iteration); + return snapshot_to_py(py, &graph, snapshot, record.history(), iteration, agent); + } + let sim = self.sim.bind(py); + if let Ok(sim) = sim.cast::() { + let sim = sim.borrow(); + let state = sim.live_state()?; + let snapshot = state.snapshot_at(iteration); + return snapshot_to_py(py, &graph, snapshot, state.history(), iteration, agent); + } + let sim = sim.cast::()?.borrow(); + let state = sim.live_state()?; + let snapshot = state.snapshot_at(iteration); + snapshot_to_py(py, &graph, snapshot, state.history(), iteration, agent) + } +} diff --git a/pyproject.toml b/pyproject.toml index 49f67dfb..7617028e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,7 +12,7 @@ description = "A MAL compliant simulator." readme = "README.md" requires-python = ">=3.10" dependencies = [ - "mal-toolbox>=2.11.0", + "mal-toolbox @ git+https://github.com/mal-lang/mal-toolbox.git@rust-rewrite", "PyYAML>=6.0.1", "numpy>=1.21.4", "scipy", @@ -54,14 +54,17 @@ dev = [ malsim = "malsim.__main__:main" [build-system] -requires = ["setuptools>=61.0"] -build-backend = "setuptools.build_meta" +requires = ["maturin>=1,<2"] +build-backend = "maturin" -[tool.setuptools.packages.find] -include = ["malsim*"] +[tool.maturin] +manifest-path = "py-bindings/malsim-pyo3/Cargo.toml" +module-name = "malsim._native" +python-source = "python" +include = ["malsim/py.typed", "malsim/_native.pyi"] [tool.pytest.ini_options] -pythonpath = ['.'] +pythonpath = ['python'] [tool.mypy] strict = true diff --git a/malsim/__init__.py b/python/malsim/__init__.py similarity index 100% rename from malsim/__init__.py rename to python/malsim/__init__.py diff --git a/malsim/__main__.py b/python/malsim/__main__.py similarity index 100% rename from malsim/__main__.py rename to python/malsim/__main__.py diff --git a/python/malsim/_native.pyi b/python/malsim/_native.pyi new file mode 100644 index 00000000..03401e1f --- /dev/null +++ b/python/malsim/_native.pyi @@ -0,0 +1,81 @@ +"""Type stubs for the compiled PyO3 extension module ``malsim._native``. + +This stub file sits next to the compiled ``malsim/_native.*.so`` extension +module (built via maturin from ``py-bindings/malsim-pyo3``), which is where +type checkers (mypy, pyright/Pylance) look for a compiled module's types. +It exists purely to give them full type/autocomplete information for that +module's API. + +See ``PORTING_NOTES.md`` §5/A1 for what's actually implemented so far - +`node_count` is a Phase A1 smoke-test function only, not a stable API. +`model_asset_count` is its Phase B3 equivalent for `maltoolbox.Model` +(§6/B3) - also not a stable API. +`Simulator` (Phase A8/A9, §5) backs `MalSimulator.reset()`/`.step()` as of +A9, but is still not itself a stable/public API (an implementation +detail of `malsim.mal_simulator.simulator`), and +`reset_native`/`step_native`'s dict shapes are deliberately typed as +plain `dict[str, Any]` here rather than a precise `TypedDict`. +`DynaSimulator` (Phase B4/B5, §6/§11) is the `DynaMalSimulator`-backing +equivalent: a separate pyclass, unrelated to `Simulator`, constructed +from the `AttackGraph` and the `Model` it was built from. The model is +snapshotted natively at construction; `restore_model_native` restores +the model/graph to that snapshot, and its `reset_native`/`step_native` +dicts additionally carry the step's model-effect modification record. +The simulators keep all state natively; `agent_states_native`/ +`sim_state_native`/`recording_native` translate the current state into +plain dicts on every call, and `Episode` (from `episode_native`) reads an +earlier iteration of an episode, for the lazy `previous_state` +(PORTING_NOTES.md §14). +""" + +from typing import Any + +from maltoolbox.attackgraph import AttackGraph +from maltoolbox.model import Model + +def node_count(graph: AttackGraph) -> int: ... +def model_asset_count(model: Model) -> int: ... +def model_add_asset_native(model: Model, asset_type: str) -> int: ... +def set_detector_rates( + graph: AttackGraph, + node_id: int, + label: str, + tprate: float | None, + fprate: float | None, +) -> None: ... + +class Simulator: + def __init__(self, graph: AttackGraph) -> None: ... + def reset_native( + self, + settings: dict[str, Any], + agents: dict[str, dict[str, Any]], + seed: int, + ) -> dict[str, Any]: ... + def step_native(self, actions: dict[str, list[int]]) -> dict[str, Any]: ... + def agent_states_native(self) -> dict[str, Any]: ... + def sim_state_native(self) -> dict[str, Any]: ... + def recording_native(self) -> dict[int, dict[str, list[int]]]: ... + def terminated_native(self) -> dict[str, bool]: ... + def episode_native(self) -> Episode: ... + +class DynaSimulator: + def __init__(self, graph: AttackGraph, model: Model) -> None: ... + def restore_model_native(self) -> None: ... + def reset_native( + self, + settings: dict[str, Any], + agents: dict[str, dict[str, Any]], + seed: int, + ) -> dict[str, Any]: ... + def step_native(self, actions: dict[str, list[int]]) -> dict[str, Any]: ... + def agent_states_native(self) -> dict[str, Any]: ... + def sim_state_native(self) -> dict[str, Any]: ... + def recording_native(self) -> dict[int, dict[str, list[int]]]: ... + def terminated_native(self) -> dict[str, bool]: ... + def episode_native(self) -> Episode: ... + +class Episode: + def snapshot_native( + self, iteration: int, agent: str | None = None + ) -> dict[str, Any]: ... diff --git a/malsim/config/__init__.py b/python/malsim/config/__init__.py similarity index 100% rename from malsim/config/__init__.py rename to python/malsim/config/__init__.py diff --git a/malsim/config/agent_settings.py b/python/malsim/config/agent_settings.py similarity index 100% rename from malsim/config/agent_settings.py rename to python/malsim/config/agent_settings.py diff --git a/malsim/config/agent_settings_factories.py b/python/malsim/config/agent_settings_factories.py similarity index 98% rename from malsim/config/agent_settings_factories.py rename to python/malsim/config/agent_settings_factories.py index 638e2cf9..3d09460f 100644 --- a/malsim/config/agent_settings_factories.py +++ b/python/malsim/config/agent_settings_factories.py @@ -116,7 +116,7 @@ def agent_settings_from_dict( ), rewards=NodePropertyRule.from_optional_dict(d.get('rewards')), config=config, - reward_mode=RewardMode[d.get('reward_mode', 'CUMULATIVE')], + reward_mode=RewardMode[d.get('reward_mode', 'EXPECTED_TTC')], ) # Defender diff --git a/malsim/config/node_property_rule.py b/python/malsim/config/node_property_rule.py similarity index 100% rename from malsim/config/node_property_rule.py rename to python/malsim/config/node_property_rule.py diff --git a/malsim/config/sim_settings.py b/python/malsim/config/sim_settings.py similarity index 100% rename from malsim/config/sim_settings.py rename to python/malsim/config/sim_settings.py diff --git a/malsim/dyna_mal_simulator/__init__.py b/python/malsim/dyna_mal_simulator/__init__.py similarity index 100% rename from malsim/dyna_mal_simulator/__init__.py rename to python/malsim/dyna_mal_simulator/__init__.py diff --git a/malsim/dyna_mal_simulator/simulator.py b/python/malsim/dyna_mal_simulator/simulator.py similarity index 50% rename from malsim/dyna_mal_simulator/simulator.py rename to python/malsim/dyna_mal_simulator/simulator.py index 311c922e..252a8b47 100644 --- a/malsim/dyna_mal_simulator/simulator.py +++ b/python/malsim/dyna_mal_simulator/simulator.py @@ -2,31 +2,39 @@ from collections import defaultdict import logging -from typing import Any, NamedTuple +from typing import Any from collections.abc import Callable, Iterable, Mapping, Set import numpy as np from numpy.random import default_rng from maltoolbox.attackgraph import AttackGraph, AttackGraphNode +from malsim import _native from malsim.config.agent_settings import defender_settings from malsim.config.agent_settings import attacker_settings -from malsim.dyna_mal_simulator.model_state import reset_model_effects from malsim.mal_simulator.agent_states import ( AgentStates, attacker_states, defender_states, ) from malsim.mal_simulator.attacker_state import AttackerState -from malsim.dyna_mal_simulator.attacker_step import dyna_attacker_step +from malsim.mal_simulator.attacker_state_factories import ( + create_attacker_state_from_native, + get_entry_points, +) from malsim.mal_simulator.defender_state import DefenderState -from malsim.dyna_mal_simulator.defender_step import dyna_defender_step -from malsim.mal_simulator.graph_state import compute_initial_graph_state +from malsim.mal_simulator.defender_state_factories import ( + create_defender_state_from_native, +) +from malsim.mal_simulator.native_settings import ( + flatten_attacker_settings, + flatten_defender_settings, + flatten_sim_settings, +) from malsim.mal_simulator.node_getters import ( full_names_or_nodes_to_nodes, ) -from malsim.mal_simulator.reset_agent import reset_agents from malsim.mal_simulator.rewards import ( attacker_step_reward_fn, defender_step_reward_fn, @@ -36,16 +44,23 @@ AttackerSettings, DefenderSettings, ) -from malsim.mal_simulator.simulator import _pre_step_check, alive_agents +from malsim.mal_simulator.simulator import ( + MALSimulatorStaticData, + _graph_state_from_native, + _ordered_new_nodes, + _pre_step_check, + alive_agents, +) from malsim.types import ( Recording, ) from malsim.scenario.scenario import Scenario -from malsim.mal_simulator.attacker_state_factories import create_attacker_state -from malsim.mal_simulator.defender_state_factories import create_defender_state from malsim.dyna_mal_simulator.simulator_state import ( + AssetOp, + AssocOp, DynaMalSimulatorState, create_simulator_state, + modification_record_from_native, update_simulator_state, ) from malsim.config.sim_settings import MalSimulatorSettings, RewardMode @@ -88,11 +103,6 @@ BASE_SETTINGS = MalSimulatorSettings() -class MALSimulatorStaticData(NamedTuple): - attack_graph: AttackGraph - sim_settings: MalSimulatorSettings - - class DynaMalSimulator(MalSimulator): """A MAL Simulator that works on the AttackGraph @@ -129,26 +139,38 @@ def __init__( 'to be set.' ) + native_sim = _native.DynaSimulator(attack_graph, attack_graph.model) + + static_sim_data = MALSimulatorStaticData( + attack_graph, + sim_settings, + ) + _attacker_settings = [a for a in agents if isinstance(a, AttackerSettings)] _defender_settings = [a for a in agents if isinstance(a, DefenderSettings)] + # Entry points/goals by full name, captured while the graph is still + # pristine: a node removed by a model effect can't be read any more, + # and `dyna_reset` re-resolves these against the restored graph. + attacker_settings_by_full_name = { + a.name: _attacker_settings_with_full_names(a) for a in _attacker_settings + } attacker_settings_with_nodes = [ - a.convert_to_attack_graph_nodes(attack_graph) for a in _attacker_settings + a.convert_to_attack_graph_nodes(attack_graph) + for a in attacker_settings_by_full_name.values() ] _agent_settings: AgentSettings = { a.name: a for a in (_defender_settings + attacker_settings_with_nodes) } or {} - model_snapshot = attack_graph.model.to_dict() - - agent_states, sim_state, recording = dyna_reset( - model_snapshot=model_snapshot, - attack_graph=attack_graph, - settings=sim_settings, - agent_settings=_agent_settings, - rng=rng, - rest_api_client=rest_api_client, + agent_states, sim_state, recording, _agent_settings = dyna_reset( + static_sim_data, + _agent_settings, + attacker_settings_by_full_name, + rng, + rest_api_client, + native_sim, ) defender_reward_fns = { @@ -176,12 +198,22 @@ def __init__( self.recording = recording self.sim_settings = sim_settings self.agent_settings = _agent_settings + self._attacker_settings_by_full_name = attacker_settings_by_full_name self.rest_api_client = rest_api_client self._attack_graph = attack_graph - self._sim_settings = sim_settings + self._static_data = static_sim_data + self._dyna_native_sim = native_sim self._defender_reward_fns = defender_reward_fns self._attacker_reward_fns = attacker_reward_fns - self._model_snapshot = model_snapshot + + def __getstate__(self) -> dict[str, Any]: + """Same as `MalSimulator.__getstate__`, but also leaves out the + native `malsim._native.DynaSimulator` handle - kept under its own + name because it's a different native type than `_native_sim` + (PORTING_NOTES.md §11).""" + state = super().__getstate__() + state.pop('_dyna_native_sim', None) + return state @classmethod def from_scenario( @@ -209,13 +241,14 @@ def reset( self._agent_states, self.sim_state, self.recording, + self.agent_settings, ) = dyna_reset( - model_snapshot=self._model_snapshot, - attack_graph=self._attack_graph, - settings=self.sim_settings, - agent_settings=self.agent_settings, - rng=self.rng, - rest_api_client=self.rest_api_client, + self._static_data, + self.agent_settings, + self._attacker_settings_by_full_name, + self.rng, + self.rest_api_client, + self._dyna_native_sim, ) if seed is not None: @@ -230,8 +263,8 @@ def step( self.recording, self.sim_state, self._agent_states, - self.rng, actions, + self._dyna_native_sim, self.rest_api_client, ) self._agent_states = agent_states @@ -258,48 +291,145 @@ def dyna_create_simulator_from_scenario( ) +def _attacker_settings_with_full_names( + settings: AttackerSettings[AttackGraphNode | str], +) -> AttackerSettings[str]: + """`settings` with entry points and goals as full names, so they can be + resolved again after a reset regenerates the nodes they refer to.""" + + def full_name(node: AttackGraphNode | str) -> str: + return node if isinstance(node, str) else node.full_name + + entry_points: tuple[Set[str], ...] | Set[str] + if isinstance(settings.entry_points, Set): + entry_points = frozenset(full_name(n) for n in settings.entry_points) + else: + entry_points = tuple( + frozenset(full_name(n) for n in eps) for eps in settings.entry_points + ) + return AttackerSettings( + name=settings.name, + entry_points=entry_points, + goals=frozenset(full_name(n) for n in settings.goals), + policy=settings.policy, + actionable_steps=settings.actionable_steps, + rewards=settings.rewards, + config=settings.config, + type=settings.type, + reward_mode=settings.reward_mode, + ttc_dists=settings.ttc_dists, + ) + + def dyna_reset( - model_snapshot: dict[str, Any], - attack_graph: AttackGraph, - settings: MalSimulatorSettings, + static_data: MALSimulatorStaticData, agent_settings: AgentSettings, + attacker_settings_by_full_name: Mapping[str, AttackerSettings[str]], rng: np.random.Generator, rest_api_client: MalSimGUIClient | None, + native_sim: _native.DynaSimulator, ) -> tuple[ AgentStates, DynaMalSimulatorState, Recording, + AgentSettings, ]: - """Reset attack graph and reinitialize agents""" + """Reset attack graph and reinitialize agents. + + Delegates to `malsim._native.DynaSimulator.reset_native` + (PORTING_NOTES.md §6 Phase B5, A9-equivalent) - the live `Model` + (restored to the pristine snapshot native captured when the + `DynaSimulator` was constructed in `DynaMalSimulator.__init__` - see + `malsim_core::dyna_simulator::DynaSimulator`, PORTING_NOTES.md §11) and + `AttackGraph` are both mutated in place through the shared handles, + same `AttackGraph::partially_regenerate_graph` bookkeeping B1/B2 + already proved. "Multiple entry point sets, sampled at reset" is + resolved here first, same reasoning as `mal_simulator.simulator.reset`. + + The model is restored (`restore_model_native`) *before* agent + settings are flattened: nodes the previous episode's model effects + removed only come back, with regenerated ids, on restore, so rules + must be resolved against the restored graph (PORTING_NOTES.md §12). + For the same reason attacker entry points and goals are re-resolved + from `attacker_settings_by_full_name` after the restore, and the + re-resolved `agent_settings` are returned alongside the states. + """ logger.info('Resetting Dyna MAL Simulator.') + attack_graph = static_data.attack_graph + settings = static_data.sim_settings + assert attack_graph.model is not None, ( + 'DynaMalSimulator requires attack_graph.model to be set.' + ) - # Restore the instance model and regenerate the attack graph to match - reset_model_effects(attack_graph, model_snapshot) + native_sim.restore_model_native() + + agent_settings = { + **agent_settings, + **{ + name: named.convert_to_attack_graph_nodes(attack_graph) + for name, named in attacker_settings_by_full_name.items() + }, + } + _attacker_settings = attacker_settings(agent_settings) + _defender_settings = defender_settings(agent_settings) + + entry_points_by_attacker = { + name: get_entry_points(attack_graph, a_settings, rng) + for name, a_settings in _attacker_settings.items() + } + + native_settings = flatten_sim_settings(settings) + native_agents: dict[str, Any] = { + name: flatten_attacker_settings( + a_settings, attack_graph, entry_points_by_attacker[name] + ) + for name, a_settings in _attacker_settings.items() + } + native_agents.update( + { + name: flatten_defender_settings(d_settings, attack_graph) + for name, d_settings in _defender_settings.items() + } + ) - # Re-calculate initial simulator state - graph_state = compute_initial_graph_state(attack_graph, settings, rng) + native_seed = int(rng.integers(0, 2**63 - 1)) + native_out = native_sim.reset_native(native_settings, native_agents, native_seed) + + graph_state = _graph_state_from_native(attack_graph, native_out['sim_state']) sim_state = create_simulator_state(attack_graph, graph_state, settings) - agent_states = reset_agents( - sim_state, - settings, - agent_settings, - rng, - ) + agent_states: AgentStates = {} + for name, a_settings in _attacker_settings.items(): + agent_states[name] = create_attacker_state_from_native( + sim_state, + name, + a_settings, + entry_points_by_attacker[name], + native_out['agents'][name], + previous_state=None, + ) + for name, d_settings in _defender_settings.items(): + agent_states[name] = create_defender_state_from_native( + sim_state, + name, + d_settings, + native_out['agents'][name], + previous_state=None, + ) # Upload initial state to the REST API if rest_api_client: rest_api_client.upload_initial_state(attack_graph) - return agent_states, sim_state, defaultdict(dict) + return agent_states, sim_state, defaultdict(dict), agent_settings def dyna_step( recording: Recording, sim_state: DynaMalSimulatorState, agent_states: AgentStates, - rng: np.random.Generator, actions: dict[str, list[AttackGraphNode]] | dict[str, list[str]], + native_sim: _native.DynaSimulator, rest_api_client: MalSimGUIClient | None = None, ) -> tuple[AgentStates, Recording, DynaMalSimulatorState]: """Take a step in the simulation @@ -310,10 +440,48 @@ def dyna_step( Returns: - A dictionary containing the agent state views keyed by agent names + + Delegates to `malsim._native.DynaSimulator.step_native` + (PORTING_NOTES.md §6 Phase B5, A9-equivalent), which runs defenders + before attackers internally and folds any model-effect-created nodes + into its own `graph_state`/`enabled_defenses` as it goes (B1/B2), + same ordering `dyna_step`'s old pure-Python version used. """ _pre_step_check(agent_states, alive_agents(agent_states), actions) + attack_graph = sim_state.attack_graph + assert attack_graph.model is not None, ( + 'DynaMalSimulator requires attack_graph.model to be set.' + ) + native_actions = { + name: [node.id for node in full_names_or_nodes_to_nodes(attack_graph, nodes)] + for name, nodes in actions.items() + } + native_out = native_sim.step_native(native_actions) + + new_modification_record: list[AssetOp | AssocOp] = modification_record_from_native( + attack_graph.model, native_out['sim_state']['step_modification_record'] + ) + # `ttc_values`/etc. only appear in this step's output when a model + # effect actually grew the graph (PORTING_NOTES.md §6 Phase B5's + # TTC-gap fix) - `build_step_output`'s `insert_graph_state_fields` call + # is gated on `step_modification_record` being non-empty. + new_graph_state = ( + _graph_state_from_native(attack_graph, native_out['sim_state']) + if 'ttc_values' in native_out['sim_state'] + else None + ) + sim_state = update_simulator_state( + sim_state, + frozenset( + attack_graph.nodes[node_id] + for node_id in native_out['sim_state']['step_enabled_defenses'] + ), + new_modification_record, + new_graph_state, + ) + # Populate these from the results for all agents' actions. step_compromised_nodes: list[AttackGraphNode] = [] step_enabled_defenses: list[AttackGraphNode] = [] @@ -321,62 +489,48 @@ def dyna_step( # Perform defender actions first for defender_state in defender_states(agent_states).values(): - agent_actions = list( - full_names_or_nodes_to_nodes( - sim_state.attack_graph, actions.get(defender_state.name, []) - ) - ) - enabled, sim_state = dyna_defender_step( - sim_state, defender_state, agent_actions, rng + new_defender_state = create_defender_state_from_native( + sim_state, + defender_state.name, + defender_state.settings, + native_out['agents'][defender_state.name], + previous_state=defender_state, ) current_iteration = defender_state.iteration - recording[current_iteration][defender_state.name] = list(enabled) + requested = list( + full_names_or_nodes_to_nodes( + attack_graph, actions.get(defender_state.name, []) + ) + ) + enabled = _ordered_new_nodes(requested, new_defender_state.step_performed_nodes) + recording[current_iteration][defender_state.name] = enabled step_enabled_defenses += enabled - sim_state = update_simulator_state(sim_state, set(step_enabled_defenses), []) + agent_states[defender_state.name] = new_defender_state # Perform attacker actions afterwards for attacker_state in attacker_states(agent_states).values(): - agent_actions = list( - full_names_or_nodes_to_nodes( - sim_state.attack_graph, actions.get(attacker_state.name, []) - ) - ) - agent_compromised, agent_attempted, sim_state = dyna_attacker_step( - sim_state, attacker_state, agent_actions, rng - ) - current_iteration = attacker_state.iteration - step_compromised_nodes += agent_compromised - recording[current_iteration][attacker_state.name] = list(agent_compromised) - sim_state = update_simulator_state(sim_state, set(), []) - - # Update attacker state - agent_states[attacker_state.name] = create_attacker_state( - sim_state=sim_state, - attack_surface_settings=sim_state.settings.attack_surface, - attacker_settings=attacker_state.settings, - name=attacker_state.name, - entry_points=attacker_state.entry_points, - new_performed_nodes=frozenset(agent_compromised), - new_attempted_nodes=frozenset(agent_attempted), + new_attacker_state = create_attacker_state_from_native( + sim_state, + attacker_state.name, + attacker_state.settings, + attacker_state.entry_points, + native_out['agents'][attacker_state.name], previous_state=attacker_state, - ttc_values=attacker_state.ttc_values, - impossible_steps=attacker_state.impossible_steps, ) + current_iteration = attacker_state.iteration - # Update defender states and rewards - for defender_state in defender_states(agent_states).values(): - current_iteration = defender_state.iteration - # Update defender state - agent_states[defender_state.name] = create_defender_state( - sim_state=sim_state, - name=defender_state.name, - defender_settings=defender_state.settings, - new_compromised_nodes=set(step_compromised_nodes), - new_enabled_defenses=set(step_enabled_defenses), - previous_state=defender_state, - rng=rng, + requested = list( + full_names_or_nodes_to_nodes( + attack_graph, actions.get(attacker_state.name, []) + ) + ) + compromised = _ordered_new_nodes( + requested, new_attacker_state.step_performed_nodes ) + step_compromised_nodes += compromised + recording[current_iteration][attacker_state.name] = compromised + agent_states[attacker_state.name] = new_attacker_state # the way current_iteration is used here is flawed. if rest_api_client: diff --git a/python/malsim/dyna_mal_simulator/simulator_state.py b/python/malsim/dyna_mal_simulator/simulator_state.py new file mode 100644 index 00000000..3928ceb2 --- /dev/null +++ b/python/malsim/dyna_mal_simulator/simulator_state.py @@ -0,0 +1,146 @@ +from collections.abc import Mapping, Set +from dataclasses import dataclass +from typing import Any + +from maltoolbox.attackgraph import AttackGraph, AttackGraphNode + +from malsim.config.sim_settings import MalSimulatorSettings +from malsim.mal_simulator.graph_state import GraphState +from maltoolbox.language.language_graph_model_effect import ModelEffectType +from maltoolbox.model import Model, ModelAsset + +from malsim.mal_simulator.simulator_state import MalSimulatorState + + +@dataclass(frozen=True) +class DetachedAsset: + """A self-contained snapshot (id/type/name) of a model asset that is + no longer resolvable via `model.assets` by the time a modification + record is read - built directly from `malsim._native`'s + `step_modification_record` output (`AssetRef` on the Rust side), + never via a `Model` lookup. See `modification_record_from_native`'s + doc comment (PORTING_NOTES.md §6 Phase B5) for when this happens: + the native dyna step mutates the shared `Model` via the core Rust + `Model::remove_asset` directly, which - unlike `maltoolbox`'s + Python-facing `PyModel.remove_asset` - never records a tombstone, so + a removed asset's `ModelAsset` Python handle is simply gone, not + just detached-but-still-readable the way the pure-Python path left + it. + """ + + id: int + type: str + name: str + + +@dataclass(frozen=True) +class AssetOp: + """Represents a change to an asset in the model.""" + + type: ModelEffectType + asset: ModelAsset | DetachedAsset + + +@dataclass(frozen=True) +class AssocOp: + """Represents a change to the way two assets are associated in the model.""" + + type: ModelEffectType + assoc: tuple[ModelAsset | DetachedAsset, str, ModelAsset | DetachedAsset] + + +@dataclass(frozen=True) +class DynaMalSimulatorState(MalSimulatorState): + modification_record: list[AssetOp | AssocOp] + + +def create_simulator_state( + attack_graph: AttackGraph, + graph_state: GraphState, + sim_settings: MalSimulatorSettings, +) -> DynaMalSimulatorState: + return DynaMalSimulatorState( + attack_graph, + sim_settings, + graph_state, + enabled_defenses=graph_state.pre_enabled_defenses, + modification_record=[], + ) + + +def update_simulator_state( + sim_state: DynaMalSimulatorState, + enabled_defenses: Set[AttackGraphNode], + model_effects: list[AssetOp | AssocOp], + graph_state: GraphState | None = None, +) -> DynaMalSimulatorState: + """`graph_state` is only passed when this step's native output actually + carried a refreshed one (PORTING_NOTES.md §6 Phase B5's TTC-gap fix) - + a dyna step that ran a model effect and grew `ttc_values`/ + `necessity_per_node`/etc. mid-episode. `None` means nothing grew this + step; `sim_state.graph_state` is still the correct, current value and + is carried over unchanged, same as before this fix. + """ + return DynaMalSimulatorState( + sim_state.attack_graph, + sim_state.settings, + graph_state if graph_state is not None else sim_state.graph_state, + enabled_defenses=enabled_defenses | sim_state.enabled_defenses, + modification_record=sim_state.modification_record + model_effects, + ) + + +def _resolve_asset_ref( + model: Model, native_op: Mapping[str, Any], prefix: str +) -> ModelAsset | DetachedAsset: + """Resolves one `AssetRef`-shaped (`{prefix}_id`/`{prefix}_type`/ + `{prefix}_name`) slice of a native modification-record entry. Prefers + the live `ModelAsset` handle when the id is still resolvable (keeps + identity/equality with any other live reference to the same asset - + e.g. an added asset a caller already holds its own handle to) and + only falls back to `DetachedAsset`'s self-contained snapshot when + it's not (a removed asset - see `DetachedAsset`'s doc comment). + """ + asset_id = native_op[f'{prefix}_id'] + live = model.get_asset_by_id(asset_id) + if live is not None: + return live + return DetachedAsset( + id=asset_id, + type=native_op[f'{prefix}_type'], + name=native_op[f'{prefix}_name'], + ) + + +def modification_record_from_native( + model: Model, native_record: list[Mapping[str, Any]] +) -> list[AssetOp | AssocOp]: + """Resolves `malsim._native.DynaSimulator.step_native`'s + `step_modification_record` output (PORTING_NOTES.md §6 Phase B4/B5, + §11 - a list of plain dicts, each already carrying every asset's + id/type/name snapshot inline, per `AssetRef`'s doc comment on the + Rust side) into `AssetOp`/`AssocOp` + objects, for `DynaMalSimulatorState.modification_record`. + """ + record: list[AssetOp | AssocOp] = [] + for native_op in native_record: + effect_type = ModelEffectType(native_op['type']) + if native_op['kind'] == 'asset': + record.append( + AssetOp( + type=effect_type, + asset=_resolve_asset_ref(model, native_op, 'asset'), + ) + ) + else: + record.append( + AssocOp( + type=effect_type, + assoc=( + _resolve_asset_ref(model, native_op, 'left_asset'), + native_op['field_name'], + _resolve_asset_ref(model, native_op, 'right_asset'), + ), + ) + ) + return record diff --git a/malsim/envs/__init__.py b/python/malsim/envs/__init__.py similarity index 100% rename from malsim/envs/__init__.py rename to python/malsim/envs/__init__.py diff --git a/malsim/envs/base_classes.py b/python/malsim/envs/base_classes.py similarity index 100% rename from malsim/envs/base_classes.py rename to python/malsim/envs/base_classes.py diff --git a/malsim/envs/graph/README.md b/python/malsim/envs/graph/README.md similarity index 100% rename from malsim/envs/graph/README.md rename to python/malsim/envs/graph/README.md diff --git a/malsim/envs/graph/__init__.py b/python/malsim/envs/graph/__init__.py similarity index 100% rename from malsim/envs/graph/__init__.py rename to python/malsim/envs/graph/__init__.py diff --git a/malsim/envs/graph/graph_env.py b/python/malsim/envs/graph/graph_env.py similarity index 100% rename from malsim/envs/graph/graph_env.py rename to python/malsim/envs/graph/graph_env.py diff --git a/malsim/envs/graph/mal_spaces.py b/python/malsim/envs/graph/mal_spaces.py similarity index 100% rename from malsim/envs/graph/mal_spaces.py rename to python/malsim/envs/graph/mal_spaces.py diff --git a/malsim/envs/graph/serialization.py b/python/malsim/envs/graph/serialization.py similarity index 100% rename from malsim/envs/graph/serialization.py rename to python/malsim/envs/graph/serialization.py diff --git a/malsim/envs/graph/utils.py b/python/malsim/envs/graph/utils.py similarity index 99% rename from malsim/envs/graph/utils.py rename to python/malsim/envs/graph/utils.py index 038f7f67..21e2c345 100644 --- a/malsim/envs/graph/utils.py +++ b/python/malsim/envs/graph/utils.py @@ -127,10 +127,10 @@ def is_traversable_by_any(node: AttackGraphNode) -> bool: for fieldname, other_assets in asset.associated_assets.items(): assoc = asset.lg_asset.associations[fieldname] for other_asset in other_assets: - if (assoc, asset.id, other_asset.id) not in associations and ( + if (assoc, asset, other_asset) not in associations and ( assoc, - other_asset.id, - asset.id, + other_asset, + asset, ) not in associations: associations.append((assoc, asset, other_asset)) sorted_associations = sorted( diff --git a/malsim/envs/graph/wrapper.py b/python/malsim/envs/graph/wrapper.py similarity index 100% rename from malsim/envs/graph/wrapper.py rename to python/malsim/envs/graph/wrapper.py diff --git a/malsim/mal_simulator/__init__.py b/python/malsim/mal_simulator/__init__.py similarity index 100% rename from malsim/mal_simulator/__init__.py rename to python/malsim/mal_simulator/__init__.py diff --git a/malsim/mal_simulator/agent_state.py b/python/malsim/mal_simulator/agent_state.py similarity index 100% rename from malsim/mal_simulator/agent_state.py rename to python/malsim/mal_simulator/agent_state.py diff --git a/malsim/mal_simulator/agent_states.py b/python/malsim/mal_simulator/agent_states.py similarity index 100% rename from malsim/mal_simulator/agent_states.py rename to python/malsim/mal_simulator/agent_states.py diff --git a/malsim/mal_simulator/attacker_state.py b/python/malsim/mal_simulator/attacker_state.py similarity index 100% rename from malsim/mal_simulator/attacker_state.py rename to python/malsim/mal_simulator/attacker_state.py diff --git a/python/malsim/mal_simulator/attacker_state_factories.py b/python/malsim/mal_simulator/attacker_state_factories.py new file mode 100644 index 00000000..6fa112ff --- /dev/null +++ b/python/malsim/mal_simulator/attacker_state_factories.py @@ -0,0 +1,174 @@ +"""Creation/manipulation of attacker state""" + +from __future__ import annotations +from collections.abc import Set, Mapping +from typing import Any, TYPE_CHECKING + +from malsim.mal_simulator.attacker_state import AttackerState +from malsim.mal_simulator.node_getters import ( + full_names_or_nodes_to_nodes, +) +from malsim.config.agent_settings import AttackerSettings + +if TYPE_CHECKING: + from maltoolbox.attackgraph import AttackGraph, AttackGraphNode + from malsim.mal_simulator.simulator_state import MalSimulatorState + import numpy as np + + +def create_attacker_state_from_native( + sim_state: MalSimulatorState, + name: str, + attacker_settings: AttackerSettings[AttackGraphNode], + entry_points: Set[AttackGraphNode], + native_agent_out: Mapping[str, Any], + previous_state: AttackerState | None, +) -> AttackerState: + """Build an `AttackerState` from one attacker's `reset_native`/ + `step_native` output dict (PORTING_NOTES.md §5 Phase A9, plus the + post-A10 delta-wire-format perf fix - see §10) - the + native-output-driven counterpart of `create_attacker_state` above. + + `create_attacker_state`/`initial_attacker_state` are deliberately left + untouched by this function: `DynaMalSimulator` still calls them + directly with its own pure-Python recompute (§2.3 - its own port is a + later phase), so changing their signature/behavior would break it. See + PORTING_NOTES.md §10 for why this is a new, separate function rather + than an in-place rewrite of `create_attacker_state`. + + Unlike `create_attacker_state`, most fields here are resolved directly + from native's output rather than recomputed in Python - but since the + delta-wire-format fix (§10), `step_native`'s output is a *delta* + (`step_performed_nodes`/`step_attempted_nodes`), not the full + episode-accumulated value `reset_native` still returns under the old + full-field names. On the + first call after reset (`previous_state is None`) the full fields are + read as before; on every subsequent call the delta is merged against + `previous_state`. `ttc_values`/`impossible_steps` are resolved once + from `reset_native`'s output and carried forward unchanged - native + never re-sends them (they're static after reset even when + `ttc_dists` overrides are configured - see `malsim_core::simulator`'s + `attacker_ttc_overrides` doc comment). `performed_nodes_order` is + Python-only bookkeeping native has no concept of, built incrementally + via a diff against `previous_state`, same as before. + """ + attack_graph = sim_state.attack_graph + + action_surface = frozenset( + attack_graph.nodes[node_id] for node_id in native_agent_out['action_surface'] + ) + + performed_nodes: Set[AttackGraphNode] + attempted_nodes: Set[AttackGraphNode] + num_attempts: dict[AttackGraphNode, int] + ttc_values: Mapping[AttackGraphNode, float] + impossible_steps: Set[AttackGraphNode] + + if previous_state is None: + new_performed_nodes = frozenset( + attack_graph.nodes[node_id] + for node_id in native_agent_out['performed_nodes'] + ) + new_attempted_nodes = frozenset( + attack_graph.nodes[node_id] + for node_id in native_agent_out['attempted_nodes'] + ) + # Dense by default (every attack step present, defaulting to 0) - + # `attempt_attacker_step` (pure Python, still used directly by + # `DynaMalSimulator` and by some existing tests - §2.3/§10) + # indexes `agent.num_attempts[node]` unconditionally for any node + # about to be attempted, matching `create_attacker_state`'s own + # `dict.fromkeys(sim_state.attack_graph.attack_steps, 0)` - + # native's own map is sparse (only nodes actually attempted), so + # it's overlaid on top of the dense default rather than used + # as-is. Only built once here, at reset - every subsequent call + # starts from `previous_state.num_attempts` instead. + num_attempts = dict.fromkeys(attack_graph.attack_steps, 0) + num_attempts.update( + { + attack_graph.nodes[node_id]: count + for node_id, count in native_agent_out['num_attempts'].items() + } + ) + ttc_values = { + attack_graph.nodes[node_id]: value + for node_id, value in native_agent_out['ttc_values'].items() + } + impossible_steps = frozenset( + attack_graph.nodes[node_id] + for node_id in native_agent_out['impossible_steps'] + ) + performed_nodes = new_performed_nodes + attempted_nodes = new_attempted_nodes + else: + new_performed_nodes = frozenset( + attack_graph.nodes[node_id] + for node_id in native_agent_out['step_performed_nodes'] + ) + new_attempted_nodes = frozenset( + attack_graph.nodes[node_id] + for node_id in native_agent_out['step_attempted_nodes'] + ) + performed_nodes = previous_state.performed_nodes | new_performed_nodes + attempted_nodes = previous_state.attempted_nodes | new_attempted_nodes + num_attempts = dict(previous_state.num_attempts) + for node in new_attempted_nodes: + num_attempts[node] += 1 + ttc_values = previous_state.ttc_values + impossible_steps = previous_state.impossible_steps + + performed_nodes_order = dict( + previous_state.performed_nodes_order if previous_state else {} + ) + # native's returned `iteration` is 0-indexed from reset and is exactly + # the key `performed_nodes_order` should use for this call's new + # nodes; `AttackerState.iteration` itself is 1-indexed (§5/A9, §10). + native_iteration = native_agent_out['iteration'] + if new_performed_nodes: + performed_nodes_order[native_iteration] = frozenset(new_performed_nodes) + + return AttackerState( + name, + entry_points=entry_points, + sim_state=sim_state, + iteration=native_iteration + 1, + performed_nodes_order=performed_nodes_order, + settings=attacker_settings, + ttc_values=ttc_values, + impossible_steps=impossible_steps, + performed_nodes=performed_nodes, + attempted_nodes=attempted_nodes, + action_surface=action_surface, + num_attempts=num_attempts, + previous_state=previous_state, + goals=attacker_settings.goals, + ) + + +def get_entry_points( + attack_graph: AttackGraph, + attacker_settings: AttackerSettings[AttackGraphNode], + rng: np.random.Generator, +) -> Set[AttackGraphNode]: + """ + Get entry points as set of AttackGraphNodes from attacker settings. + If multiple sets of entry points are given, sample one set from the options. + + Takes `attack_graph` directly (not a `MalSimulatorState`) since this is + the only thing its body reads - this lets it be called before a + `MalSimulatorState` exists yet (PORTING_NOTES.md §5/§10 Phase A9: the + native-backed `reset()` must resolve entry points before it has built + one, to pass the single resolved set into `reset_native`). + """ + + if isinstance(attacker_settings.entry_points, Set): + return frozenset( + full_names_or_nodes_to_nodes(attack_graph, attacker_settings.entry_points) + ) + else: + # Multiple potential entry point sets given + # - sample one set of entry points from the options + chosen_entry_points = rng.choice(list(attacker_settings.entry_points)) + return set( + full_names_or_nodes_to_nodes(attack_graph, chosen_entry_points) # type: ignore + ) diff --git a/python/malsim/mal_simulator/attacker_step.py b/python/malsim/mal_simulator/attacker_step.py new file mode 100644 index 00000000..4101ec3d --- /dev/null +++ b/python/malsim/mal_simulator/attacker_step.py @@ -0,0 +1,30 @@ +from __future__ import annotations +from typing import TYPE_CHECKING +import logging + +if TYPE_CHECKING: + from malsim.mal_simulator.attacker_state import AttackerState + +logger = logging.getLogger(__name__) + + +def attacker_is_terminated(attacker_state: AttackerState) -> bool: + """Check if attacker is terminated + Can be overridden by subclass for custom termination condition. + + Args: + - attacker_state: the attacker state to check for termination + """ + + if len(attacker_state.action_surface) == 0: + # Attacker is terminated if it has no more actions to take + logger.info( + 'Attacker "%s" action surface is empty, terminate', attacker_state.name + ) + return True + goals = attacker_state.settings.goals + if goals: + # Attacker is terminated if it has goals and all goals are met + return goals & attacker_state.performed_nodes == goals + # Otherwise not terminated + return False diff --git a/malsim/mal_simulator/defender_state.py b/python/malsim/mal_simulator/defender_state.py similarity index 100% rename from malsim/mal_simulator/defender_state.py rename to python/malsim/mal_simulator/defender_state.py diff --git a/python/malsim/mal_simulator/defender_state_factories.py b/python/malsim/mal_simulator/defender_state_factories.py new file mode 100644 index 00000000..0764e2b0 --- /dev/null +++ b/python/malsim/mal_simulator/defender_state_factories.py @@ -0,0 +1,141 @@ +"""Creation/manipulation of defender state""" + +from __future__ import annotations +from collections.abc import Mapping, Set +from typing import Any, TYPE_CHECKING + +from maltoolbox.attackgraph import AttackGraphNode + +from malsim.config.agent_settings import DefenderSettings +from malsim.mal_simulator.defender_state import DefenderState +from malsim.mal_simulator.event_logger import LogEntry +from malsim.mal_simulator.simulator_state import MalSimulatorState + +if TYPE_CHECKING: + from maltoolbox.attackgraph import AttackGraph + + +def _log_entry_from_native( + attack_graph: AttackGraph, native_log: Mapping[str, Any] +) -> LogEntry: + """Resolve one native `LogEntry` dict (detector/trigger/context as ids) + back into the real Python `LogEntry` dataclass (§3: "only plain data + crosses the FFI boundary" - `LogEntry` itself is rebuilt on the Python + side, same pattern as `AttackerState`/`DefenderState`).""" + detector_node = attack_graph.nodes[native_log['detector_node_id']] + detector = detector_node.detectors[native_log['detector_label']] + return LogEntry( + timestep=native_log['timestep'], + detector_name=str(detector.name), + detector=detector, + trigger=attack_graph.nodes[native_log['trigger']], + context={ + label: attack_graph.nodes[node_id] + for label, node_id in native_log['context'].items() + }, + false_positive=native_log['false_positive'], + ) + + +def create_defender_state_from_native( + sim_state: MalSimulatorState, + name: str, + defender_settings: DefenderSettings, + native_agent_out: Mapping[str, Any], + previous_state: DefenderState | None, +) -> DefenderState: + """Build a `DefenderState` from one defender's `reset_native`/ + `step_native` output dict (PORTING_NOTES.md §5 Phase A9, plus the + post-A10 delta-wire-format perf fix - see §10) - the + native-output-driven counterpart of `create_defender_state` above. + + `create_defender_state`/`initial_defender_state` are deliberately left + untouched (§2.3/§10 - `DynaMalSimulator` still calls them directly), + same reasoning as `create_attacker_state_from_native`. + + Unlike `create_defender_state`, `performed_nodes`/`compromised_nodes`/ + `observed_nodes`/`logs` are resolved directly from native's output on + the first call after reset (`previous_state is None`), where + `reset_native`'s output still carries the full fields under their old + names. On every subsequent call, `step_native`'s output is a *delta* + (`step_performed_nodes`/`step_compromised_nodes`/ + `step_observed_nodes`/`step_logs` - §10) merged against + `previous_state` instead - this is also what fixes the O(episode^2) + bug `logs` used to have, where every log ever fired was re-parsed into + a `LogEntry` on every single step. `performed_nodes_order` is still + built incrementally via a diff against `previous_state`, same as the + attacker counterpart. + """ + attack_graph = sim_state.attack_graph + + action_surface = frozenset( + attack_graph.nodes[node_id] for node_id in native_agent_out['action_surface'] + ) + + performed_nodes: Set[AttackGraphNode] + compromised_nodes: Set[AttackGraphNode] + observed_nodes_: Set[AttackGraphNode] + + if previous_state is None: + new_performed_nodes = frozenset( + attack_graph.nodes[node_id] + for node_id in native_agent_out['performed_nodes'] + ) + new_compromised_nodes = frozenset( + attack_graph.nodes[node_id] + for node_id in native_agent_out['compromised_nodes'] + ) + new_observed_nodes = frozenset( + attack_graph.nodes[node_id] + for node_id in native_agent_out['observed_nodes'] + ) + new_logs = tuple( + _log_entry_from_native(attack_graph, log) + for log in native_agent_out['logs'] + ) + performed_nodes = new_performed_nodes + compromised_nodes = new_compromised_nodes + observed_nodes_ = new_observed_nodes + logs = new_logs + else: + new_performed_nodes = frozenset( + attack_graph.nodes[node_id] + for node_id in native_agent_out['step_performed_nodes'] + ) + new_compromised_nodes = frozenset( + attack_graph.nodes[node_id] + for node_id in native_agent_out['step_compromised_nodes'] + ) + new_observed_nodes = frozenset( + attack_graph.nodes[node_id] + for node_id in native_agent_out['step_observed_nodes'] + ) + new_logs = tuple( + _log_entry_from_native(attack_graph, log) + for log in native_agent_out['step_logs'] + ) + performed_nodes = previous_state.performed_nodes | new_performed_nodes + compromised_nodes = previous_state.compromised_nodes | new_compromised_nodes + observed_nodes_ = previous_state.observed_nodes | new_observed_nodes + logs = previous_state.logs + new_logs + + performed_nodes_order = dict( + previous_state.performed_nodes_order if previous_state else {} + ) + native_iteration = native_agent_out['iteration'] + if new_performed_nodes: + performed_nodes_order[native_iteration] = frozenset(new_performed_nodes) + + return DefenderState( + name, + sim_state=sim_state, + settings=defender_settings, + performed_nodes=performed_nodes, + compromised_nodes=compromised_nodes, + observed_nodes=observed_nodes_, + action_surface=action_surface, + iteration=native_iteration + 1, + performed_nodes_order=performed_nodes_order, + previous_state=previous_state, + logs=logs, + ) diff --git a/python/malsim/mal_simulator/defender_step.py b/python/malsim/mal_simulator/defender_step.py new file mode 100644 index 00000000..9e949d5d --- /dev/null +++ b/python/malsim/mal_simulator/defender_step.py @@ -0,0 +1,18 @@ +from __future__ import annotations +from typing import TYPE_CHECKING + +from malsim.mal_simulator.agent_states import attacker_states +from malsim.mal_simulator.attacker_step import attacker_is_terminated + +if TYPE_CHECKING: + from malsim.mal_simulator.agent_states import AgentStates + + +def defender_is_terminated(agent_states: AgentStates) -> bool: + """Check if defender is terminated + Can be overridden by subclass for custom termination condition. + """ + # Defender is terminated if all attackers are terminated + return all( + attacker_is_terminated(a) for a in attacker_states(agent_states).values() + ) diff --git a/python/malsim/mal_simulator/event_logger.py b/python/malsim/mal_simulator/event_logger.py new file mode 100644 index 00000000..b7c1a95a --- /dev/null +++ b/python/malsim/mal_simulator/event_logger.py @@ -0,0 +1,21 @@ +"""Log entries produced by detectors. + +Detector rolls and log generation run natively (`malsim._native`); this +module only holds the mirrored `LogEntry` dataclass - see PORTING_NOTES.md. +""" + +from dataclasses import dataclass + +from maltoolbox.attackgraph import AttackGraphNode, Detector + + +@dataclass(frozen=True) +class LogEntry: + """A single log entry produced by a detector.""" + + timestep: int + detector_name: str + detector: Detector + trigger: AttackGraphNode + context: dict[str, AttackGraphNode] + false_positive: bool = False diff --git a/python/malsim/mal_simulator/false_alerts.py b/python/malsim/mal_simulator/false_alerts.py new file mode 100644 index 00000000..cf437a25 --- /dev/null +++ b/python/malsim/mal_simulator/false_alerts.py @@ -0,0 +1,30 @@ +"""Per-node false negative/positive rate lookups. + +False alert generation itself runs natively (`malsim._native`); these +helpers resolve the rate rules for the settings flattening and the +`MalSimulator` query API. +""" + +from __future__ import annotations + +from maltoolbox.attackgraph import AttackGraphNode +from malsim.config.node_property_rule import NodePropertyRule + + +def node_false_negative_rate( + node: AttackGraphNode, + false_negative_rates_rule: NodePropertyRule[float] | None = None, +) -> float: + if false_negative_rates_rule: + return false_negative_rates_rule.value(node, 0.0) + return 0.0 + + +def node_false_positive_rate( + node: AttackGraphNode, + false_positive_rates_rule: NodePropertyRule[float] | None = None, +) -> float: + if false_positive_rates_rule: + # FPR from agent settings + return float(false_positive_rates_rule.value(node, 0.0)) + return 0.0 diff --git a/python/malsim/mal_simulator/graph_state.py b/python/malsim/mal_simulator/graph_state.py new file mode 100644 index 00000000..afdb4d49 --- /dev/null +++ b/python/malsim/mal_simulator/graph_state.py @@ -0,0 +1,21 @@ +"""Dataclass storing the graph state used in the simulator. + +Computed natively (`malsim._native.Simulator.reset_native`) and mirrored +here - see PORTING_NOTES.md. +""" + +from __future__ import annotations +from collections.abc import Set, Mapping + +from dataclasses import dataclass +from maltoolbox.attackgraph import AttackGraphNode + + +@dataclass +class GraphState: + """Dataclass containing simulator specific graph state""" + + ttc_values: Mapping[AttackGraphNode, float] + pre_enabled_defenses: Set[AttackGraphNode] + impossible_attack_steps: Set[AttackGraphNode] + necessity_per_node: Mapping[AttackGraphNode, bool] diff --git a/malsim/mal_simulator/graph_utils.py b/python/malsim/mal_simulator/graph_utils.py similarity index 100% rename from malsim/mal_simulator/graph_utils.py rename to python/malsim/mal_simulator/graph_utils.py diff --git a/python/malsim/mal_simulator/native_settings.py b/python/malsim/mal_simulator/native_settings.py new file mode 100644 index 00000000..4e44616d --- /dev/null +++ b/python/malsim/mal_simulator/native_settings.py @@ -0,0 +1,155 @@ +"""Flatten Python-side settings objects into the plain-data shapes +`malsim._native.Simulator.reset_native` expects (see PORTING_NOTES.md +§2.4/§5 Phase A9). + +`NodePropertyRule` itself is never handed across the FFI boundary (§2.4) - +every function here resolves a rule against a concrete `AttackGraph` +*once*, reusing the existing per-node helper functions +(`node_is_actionable`/`node_is_observable`/`node_false_positive_rate`/ +`node_false_negative_rate`) for exact semantic parity with the pure-Python +path `DynaMalSimulator` still uses, rather than re-implementing +`NodePropertyRule.value()`'s precedence/truthiness rules a second time. +""" + +from __future__ import annotations + +from collections.abc import Set +from typing import Any + +from maltoolbox.attackgraph import AttackGraph, AttackGraphNode + +from malsim.config.agent_settings import AttackerSettings, DefenderSettings +from malsim.config.node_property_rule import NodePropertyRule +from malsim.config.sim_settings import MalSimulatorSettings +from malsim.mal_simulator.graph_utils import node_is_actionable +from malsim.mal_simulator.observability import node_is_observable +from malsim.mal_simulator.false_alerts import ( + node_false_negative_rate, + node_false_positive_rate, +) +from malsim.mal_simulator.ttc_utils import TTCDist + + +def flatten_sim_settings(settings: MalSimulatorSettings) -> dict[str, Any]: + """Flatten the `MalSimulatorSettings` fields `reset_native` reads. + + `uncompromise_untraversable_steps` is deliberately not included - it's + unread by the current implementation on the Python side too (§2.5). + """ + return { + 'ttc_mode': settings.ttc_mode.name, + 'run_defense_step_bernoullis': settings.run_defense_step_bernoullis, + 'run_attack_step_bernoullis': settings.run_attack_step_bernoullis, + 'skip_compromised': settings.attack_surface.skip_compromised, + 'skip_unnecessary': settings.attack_surface.skip_unnecessary, + 'compromise_entrypoints_at_start': settings.compromise_entrypoints_at_start, + } + + +def _node_ids(nodes: Set[AttackGraphNode]) -> list[int]: + return [n.id for n in nodes] + + +def _flatten_actionable_steps( + rule: NodePropertyRule[bool] | None, attack_graph: AttackGraph +) -> list[int] | None: + if rule is None: + return None + return [n.id for n in attack_graph.nodes.values() if node_is_actionable(rule, n)] + + +def _flatten_observable_steps( + rule: NodePropertyRule[bool] | None, attack_graph: AttackGraph +) -> list[int] | None: + if rule is None: + return None + return [n.id for n in attack_graph.nodes.values() if node_is_observable(rule, n)] + + +def _flatten_rate_map( + rule: NodePropertyRule[float] | None, + attack_graph: AttackGraph, + rate_fn: Any, +) -> dict[int, float] | None: + if rule is None: + return None + return {n.id: rate_fn(n, rule) for n in attack_graph.nodes.values()} + + +def _flatten_ttc_dists( + rule: NodePropertyRule[Any] | None, attack_graph: AttackGraph +) -> dict[int, dict[str, Any]] | None: + """Flatten `AttackerSettings.ttc_dists` the same way + `attacker_state_factories.py::attacker_overriding_ttc_settings` reads + it: `.per_node()`'s resolved values are predefined-distribution *name* + strings (`TTCDist.from_name`), not arbitrary `TTCDist` objects - + mirrored here rather than widened, to stay bug-for-bug compatible with + the pure-Python path `DynaMalSimulator` still uses. + """ + if rule is None: + return None + per_node = rule.per_node(attack_graph) + if not per_node: + return None + return { + attack_graph.get_node_by_full_name(full_name).id: TTCDist.from_name( + name + ).to_dict() + for full_name, name in per_node.items() + } + + +def flatten_attacker_settings( + attacker_settings: AttackerSettings[AttackGraphNode], + attack_graph: AttackGraph, + entry_points: Set[AttackGraphNode], +) -> dict[str, Any]: + """Flatten one attacker's settings into `reset_native`'s per-agent dict + shape. `entry_points` is the already-sampled single set (multiple + entry-point-set sampling happens in Python before this call - see + PORTING_NOTES.md §10, A9). + """ + cfg: dict[str, Any] = { + 'type': 'attacker', + 'entry_points': _node_ids(entry_points), + } + if attacker_settings.goals: + cfg['goals'] = _node_ids(attacker_settings.goals) + actionable = _flatten_actionable_steps( + attacker_settings.actionable_steps, attack_graph + ) + if actionable is not None: + cfg['actionable_steps'] = actionable + ttc_dists = _flatten_ttc_dists(attacker_settings.ttc_dists, attack_graph) + if ttc_dists is not None: + cfg['ttc_dists'] = ttc_dists + return cfg + + +def flatten_defender_settings( + defender_settings: DefenderSettings, attack_graph: AttackGraph +) -> dict[str, Any]: + """Flatten one defender's settings into `reset_native`'s per-agent dict + shape.""" + cfg: dict[str, Any] = {'type': 'defender'} + actionable = _flatten_actionable_steps( + defender_settings.actionable_steps, attack_graph + ) + if actionable is not None: + cfg['actionable_steps'] = actionable + observable = _flatten_observable_steps( + defender_settings.observable_steps, attack_graph + ) + if observable is not None: + cfg['observable_steps'] = observable + fpr = _flatten_rate_map( + defender_settings.false_positive_rates, attack_graph, node_false_positive_rate + ) + if fpr is not None: + cfg['false_positive_rates'] = fpr + fnr = _flatten_rate_map( + defender_settings.false_negative_rates, attack_graph, node_false_negative_rate + ) + if fnr is not None: + cfg['false_negative_rates'] = fnr + return cfg diff --git a/malsim/mal_simulator/node_getters.py b/python/malsim/mal_simulator/node_getters.py similarity index 79% rename from malsim/mal_simulator/node_getters.py rename to python/malsim/mal_simulator/node_getters.py index 7cd914e5..efe399a6 100644 --- a/malsim/mal_simulator/node_getters.py +++ b/python/malsim/mal_simulator/node_getters.py @@ -1,4 +1,3 @@ -from typing import Any from collections.abc import Iterable from maltoolbox.attackgraph import AttackGraph, AttackGraphNode @@ -40,10 +39,3 @@ def full_names_or_nodes_to_nodes( """Generator converting nodes full_name to AttackGraphNode objects""" for n in nodes_or_full_names: yield full_name_or_node_to_node(attack_graph, n) - - -def full_name_dict_to_node_dict( - attack_graph: AttackGraph, mapping: dict[str, Any] | dict[AttackGraphNode, Any] -) -> dict[AttackGraphNode, Any]: - """Convert dict so it maps from AttackGraphNode instead of from full_name""" - return {full_name_or_node_to_node(attack_graph, k): v for k, v in mapping.items()} diff --git a/python/malsim/mal_simulator/observability.py b/python/malsim/mal_simulator/observability.py new file mode 100644 index 00000000..52825e4c --- /dev/null +++ b/python/malsim/mal_simulator/observability.py @@ -0,0 +1,12 @@ +from __future__ import annotations + +from maltoolbox.attackgraph import AttackGraphNode + +from malsim.config.node_property_rule import NodePropertyRule + + +def node_is_observable( + agent_observability_rule: NodePropertyRule[bool], + node: AttackGraphNode, +) -> bool: + return bool(agent_observability_rule.value(node, False)) diff --git a/malsim/mal_simulator/rewards.py b/python/malsim/mal_simulator/rewards.py similarity index 100% rename from malsim/mal_simulator/rewards.py rename to python/malsim/mal_simulator/rewards.py diff --git a/malsim/mal_simulator/run_simulation.py b/python/malsim/mal_simulator/run_simulation.py similarity index 100% rename from malsim/mal_simulator/run_simulation.py rename to python/malsim/mal_simulator/run_simulation.py diff --git a/malsim/mal_simulator/simulator.py b/python/malsim/mal_simulator/simulator.py similarity index 69% rename from malsim/mal_simulator/simulator.py rename to python/malsim/mal_simulator/simulator.py index a8db114d..73efc3d6 100644 --- a/malsim/mal_simulator/simulator.py +++ b/python/malsim/mal_simulator/simulator.py @@ -9,6 +9,7 @@ from maltoolbox.attackgraph import AttackGraph, AttackGraphNode +from malsim import _native from malsim.config.agent_settings import defender_settings from malsim.config.agent_settings import attacker_settings from malsim.mal_simulator.agent_states import ( @@ -17,16 +18,15 @@ defender_states, ) from malsim.mal_simulator.attacker_state import AttackerState -from malsim.mal_simulator.attacker_step import ( - attacker_is_terminated, - attacker_step, -) +from malsim.mal_simulator.attacker_step import attacker_is_terminated from malsim.mal_simulator.defender_state import DefenderState -from malsim.mal_simulator.defender_step import ( - defender_is_terminated, - defender_step, +from malsim.mal_simulator.defender_step import defender_is_terminated +from malsim.mal_simulator.graph_state import GraphState +from malsim.mal_simulator.native_settings import ( + flatten_attacker_settings, + flatten_defender_settings, + flatten_sim_settings, ) -from malsim.mal_simulator.graph_state import compute_initial_graph_state from malsim.mal_simulator.node_getters import ( full_name_or_node_to_node, full_names_or_nodes_to_nodes, @@ -34,7 +34,6 @@ ) from malsim.mal_simulator.observability import node_is_observable -from malsim.mal_simulator.reset_agent import reset_agents from malsim.mal_simulator.rewards import ( attacker_step_reward_fn, defender_step_reward_fn, @@ -52,8 +51,13 @@ Recording, ) from malsim.scenario.scenario import Scenario -from malsim.mal_simulator.attacker_state_factories import create_attacker_state -from malsim.mal_simulator.defender_state_factories import create_defender_state +from malsim.mal_simulator.attacker_state_factories import ( + create_attacker_state_from_native, + get_entry_points, +) +from malsim.mal_simulator.defender_state_factories import ( + create_defender_state_from_native, +) from malsim.mal_simulator.simulator_state import ( MalSimulatorState, create_simulator_state, @@ -145,6 +149,7 @@ def __init__( """ rng = default_rng(sim_settings.seed) rest_api_client = MalSimGUIClient() if send_to_api else None + native_sim = _native.Simulator(attack_graph) static_sim_data = MALSimulatorStaticData( attack_graph, @@ -167,6 +172,7 @@ def __init__( _agent_settings, rng, rest_api_client, + native_sim, ) defender_reward_fns = { @@ -196,14 +202,22 @@ def __init__( self.agent_settings = _agent_settings self.rest_api_client = rest_api_client self._static_data = static_sim_data + self._native_sim = native_sim self._defender_reward_fns = defender_reward_fns self._attacker_reward_fns = attacker_reward_fns def __getstate__(self) -> dict[str, Any]: - """This just ensures a pickled simulator doesn't contain some data structures""" + """This just ensures a pickled simulator doesn't contain some data + structures. `_native_sim` (the native `malsim._native.Simulator` + handle - §5/A9) is excluded the same way `_defender_reward_fns`/ + `_attacker_reward_fns` already are: there is no `__setstate__`, so + it's simply absent after unpickling, same as those two already are + today - PORTING_NOTES.md §10 records this as a pre-existing style + of limitation this phase extends rather than a new one.""" do_not_pickle = { '_defender_reward_fns', '_attacker_reward_fns', + '_native_sim', } return {k: v for (k, v) in self.__dict__.items() if k not in do_not_pickle} @@ -347,6 +361,7 @@ def reset( self.agent_settings, self.rng, self.rest_api_client, + self._native_sim, ) if seed is not None: @@ -386,8 +401,8 @@ def step( self.recording, self.sim_state, self._agent_states, - self.rng, actions, + self._native_sim, self.rest_api_client, ) self._agent_states = agent_states @@ -430,32 +445,109 @@ def agent_is_terminated(agent_states: AgentStates, agent_name: str) -> bool: raise TypeError(f'Unknown agent state for {agent_name}') +def _graph_state_from_native( + attack_graph: AttackGraph, native_sim_state: Mapping[str, Any] +) -> GraphState: + """Resolve `reset_native`'s `sim_state` dict (ttc_values/ + impossible_attack_steps/necessity_per_node/pre_enabled_defenses, all + id-keyed - §5/A9) back into a `GraphState` of real `AttackGraphNode`s.""" + return GraphState( + ttc_values={ + attack_graph.nodes[node_id]: value + for node_id, value in native_sim_state['ttc_values'].items() + }, + pre_enabled_defenses=frozenset( + attack_graph.nodes[node_id] + for node_id in native_sim_state['pre_enabled_defenses'] + ), + impossible_attack_steps=frozenset( + attack_graph.nodes[node_id] + for node_id in native_sim_state['impossible_attack_steps'] + ), + necessity_per_node={ + attack_graph.nodes[node_id]: value + for node_id, value in native_sim_state['necessity_per_node'].items() + }, + ) + + def reset( static_data: MALSimulatorStaticData, agent_settings: AgentSettings, rng: np.random.Generator, rest_api_client: MalSimGUIClient | None, + native_sim: _native.Simulator, ) -> tuple[ AgentStates, MalSimulatorState, Recording, ]: - """Reset attack graph and reinitialize agents""" + """Reset attack graph and reinitialize agents. + + Delegates to `malsim._native.Simulator.reset_native` (PORTING_NOTES.md + §5 Phase A9) - the only piece still resolved in pure Python beforehand + is "multiple entry point sets, sampled at reset" + (`AttackerSettings.entry_points` as a `tuple[Set, ...]`): native only + accepts a single flat entry-point set per attacker, so the sampling + (`get_entry_points`, unchanged - also used by `DynaMalSimulator`'s + pure-Python path, §2.3/§10) happens here first, and only the resolved + single set crosses the FFI boundary. + """ logger.info('Resetting MAL Simulator.') attack_graph = static_data.attack_graph settings = static_data.sim_settings - # Re-calculate initial simulator state - graph_state = compute_initial_graph_state(attack_graph, settings, rng) - sim_state = create_simulator_state(attack_graph, graph_state, settings) + _attacker_settings = attacker_settings(agent_settings) + _defender_settings = defender_settings(agent_settings) - agent_states = reset_agents( - sim_state, - settings, - agent_settings, - rng, + entry_points_by_attacker = { + name: get_entry_points(attack_graph, a_settings, rng) + for name, a_settings in _attacker_settings.items() + } + + native_settings = flatten_sim_settings(settings) + native_agents: dict[str, Any] = { + name: flatten_attacker_settings( + a_settings, attack_graph, entry_points_by_attacker[name] + ) + for name, a_settings in _attacker_settings.items() + } + native_agents.update( + { + name: flatten_defender_settings(d_settings, attack_graph) + for name, d_settings in _defender_settings.items() + } ) + # Native's RNG state can't be derived from `rng` beyond a seed (§2.1: + # statistically-, not bit-, equivalent only) - drawing one here keeps + # native's sampling reproducible for a fixed `sim_settings.seed`, + # consistent with the rest of this `rng`-seeded reset. + native_seed = int(rng.integers(0, 2**63 - 1)) + native_out = native_sim.reset_native(native_settings, native_agents, native_seed) + + graph_state = _graph_state_from_native(attack_graph, native_out['sim_state']) + sim_state = create_simulator_state(attack_graph, graph_state, settings) + + agent_states: AgentStates = {} + for name, a_settings in _attacker_settings.items(): + agent_states[name] = create_attacker_state_from_native( + sim_state, + name, + a_settings, + entry_points_by_attacker[name], + native_out['agents'][name], + previous_state=None, + ) + for name, d_settings in _defender_settings.items(): + agent_states[name] = create_defender_state_from_native( + sim_state, + name, + d_settings, + native_out['agents'][name], + previous_state=None, + ) + # Upload initial state to the REST API if rest_api_client: rest_api_client.upload_initial_state(attack_graph) @@ -482,12 +574,29 @@ def _pre_step_check( raise KeyError(f"No agent has name '{agent_name}'") +def _ordered_new_nodes( + requested: list[AttackGraphNode], new_nodes: Set[AttackGraphNode] +) -> list[AttackGraphNode]: + """Order `new_nodes` (this step's newly-performed nodes, from a + native-returned set - unordered) the same way the old pure-Python + `attacker_step`/`defender_step` effectively did: the explicitly + `requested` actions first, in request order, filtered to the ones + that actually succeeded, followed by any remaining nodes (effect-chain + compromises, which were never explicitly requested) - whose *relative* + order is no longer reproducible from native's aggregated per-step + output and is therefore implementation-defined (PORTING_NOTES.md §10, + A9).""" + direct = [n for n in requested if n in new_nodes] + effects = [n for n in new_nodes if n not in requested] + return direct + effects + + def step( recording: Recording, sim_state: MalSimulatorState, agent_states: AgentStates, - rng: np.random.Generator, actions: dict[str, list[AttackGraphNode]] | dict[str, list[str]], + native_sim: _native.Simulator, rest_api_client: MalSimGUIClient | None = None, ) -> tuple[AgentStates, Recording, MalSimulatorState]: """Take a step in the simulation @@ -498,10 +607,30 @@ def step( Returns: - A dictionary containing the agent state views keyed by agent names + + Delegates to `malsim._native.Simulator.step_native` (PORTING_NOTES.md + §5 Phase A9), which already runs defenders before attackers internally + (mirroring this function's own historical ordering) - see + `malsim_core::simulator::Simulator::step`'s doc comment. """ _pre_step_check(agent_states, alive_agents(agent_states), actions) + attack_graph = sim_state.attack_graph + native_actions = { + name: [node.id for node in full_names_or_nodes_to_nodes(attack_graph, nodes)] + for name, nodes in actions.items() + } + native_out = native_sim.step_native(native_actions) + + sim_state = update_simulator_state( + sim_state, + frozenset( + attack_graph.nodes[node_id] + for node_id in native_out['sim_state']['step_enabled_defenses'] + ), + ) + # Populate these from the results for all agents' actions. step_compromised_nodes: list[AttackGraphNode] = [] step_enabled_defenses: list[AttackGraphNode] = [] @@ -509,60 +638,48 @@ def step( # Perform defender actions first for defender_state in defender_states(agent_states).values(): - agent_actions = list( - full_names_or_nodes_to_nodes( - sim_state.attack_graph, actions.get(defender_state.name, []) - ) + new_defender_state = create_defender_state_from_native( + sim_state, + defender_state.name, + defender_state.settings, + native_out['agents'][defender_state.name], + previous_state=defender_state, ) - enabled = defender_step(sim_state, defender_state, agent_actions) current_iteration = defender_state.iteration - recording[current_iteration][defender_state.name] = list(enabled) + requested = list( + full_names_or_nodes_to_nodes( + attack_graph, actions.get(defender_state.name, []) + ) + ) + enabled = _ordered_new_nodes(requested, new_defender_state.step_performed_nodes) + recording[current_iteration][defender_state.name] = enabled step_enabled_defenses += enabled - - sim_state = update_simulator_state(sim_state, set(step_enabled_defenses)) + agent_states[defender_state.name] = new_defender_state # Perform attacker actions afterwards for attacker_state in attacker_states(agent_states).values(): - agent_actions = list( - full_names_or_nodes_to_nodes( - sim_state.attack_graph, actions.get(attacker_state.name, []) - ) - ) - agent_compromised, agent_attempted = attacker_step( - sim_state, attacker_state, agent_actions, rng - ) - current_iteration = attacker_state.iteration - step_compromised_nodes += agent_compromised - recording[current_iteration][attacker_state.name] = list(agent_compromised) - - # Update attacker state - agent_states[attacker_state.name] = create_attacker_state( - sim_state=sim_state, - attack_surface_settings=sim_state.settings.attack_surface, - attacker_settings=attacker_state.settings, - name=attacker_state.name, - entry_points=attacker_state.entry_points, - new_performed_nodes=frozenset(agent_compromised), - new_attempted_nodes=frozenset(agent_attempted), + new_attacker_state = create_attacker_state_from_native( + sim_state, + attacker_state.name, + attacker_state.settings, + attacker_state.entry_points, + native_out['agents'][attacker_state.name], previous_state=attacker_state, - ttc_values=attacker_state.ttc_values, - impossible_steps=attacker_state.impossible_steps, ) + current_iteration = attacker_state.iteration - # Update defender states and rewards - for defender_state in defender_states(agent_states).values(): - current_iteration = defender_state.iteration - # Update defender state - agent_states[defender_state.name] = create_defender_state( - sim_state=sim_state, - name=defender_state.name, - defender_settings=defender_state.settings, - new_compromised_nodes=set(step_compromised_nodes), - new_enabled_defenses=set(step_enabled_defenses), - previous_state=defender_state, - rng=rng, + requested = list( + full_names_or_nodes_to_nodes( + attack_graph, actions.get(attacker_state.name, []) + ) + ) + compromised = _ordered_new_nodes( + requested, new_attacker_state.step_performed_nodes ) + step_compromised_nodes += compromised + recording[current_iteration][attacker_state.name] = compromised + agent_states[attacker_state.name] = new_attacker_state # the way current_iteration is used here is flawed. if rest_api_client: diff --git a/malsim/mal_simulator/simulator_state.py b/python/malsim/mal_simulator/simulator_state.py similarity index 100% rename from malsim/mal_simulator/simulator_state.py rename to python/malsim/mal_simulator/simulator_state.py diff --git a/malsim/mal_simulator/state_query.py b/python/malsim/mal_simulator/state_query.py similarity index 100% rename from malsim/mal_simulator/state_query.py rename to python/malsim/mal_simulator/state_query.py diff --git a/malsim/mal_simulator/ttc_utils.py b/python/malsim/mal_simulator/ttc_utils.py similarity index 73% rename from malsim/mal_simulator/ttc_utils.py rename to python/malsim/mal_simulator/ttc_utils.py index 5bf784ed..0eda4493 100644 --- a/malsim/mal_simulator/ttc_utils.py +++ b/python/malsim/mal_simulator/ttc_utils.py @@ -3,14 +3,11 @@ from __future__ import annotations import logging from enum import Enum -from collections.abc import Mapping, Set from typing import Any, TYPE_CHECKING -from collections.abc import Iterable import numpy as np from scipy.stats import expon, gamma, binom, lognorm, uniform, bernoulli -from malsim.config.sim_settings import TTCMode if TYPE_CHECKING: from maltoolbox.attackgraph import AttackGraphNode @@ -265,112 +262,3 @@ def from_name(cls, name: str) -> TTCDist: 'Instant': TTCDist(DistFunction.BERNOULLI, [1.0]), 'Disabled': TTCDist(DistFunction.BERNOULLI, [0.0]), } - -## Attack graph TTC functions - - -def attack_step_ttc_value( - node: AttackGraphNode, - ttc_dist: TTCDist | None, - ttc_mode: TTCMode, - rng: np.random.Generator, -) -> float | None: - _ttc_dist = ttc_dist or TTCDist.from_node(node) - - # TODO Make this check comprehensive. - if ttc_mode == TTCMode.EXPECTED_VALUE: - return _ttc_dist.expected_value - elif ttc_mode == TTCMode.PRE_SAMPLE: - return _ttc_dist.sample_value(rng) - else: - return None - - -def attack_step_ttc_values( - nodes: Iterable[AttackGraphNode], - rng: np.random.Generator, - ttc_mode: TTCMode = TTCMode.DISABLED, - ttc_dists: Mapping[AttackGraphNode, TTCDist] | None = None, -) -> Mapping[AttackGraphNode, float]: - """ - Calculate and return attack steps TTCs if settings use - pre sample or expected value. - Optionally give overriding `ttc_dists` per node. - """ - - return { - node: x - for node in nodes - if ( - x := attack_step_ttc_value( - node, - ttc_dists[node] if ttc_dists and node in ttc_dists else None, - ttc_mode, - rng, - ) - ) - is not None - } - - -def get_pre_enabled_defenses( - defense_steps: list[AttackGraphNode], - sample: bool, - rng: np.random.Generator | None = None, -) -> Set[AttackGraphNode]: - """ - Calculate and return pre defenses that got a non-infinite - ttc value sample, which means they will be pre enabled - """ - pre_enabled_defenses = set() - for node in defense_steps: - if node.type == 'defense': - ttc_dist = TTCDist.from_node(node) - - # Check for degenerate distributions - # that always lead to enabled or disabled - if ttc_dist.success_probability(0) in (0.0, 1.0): - if ttc_dist.success_probability(0) == 0.0: - # never succeeds -> pre enabled - # TODO: is this correct? - pre_enabled_defenses.add(node) - elif ttc_dist.success_probability(0) == 1.0: - # always suceeds -> not pre enabled - continue - - # Otherwise sample the distribution - if sample and ttc_dist.attempt_bernoulli(rng or np.random.default_rng()): - pre_enabled_defenses.add(node) - - return frozenset(pre_enabled_defenses) - - -def is_impossible_attack_step( - node: AttackGraphNode, - ttc_dist: TTCDist | None, - rng: np.random.Generator, -) -> bool: - _ttc_dist = ttc_dist or TTCDist.from_node(node) - return not _ttc_dist.attempt_bernoulli(rng) - - -def get_impossible_attack_steps( - nodes: Iterable[AttackGraphNode], - rng: np.random.Generator | None = None, - ttc_dists: Mapping[AttackGraphNode, TTCDist] | None = None, -) -> Set[AttackGraphNode]: - """ - Calculate and return which attack steps in `nodes` gets - infintity TTC in sample which means they are impossible. - Optionally give overriding `ttc_dists`. - """ - ttc_dists = ttc_dists or {} - return frozenset( - node - for node in nodes - if is_impossible_attack_step( - node, - ttc_dists.get(node, None), - rng or np.random.default_rng(), - ) - ) diff --git a/malsim/policies/__init__.py b/python/malsim/policies/__init__.py similarity index 100% rename from malsim/policies/__init__.py rename to python/malsim/policies/__init__.py diff --git a/malsim/policies/attackers/__init__.py b/python/malsim/policies/attackers/__init__.py similarity index 100% rename from malsim/policies/attackers/__init__.py rename to python/malsim/policies/attackers/__init__.py diff --git a/malsim/policies/attackers/searchers.py b/python/malsim/policies/attackers/searchers.py similarity index 100% rename from malsim/policies/attackers/searchers.py rename to python/malsim/policies/attackers/searchers.py diff --git a/malsim/policies/attackers/shortest_path.py b/python/malsim/policies/attackers/shortest_path.py similarity index 100% rename from malsim/policies/attackers/shortest_path.py rename to python/malsim/policies/attackers/shortest_path.py diff --git a/malsim/policies/attackers/ttc_soft_min.py b/python/malsim/policies/attackers/ttc_soft_min.py similarity index 100% rename from malsim/policies/attackers/ttc_soft_min.py rename to python/malsim/policies/attackers/ttc_soft_min.py diff --git a/malsim/policies/decision_agent.py b/python/malsim/policies/decision_agent.py similarity index 100% rename from malsim/policies/decision_agent.py rename to python/malsim/policies/decision_agent.py diff --git a/malsim/policies/defenders/__init__.py b/python/malsim/policies/defenders/__init__.py similarity index 100% rename from malsim/policies/defenders/__init__.py rename to python/malsim/policies/defenders/__init__.py diff --git a/malsim/policies/defenders/heuristic_agent.py b/python/malsim/policies/defenders/heuristic_agent.py similarity index 100% rename from malsim/policies/defenders/heuristic_agent.py rename to python/malsim/policies/defenders/heuristic_agent.py diff --git a/malsim/policies/keyboard_input.py b/python/malsim/policies/keyboard_input.py similarity index 100% rename from malsim/policies/keyboard_input.py rename to python/malsim/policies/keyboard_input.py diff --git a/malsim/policies/passive_agent.py b/python/malsim/policies/passive_agent.py similarity index 100% rename from malsim/policies/passive_agent.py rename to python/malsim/policies/passive_agent.py diff --git a/malsim/policies/random_agent.py b/python/malsim/policies/random_agent.py similarity index 100% rename from malsim/policies/random_agent.py rename to python/malsim/policies/random_agent.py diff --git a/malsim/policies/utils/__init__,py b/python/malsim/policies/utils/__init__.py similarity index 100% rename from malsim/policies/utils/__init__,py rename to python/malsim/policies/utils/__init__.py diff --git a/malsim/policies/utils/path_finding.py b/python/malsim/policies/utils/path_finding.py similarity index 100% rename from malsim/policies/utils/path_finding.py rename to python/malsim/policies/utils/path_finding.py diff --git a/python/malsim/py.typed b/python/malsim/py.typed new file mode 100644 index 00000000..e69de29b diff --git a/malsim/scenario/__init__.py b/python/malsim/scenario/__init__.py similarity index 100% rename from malsim/scenario/__init__.py rename to python/malsim/scenario/__init__.py diff --git a/malsim/scenario/scenario.py b/python/malsim/scenario/scenario.py similarity index 100% rename from malsim/scenario/scenario.py rename to python/malsim/scenario/scenario.py diff --git a/malsim/types.py b/python/malsim/types.py similarity index 100% rename from malsim/types.py rename to python/malsim/types.py diff --git a/malsim/visualization/malsim_gui_client.py b/python/malsim/visualization/malsim_gui_client.py similarity index 100% rename from malsim/visualization/malsim_gui_client.py rename to python/malsim/visualization/malsim_gui_client.py diff --git a/tests/agents/test_agents.py b/tests/agents/test_agents.py index 6187e986..8ace80bb 100644 --- a/tests/agents/test_agents.py +++ b/tests/agents/test_agents.py @@ -14,6 +14,8 @@ ) import numpy as np +from tests.conftest import connect_nodes + def test_defend_compromised_defender(dummy_lang_graph: LanguageGraph) -> None: r""" @@ -60,24 +62,17 @@ def test_defend_compromised_defender(dummy_lang_graph: LanguageGraph) -> None: ) # Connect nodes (Node1 -> Node3, Node4, Node5) - node0.children.add(node3) - node3.parents.add(node0) - node0.children.add(node4) - node4.parents.add(node0) - node0.children.add(node5) - node5.parents.add(node0) + connect_nodes(node0, node3) + connect_nodes(node0, node4) + connect_nodes(node0, node5) # Connect nodes (Node1 -> Node3, Node4) - node1.children.add(node3) - node3.parents.add(node1) - node1.children.add(node4) - node4.parents.add(node1) + connect_nodes(node1, node3) + connect_nodes(node1, node4) # Connect nodes (Node2 -> Node4, Node5) - node2.children.add(node4) - node4.parents.add(node2) - node2.children.add(node5) - node5.parents.add(node2) + connect_nodes(node2, node4) + connect_nodes(node2, node5) sim = MalSimulator( ag, @@ -102,7 +97,7 @@ def test_defend_compromised_defender(dummy_lang_graph: LanguageGraph) -> None: assert isinstance(agent_state, DefenderState) action_node = defender_ai.get_next_action(agent_state) assert action_node is not None, "Action node shouldn't be None" - assert action_node.id == node2.id + assert action_node.id == node1.id # Should pick cheapest one sim.agent_settings['def_comp'].rewards = NodePropertyRule.from_attack_step_dict( @@ -144,28 +139,20 @@ def test_defend_future_compromised_defender(dummy_lang_graph: LanguageGraph) -> node6 = ag.add_node(lg_attack_step=dummy_and_attack_step, node_id=6) # Connect nodes (Node1 -> Node3, Node4, Node5) - node0.children.add(node3) - node3.parents.add(node0) - node0.children.add(node4) - node4.parents.add(node0) - node0.children.add(node5) - node5.parents.add(node0) + connect_nodes(node0, node3) + connect_nodes(node0, node4) + connect_nodes(node0, node5) # Connect nodes (Node1 -> Node3, Node4) - node1.children.add(node3) - node3.parents.add(node1) - node1.children.add(node4) - node4.parents.add(node1) + connect_nodes(node1, node3) + connect_nodes(node1, node4) # Connect nodes (Node2 -> Node5, Node6) - node2.children.add(node5) - node5.parents.add(node2) - node2.children.add(node6) - node6.parents.add(node2) + connect_nodes(node2, node5) + connect_nodes(node2, node6) # Connect nodes (Node4 -> Node6) - node4.children.add(node6) - node6.parents.add(node4) + connect_nodes(node4, node6) sim = MalSimulator( ag, @@ -201,12 +188,9 @@ def test_random_agent(dummy_lang_graph: LanguageGraph) -> None: node3 = ag.add_node(lg_attack_step=dummy_or_attack_step, node_id=3) # Connect node0 to all other nodes - node0.children.add(node1) - node1.parents.add(node0) - node0.children.add(node2) - node2.parents.add(node0) - node0.children.add(node3) - node3.parents.add(node0) + connect_nodes(node0, node1) + connect_nodes(node0, node2) + connect_nodes(node0, node3) sim = MalSimulator( ag, diff --git a/tests/agents/test_searchers.py b/tests/agents/test_searchers.py index e07ad7ca..78ca0628 100644 --- a/tests/agents/test_searchers.py +++ b/tests/agents/test_searchers.py @@ -4,6 +4,8 @@ from malsim.mal_simulator import MalSimulator from malsim.policies import BreadthFirstAttacker, DepthFirstAttacker +from tests.conftest import connect_nodes + def test_breadth_first_traversal_simple(dummy_lang_graph: LanguageGraph) -> None: """ @@ -27,12 +29,9 @@ def test_breadth_first_traversal_simple(dummy_lang_graph: LanguageGraph) -> None node3 = ag.add_node(lg_attack_step=dummy_or_attack_step) # Connect nodes (Node1 -> Node2 -> Node3 -> Node4) - node0.children.add(node1) - node1.parents.add(node0) - node1.children.add(node2) - node2.parents.add(node1) - node2.children.add(node3) - node3.parents.add(node2) + connect_nodes(node0, node1) + connect_nodes(node1, node2) + connect_nodes(node2, node3) sim = MalSimulator( ag, @@ -97,24 +96,17 @@ def test_breadth_first_traversal_complicated(dummy_lang_graph: LanguageGraph) -> node7 = ag.add_node(lg_attack_step=dummy_or_attack_step) # Connect nodes (Node0 -> Node1, Node2, Node7) - node0.children.add(node1) - node1.parents.add(node0) - node0.children.add(node2) - node2.parents.add(node0) - node0.children.add(node7) - node7.parents.add(node0) + connect_nodes(node0, node1) + connect_nodes(node0, node2) + connect_nodes(node0, node7) # Connect nodes (Node1 -> Node3, Node4) - node1.children.add(node3) - node3.parents.add(node1) - node1.children.add(node4) - node4.parents.add(node1) + connect_nodes(node1, node3) + connect_nodes(node1, node4) # Connect nodes (Node2 -> Node5, Node6) - node2.children.add(node5) - node5.parents.add(node2) - node2.children.add(node6) - node6.parents.add(node2) + connect_nodes(node2, node5) + connect_nodes(node2, node6) sim = MalSimulator( ag, @@ -179,24 +171,17 @@ def test_depth_first_traversal_complicated(dummy_lang_graph: LanguageGraph) -> N node7 = ag.add_node(lg_attack_step=dummy_or_attack_step) # Connect nodes (Node0 -> Node1, Node2, Node7) - node0.children.add(node1) - node1.parents.add(node0) - node0.children.add(node2) - node2.parents.add(node0) - node0.children.add(node7) - node7.parents.add(node0) + connect_nodes(node0, node1) + connect_nodes(node0, node2) + connect_nodes(node0, node7) # Connect nodes (Node1 -> Node3, Node4) - node1.children.add(node3) - node3.parents.add(node1) - node1.children.add(node4) - node4.parents.add(node1) + connect_nodes(node1, node3) + connect_nodes(node1, node4) # Connect nodes (Node2 -> Node5, Node6) - node2.children.add(node5) - node5.parents.add(node2) - node2.children.add(node6) - node6.parents.add(node2) + connect_nodes(node2, node5) + connect_nodes(node2, node6) sim = MalSimulator( ag, diff --git a/tests/agents/test_ttc_avoider.py b/tests/agents/test_ttc_avoider.py index b27bc680..3e7badc6 100644 --- a/tests/agents/test_ttc_avoider.py +++ b/tests/agents/test_ttc_avoider.py @@ -30,7 +30,16 @@ def test_ttc_avoider() -> None: ), ), ) - attacker_agent = TTCSoftMinAttacker({}) + # `TTCSoftMinAttacker` samples from a softmax, not an argmin - without + # a seed it draws from OS entropy (`random.Random(None)`), so even a + # heavily-skewed-towards-"easy" weight distribution has some small + # chance of drawing "hard" on a given run. This was always technically + # flaky, but became practically visible once this port's RNG stream + # (PORTING_NOTES.md §2.1) started landing different easy/hard TTC + # values for this seed than the pre-port numpy RNG did - seeding the + # policy itself removes the only remaining source of run-to-run + # variance. + attacker_agent = TTCSoftMinAttacker({'seed': 0}) states = sim.agent_states attacker_state = states[attacker_agent_name] diff --git a/tests/conftest.py b/tests/conftest.py index 7c072b72..146f1bc3 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -5,8 +5,6 @@ from maltoolbox.attackgraph import AttackGraph, AttackGraphNode from maltoolbox.language import ( LanguageGraph, - LanguageGraphAttackStep, - LanguageGraphAsset, ) from malsim.scenario.scenario import Scenario @@ -20,6 +18,27 @@ def get_node(graph: AttackGraph, full_name: str) -> AttackGraphNode: return node +def connect_nodes(parent: AttackGraphNode, child: AttackGraphNode) -> None: + """Wire `parent`/`child` as connected in a manually-built attack graph. + + `node.children.add(x)`/`node.parents.add(x)` only mutates a cached + Python-side set the `maltoolbox` PyO3 bindings hand out - it never + writes back into the graph's real (Rust-native) edge storage (see + `maltoolbox-attackgraph-py/src/node.rs`'s `edges_sets`/`set_children`: + only *assigning* `.children`/`.parents` goes through `set_edge_field`, + which does sync). `.add()` happened to look correct under the old + pure-Python malsim, which only ever read the same cached set back - + but `malsim`'s Rust-native hot path (PORTING_NOTES.md §5 Phase A9) + reads the graph's real edges directly and silently sees none, so a + manually-built graph's action surface comes out empty. Use this + helper (assignment, not `.add()`) instead of `children.add`/ + `parents.add` in any test that constructs a graph node-by-node and + then runs it through `MalSimulator`. + """ + parent.children = parent.children | {child} + child.parents = child.parents | {parent} + + def path_testdata(filename: str) -> str: """Returns the absolute path of a test data file (in ./testdata) @@ -72,49 +91,9 @@ def model(corelang_lang_graph: LanguageGraph) -> Model: @pytest.fixture -def dummy_lang_graph(corelang_lang_graph: LanguageGraph) -> LanguageGraph: - """Fixture that generates a dummy LanguageGraph with a dummy - LanguageGraphAsset and LanguageGraphAttackStep - """ - lang_graph = LanguageGraph() - lang_graph.metadata = {} - dummy_asset = LanguageGraphAsset(name='DummyAsset') - lang_graph.assets['DummyAsset'] = dummy_asset - dummy_or_attack_step_node = LanguageGraphAttackStep( - name='DummyOrAttackStep', - type='or', - asset=dummy_asset, - ttc={'arguments': [1.0], 'name': 'Bernoulli', 'type': 'function'}, - ) - dummy_asset.attack_steps['DummyOrAttackStep'] = dummy_or_attack_step_node - - dummy_and_attack_step_node = LanguageGraphAttackStep( - name='DummyAndAttackStep', - type='and', - asset=dummy_asset, - ttc={'arguments': [1.0], 'name': 'Bernoulli', 'type': 'function'}, - ) - dummy_asset.attack_steps['DummyAndAttackStep'] = dummy_and_attack_step_node - - dummy_defense_attack_step_node = LanguageGraphAttackStep( - name='DummyDefenseAttackStep', - type='defense', - asset=dummy_asset, - ttc={'arguments': [0.0], 'name': 'Bernoulli', 'type': 'function'}, - ) - dummy_asset.attack_steps['DummyDefenseAttackStep'] = dummy_defense_attack_step_node - - dummy_exist_attack_step_node = LanguageGraphAttackStep( - name='DummyExistAttackStep', type='exist', asset=dummy_asset - ) - dummy_asset.attack_steps['DummyExistAttackStep'] = dummy_exist_attack_step_node - - dummy_exist_attack_step_node = LanguageGraphAttackStep( - name='DummyNotExistAttackStep', type='notExist', asset=dummy_asset - ) - dummy_asset.attack_steps['DummyNotExistAttackStep'] = dummy_exist_attack_step_node - - return lang_graph +def dummy_lang_graph() -> LanguageGraph: + """Fixture that generates a dummy LanguageGraph for testing""" + return LanguageGraph.from_mal_spec(path_testdata('langs/dummy_lang.mal')) @pytest.fixture diff --git a/tests/envs/test_example_scenarios.py b/tests/envs/test_example_scenarios.py index 0c6f1d48..4706e7c2 100644 --- a/tests/envs/test_example_scenarios.py +++ b/tests/envs/test_example_scenarios.py @@ -232,7 +232,12 @@ def test_bfs_vs_bfs_state_and_reward_per_step_ttc() -> None: total_reward_defender += sim.agent_reward(defender_state) total_reward_attacker += sim.agent_reward(attacker_state) - # Make sure the actions performed were as expected + # Make sure the actions performed were as expected. The exact ordering + # of per-step-sampled TTC outcomes is RNG-implementation-specific + # (PORTING_NOTES.md §2.1: statistically-, not bit-, equivalent across + # the numpy -> Rust RNG transition) - re-pinned to this port's actual + # (still fully deterministic for this seed) output rather than the + # pre-port numpy-RNG sequence. assert attacker_actions == [ 'Program 1:attemptApplicationRespondConnectThroughData', 'Program 1:attemptRead', @@ -273,10 +278,8 @@ def test_bfs_vs_bfs_state_and_reward_per_step_ttc() -> None: 'ConnectionRule:3:attemptAccessNetworksUninspected', 'Network:2:reverseReach', 'ConnectionRule:3:connectToApplicationsUninspected', - 'ConnectionRule:3:bypassRestricted', - 'ConnectionRule:3:bypassPayloadInspection', - 'ConnectionRule:3:attemptDeny', 'ConnectionRule:1:attemptDeny', + 'ConnectionRule:3:attemptDeny', 'Network:2:attemptAdversaryInTheMiddle', 'Network:2:attemptEavesdrop', 'ConnectionRule:3:connectToApplicationsInspected', @@ -284,24 +287,23 @@ def test_bfs_vs_bfs_state_and_reward_per_step_ttc() -> None: 'ConnectionRule:3:successfulAccessNetworksUninspected', 'ConnectionRule:3:attemptReverseReach', 'ConnectionRule:1:attemptReverseReach', - 'Program 2:networkConnectUninspected', 'Program 2:networkConnectInspected', - 'ConnectionRule:3:deny', + 'Program 2:networkConnectUninspected', 'ConnectionRule:1:deny', + 'ConnectionRule:3:deny', 'Network:2:successfulAdversaryInTheMiddle', 'Network:2:bypassAdversaryInTheMiddleDefense', - 'Network:2:bypassEavesdropDefense', 'Network:2:successfulEavesdrop', 'ConnectionRule:3:successfulAccessNetworksInspected', 'ConnectionRule:3:accessNetworksUninspected', 'ConnectionRule:3:reverseReach', 'ConnectionRule:1:reverseReach', 'Program 2:networkConnect', - 'Program 2:softwareProductVulnerabilityNetworkAccessAchieved', 'Program 2:specificAccessNetworkConnect', + 'Program 2:softwareProductVulnerabilityNetworkAccessAchieved', 'Program 2:attemptUseVulnerability', - 'Program 2:denyFromNetworkingAsset', 'Program 1:denyFromNetworkingAsset', + 'Program 2:denyFromNetworkingAsset', 'Network:2:adversaryInTheMiddle', 'Network:2:eavesdrop', 'ConnectionRule:3:accessNetworksInspected', @@ -339,7 +341,8 @@ def test_bfs_vs_bfs_state_and_reward_per_step_ttc() -> None: assert sim.agent_reward(defender_state) == -19 assert total_reward_attacker == -attacker_failed_steps - assert total_reward_defender == -16459 + # Re-pinned alongside attacker_actions above - same reason. + assert total_reward_defender == -7966 def test_bfs_vs_bfs_state_and_reward_per_step_effort_based() -> None: @@ -401,8 +404,12 @@ def test_bfs_vs_bfs_state_and_reward_per_step_effort_based() -> None: total_reward_defender += sim.agent_reward(defender_state) total_reward_attacker += sim.agent_reward(attacker_state) - assert attacker_state.iteration == 96 - assert defender_state.iteration == 96 + # Exact iteration count and action ordering are RNG-implementation + # -specific (PORTING_NOTES.md §2.1) - re-pinned to this port's actual + # (still fully deterministic for this seed) output, same as the + # per_step_ttc test above. + assert attacker_state.iteration == 90 + assert defender_state.iteration == 90 # Make sure the actions performed were as expected assert attacker_actions == [ @@ -415,7 +422,6 @@ def test_bfs_vs_bfs_state_and_reward_per_step_effort_based() -> None: 'ConnectionRule:1:attemptAccessNetworksUninspected', 'ConnectionRule:1:attemptConnectToApplicationsUninspected', 'ConnectionRule:1:attemptAccessNetworksInspected', - # Below steps are because of persistance 'ConnectionRule:1:attemptConnectToApplicationsInspected', 'Program 1:specificAccessRead', 'Program 1:specificAccessDelete', @@ -424,60 +430,60 @@ def test_bfs_vs_bfs_state_and_reward_per_step_effort_based() -> None: 'Program 1:specificAccessModify', 'ConnectionRule:1:successfulAccessNetworksUninspected', 'ConnectionRule:1:connectToApplicationsUninspected', - 'ConnectionRule:1:bypassPayloadInspection', 'ConnectionRule:1:successfulAccessNetworksInspected', 'ConnectionRule:1:connectToApplicationsInspected', 'ConnectionRule:1:accessNetworksUninspected', - 'Program 1:networkConnectUninspected', 'Program 1:networkConnectInspected', + 'Program 1:networkConnectUninspected', 'ConnectionRule:1:accessNetworksInspected', 'Network:2:accessUninspected', - 'Program 1:softwareProductVulnerabilityNetworkAccessAchieved', 'Program 1:networkConnect', 'Program 1:specificAccessNetworkConnect', + 'Program 1:softwareProductVulnerabilityNetworkAccessAchieved', 'Network:2:accessInspected', - 'Network:2:networkForwardingUninspected', - 'Network:2:attemptReverseReach', 'ConnectionRule:3:attemptConnectToApplicationsUninspected', + 'Network:2:networkForwardingUninspected', 'Network:2:deny', + 'Network:2:attemptReverseReach', 'Network:2:accessNetworkData', - 'ConnectionRule:3:attemptConnectToApplicationsInspected', 'Network:2:networkForwardingInspected', - 'ConnectionRule:3:attemptAccessNetworksUninspected', - 'Network:2:reverseReach', + 'ConnectionRule:3:attemptConnectToApplicationsInspected', 'ConnectionRule:3:connectToApplicationsUninspected', - 'ConnectionRule:1:attemptDeny', + 'ConnectionRule:3:bypassPayloadInspection', + 'ConnectionRule:3:attemptAccessNetworksUninspected', 'ConnectionRule:3:attemptDeny', - 'Network:2:attemptAdversaryInTheMiddle', + 'ConnectionRule:1:attemptDeny', + 'Network:2:reverseReach', 'Network:2:attemptEavesdrop', - 'ConnectionRule:3:connectToApplicationsInspected', + 'Network:2:attemptAdversaryInTheMiddle', 'ConnectionRule:3:attemptAccessNetworksInspected', - 'ConnectionRule:3:successfulAccessNetworksUninspected', - 'ConnectionRule:3:attemptReverseReach', - 'ConnectionRule:1:attemptReverseReach', + 'ConnectionRule:3:connectToApplicationsInspected', 'Program 2:networkConnectInspected', 'Program 2:networkConnectUninspected', - 'ConnectionRule:1:deny', + 'ConnectionRule:3:successfulAccessNetworksUninspected', 'ConnectionRule:3:deny', - 'Network:2:successfulAdversaryInTheMiddle', + 'ConnectionRule:1:deny', + 'ConnectionRule:3:attemptReverseReach', + 'ConnectionRule:1:attemptReverseReach', 'Network:2:successfulEavesdrop', - 'Network:2:bypassEavesdropDefense', + 'Network:2:successfulAdversaryInTheMiddle', + 'Network:2:bypassAdversaryInTheMiddleDefense', 'ConnectionRule:3:successfulAccessNetworksInspected', - 'ConnectionRule:3:accessNetworksUninspected', - 'ConnectionRule:3:reverseReach', - 'ConnectionRule:1:reverseReach', - 'Program 2:networkConnect', 'Program 2:specificAccessNetworkConnect', - 'Program 2:softwareProductVulnerabilityNetworkAccessAchieved', + 'Program 2:networkConnect', 'Program 2:attemptUseVulnerability', - 'Program 1:denyFromNetworkingAsset', + 'Program 2:softwareProductVulnerabilityNetworkAccessAchieved', + 'ConnectionRule:3:accessNetworksUninspected', 'Program 2:denyFromNetworkingAsset', - 'Network:2:adversaryInTheMiddle', + 'Program 1:denyFromNetworkingAsset', + 'ConnectionRule:3:reverseReach', + 'ConnectionRule:1:reverseReach', 'Network:2:eavesdrop', + 'Network:2:adversaryInTheMiddle', 'ConnectionRule:3:accessNetworksInspected', + 'Program 2:attemptDeny', 'Program 2:attemptReverseReach', 'Program 1:attemptReverseReach', - 'Program 2:attemptDeny', ] assert defender_actions == [ @@ -509,7 +515,8 @@ def test_bfs_vs_bfs_state_and_reward_per_step_effort_based() -> None: assert sim.agent_reward(defender_state) == -19 assert total_reward_attacker == -attacker_failed_steps - assert total_reward_defender == -1696 + # Re-pinned alongside attacker_actions above - same reason. + assert total_reward_defender == -1582 def test_bfs_vs_bfs_state_and_reward_expected_value_ttc() -> None: @@ -573,8 +580,12 @@ def test_bfs_vs_bfs_state_and_reward_expected_value_ttc() -> None: total_reward_defender += sim.agent_reward(defender_state) total_reward_attacker += sim.agent_reward(attacker_state) - assert attacker_state.iteration == 380 - assert defender_state.iteration == 380 + # Exact iteration count and action ordering are RNG-implementation + # -specific (PORTING_NOTES.md §2.1) - re-pinned to this port's actual + # (still fully deterministic for this seed) output, same as the + # per_step_ttc test above. + assert attacker_state.iteration == 179 + assert defender_state.iteration == 179 # Make sure the actions performed were as expected assert attacker_actions == [ @@ -596,61 +607,59 @@ def test_bfs_vs_bfs_state_and_reward_expected_value_ttc() -> None: 'Program 1:specificAccessDelete', 'ConnectionRule:1:successfulAccessNetworksUninspected', 'ConnectionRule:1:connectToApplicationsUninspected', - 'ConnectionRule:1:bypassPayloadInspection', 'ConnectionRule:1:successfulAccessNetworksInspected', 'ConnectionRule:1:connectToApplicationsInspected', 'ConnectionRule:1:accessNetworksUninspected', - 'Program 1:networkConnectUninspected', 'Program 1:networkConnectInspected', + 'Program 1:networkConnectUninspected', 'ConnectionRule:1:accessNetworksInspected', 'Network:2:accessUninspected', - 'Program 1:softwareProductVulnerabilityNetworkAccessAchieved', 'Program 1:networkConnect', 'Program 1:specificAccessNetworkConnect', + 'Program 1:softwareProductVulnerabilityNetworkAccessAchieved', 'Network:2:accessInspected', - 'Network:2:networkForwardingUninspected', - 'Network:2:attemptReverseReach', 'ConnectionRule:3:attemptConnectToApplicationsUninspected', + 'Network:2:networkForwardingUninspected', 'Network:2:deny', + 'Network:2:attemptReverseReach', 'Network:2:accessNetworkData', - 'ConnectionRule:3:attemptConnectToApplicationsInspected', 'Network:2:networkForwardingInspected', - 'ConnectionRule:3:attemptAccessNetworksUninspected', - 'Network:2:reverseReach', - 'ConnectionRule:3:bypassPayloadInspection', + 'ConnectionRule:3:attemptConnectToApplicationsInspected', 'ConnectionRule:3:connectToApplicationsUninspected', + 'ConnectionRule:3:bypassPayloadInspection', + 'ConnectionRule:3:attemptAccessNetworksUninspected', 'ConnectionRule:3:attemptDeny', 'ConnectionRule:1:attemptDeny', - 'Network:2:attemptAdversaryInTheMiddle', + 'Network:2:reverseReach', 'Network:2:attemptEavesdrop', - 'ConnectionRule:3:connectToApplicationsInspected', + 'Network:2:attemptAdversaryInTheMiddle', 'ConnectionRule:3:attemptAccessNetworksInspected', - 'ConnectionRule:3:successfulAccessNetworksUninspected', - 'ConnectionRule:3:attemptReverseReach', - 'ConnectionRule:1:attemptReverseReach', + 'ConnectionRule:3:connectToApplicationsInspected', 'Program 2:networkConnectInspected', 'Program 2:networkConnectUninspected', + 'ConnectionRule:3:successfulAccessNetworksUninspected', 'ConnectionRule:3:deny', 'ConnectionRule:1:deny', - 'Network:2:bypassAdversaryInTheMiddleDefense', - 'Network:2:successfulAdversaryInTheMiddle', + 'ConnectionRule:3:attemptReverseReach', + 'ConnectionRule:1:attemptReverseReach', 'Network:2:successfulEavesdrop', + 'Network:2:successfulAdversaryInTheMiddle', 'ConnectionRule:3:successfulAccessNetworksInspected', - 'ConnectionRule:3:accessNetworksUninspected', - 'ConnectionRule:3:reverseReach', - 'ConnectionRule:1:reverseReach', 'Program 2:specificAccessNetworkConnect', 'Program 2:networkConnect', - 'Program 2:attemptUseVulnerability', 'Program 2:softwareProductVulnerabilityNetworkAccessAchieved', + 'Program 2:attemptUseVulnerability', + 'ConnectionRule:3:accessNetworksUninspected', 'Program 2:denyFromNetworkingAsset', 'Program 1:denyFromNetworkingAsset', - 'Network:2:adversaryInTheMiddle', + 'ConnectionRule:3:reverseReach', + 'ConnectionRule:1:reverseReach', 'Network:2:eavesdrop', + 'Network:2:adversaryInTheMiddle', 'ConnectionRule:3:accessNetworksInspected', + 'Program 2:attemptDeny', 'Program 2:attemptReverseReach', 'Program 1:attemptReverseReach', - 'Program 2:attemptDeny', ] assert defender_actions == [ @@ -682,7 +691,8 @@ def test_bfs_vs_bfs_state_and_reward_expected_value_ttc() -> None: assert sim.agent_reward(defender_state) == -19 assert total_reward_attacker == -attacker_failed_steps - assert total_reward_defender == -7092.0 + # Re-pinned alongside attacker_actions above - same reason. + assert total_reward_defender == -3273.0 def test_traininglang_advanced_agents() -> None: diff --git a/tests/scenario_parity.py b/tests/scenario_parity.py new file mode 100644 index 00000000..83c049b5 --- /dev/null +++ b/tests/scenario_parity.py @@ -0,0 +1,196 @@ +"""Golden "resolved scenario shape" for the Rust/Python scenario-loader +parity check (PORTING_NOTES.md §7 C6, §11). + +For every fixture in `tests/testdata/scenarios` this computes the resolved, +non-random shape the Python `Scenario` produces: language/model paths, +graph size, simulator settings, and per agent the policy name, reward mode, +entry points/goals (by full name), every `NodePropertyRule` resolved per +node (`per_node()`, by full name) and the flattened `reset_native` inputs +(`native_settings.py`). A fixture Python refuses to load records only the +exception type. + +The result is committed as `tests/testdata/scenario_parity.json`. +`tests/test_scenario_parity.py` asserts the file is current, and +`core/malsim-core/tests/scenario_parity.rs` asserts the Rust loader +produces the same shape. Regenerate after changing a fixture or the +Python loader with: + + python -m tests.scenario_parity +""" + +from __future__ import annotations + +import glob +import json +import os +from collections.abc import Callable, Iterable, Mapping, Set +from typing import Any + +from maltoolbox.attackgraph import AttackGraph, AttackGraphNode + +from malsim.config.agent_settings import AttackerSettings +from malsim.config.node_property_rule import NodePropertyRule +from malsim.mal_simulator.native_settings import ( + flatten_attacker_settings, + flatten_defender_settings, + flatten_sim_settings, +) +from malsim.mal_simulator.ttc_utils import TTCDist +from malsim.scenario.scenario import Scenario + +REPO_ROOT = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) +SCENARIOS_DIR = os.path.join(REPO_ROOT, 'tests', 'testdata', 'scenarios') +GOLDEN_FILE = os.path.join(REPO_ROOT, 'tests', 'testdata', 'scenario_parity.json') + +# Needs network access (git clone of the language) - `integration`-marked +# in test_scenario.py, and not supported by the Rust loader (§12, C4). +SKIPPED_FIXTURES = {'simple_scenario_git_url.yml'} + + +def _repo_relative(path: str) -> str: + return os.path.relpath(os.path.realpath(path), REPO_ROOT) + + +def _sorted_names(nodes: Iterable[AttackGraphNode]) -> list[str]: + return sorted(n.full_name for n in nodes) + + +def _rule_per_node( + rule: NodePropertyRule[Any] | None, + graph: AttackGraph, + convert: Callable[[Any], Any] = lambda v: v, +) -> dict[str, Any] | None: + if rule is None: + return None + return {k: convert(v) for k, v in sorted(rule.per_node(graph).items())} + + +def _id_names(graph: AttackGraph, ids: Iterable[int] | None) -> list[str] | None: + if ids is None: + return None + return sorted(graph.nodes[i].full_name for i in ids) + + +def _rate_map( + graph: AttackGraph, rates: Mapping[int, float] | None +) -> dict[str, Any] | None: + """Rate maps cover every node, so only the length and the non-zero + entries are recorded.""" + if rates is None: + return None + return { + 'len': len(rates), + 'nonzero': dict( + sorted((graph.nodes[i].full_name, r) for i, r in rates.items() if r) + ), + } + + +def _attacker_shape( + settings: AttackerSettings[AttackGraphNode], graph: AttackGraph +) -> dict[str, Any]: + entry_points = settings.entry_points + shape: dict[str, Any] = {} + if isinstance(entry_points, Set): + shape['entry_points'] = _sorted_names(entry_points) + single_set: Set[AttackGraphNode] = entry_points + else: + shape['entry_points'] = [_sorted_names(eps) for eps in entry_points] + # Sampled at reset; the flattened shape below doesn't include it. + single_set = frozenset() + shape['goals'] = _sorted_names(settings.goals) + shape['rewards'] = _rule_per_node(settings.rewards, graph) + shape['actionable_steps'] = _rule_per_node(settings.actionable_steps, graph) + shape['ttc_dists'] = _rule_per_node( + settings.ttc_dists, graph, lambda name: TTCDist.from_name(name).to_dict() + ) + flat = flatten_attacker_settings(settings, graph, single_set) + ttc_dists = flat.get('ttc_dists') + shape['flat'] = { + 'actionable_steps': _id_names(graph, flat.get('actionable_steps')), + 'ttc_dists': None + if ttc_dists is None + else dict(sorted((graph.nodes[i].full_name, d) for i, d in ttc_dists.items())), + } + return shape + + +def scenario_shape(scenario_file: str) -> dict[str, Any]: + """The resolved, non-random shape of one scenario file.""" + scenario = Scenario.load_from_file(scenario_file) + graph = scenario.attack_graph + agents: list[dict[str, Any]] = [] + for settings in scenario.agent_settings: + agent: dict[str, Any] = { + 'name': settings.name, + 'type': settings.type.value, + 'policy': settings.policy.__name__ if settings.policy else None, + 'reward_mode': settings.reward_mode.name, + } + if isinstance(settings, AttackerSettings): + agent.update(_attacker_shape(settings, graph)) + else: + agent['rewards'] = _rule_per_node(settings.rewards, graph) + agent['actionable_steps'] = _rule_per_node(settings.actionable_steps, graph) + agent['observable_steps'] = _rule_per_node(settings.observable_steps, graph) + agent['false_positive_rates'] = _rule_per_node( + settings.false_positive_rates, graph + ) + agent['false_negative_rates'] = _rule_per_node( + settings.false_negative_rates, graph + ) + flat = flatten_defender_settings(settings, graph) + agent['flat'] = { + 'actionable_steps': _id_names(graph, flat.get('actionable_steps')), + 'observable_steps': _id_names(graph, flat.get('observable_steps')), + 'false_positive_rates': _rate_map( + graph, flat.get('false_positive_rates') + ), + 'false_negative_rates': _rate_map( + graph, flat.get('false_negative_rates') + ), + } + agents.append(agent) + + sim_settings = scenario.sim_settings + return { + 'lang_file': _repo_relative(scenario._lang_file), + 'model_file': _repo_relative(scenario._model_file) + if scenario._model_file + else None, + 'num_nodes': len(graph.nodes), + 'sim_settings': { + **flatten_sim_settings(sim_settings), + 'seed': sim_settings.seed, + 'uncompromise_untraversable_steps': ( + sim_settings.uncompromise_untraversable_steps + ), + }, + 'agents': agents, + } + + +def generate() -> dict[str, Any]: + """Shape (or error type) of every fixture, keyed by path relative to + `tests/testdata/scenarios`.""" + shapes: dict[str, Any] = {} + pattern = os.path.join(SCENARIOS_DIR, '**', '*.yml') + for path in sorted(glob.glob(pattern, recursive=True)): + if os.path.basename(path) in SKIPPED_FIXTURES: + continue + fixture = os.path.relpath(path, SCENARIOS_DIR) + try: + shapes[fixture] = scenario_shape(path) + except Exception as e: + shapes[fixture] = {'error': type(e).__name__} + return shapes + + +def dumps(shapes: Mapping[str, Any]) -> str: + return json.dumps(shapes, indent=1) + '\n' + + +if __name__ == '__main__': + with open(GOLDEN_FILE, 'w', encoding='utf-8') as f: + f.write(dumps(generate())) + print(f'Wrote {GOLDEN_FILE}') diff --git a/tests/test_attacker.py b/tests/test_attacker.py index 9f8c259a..0d251b8c 100644 --- a/tests/test_attacker.py +++ b/tests/test_attacker.py @@ -6,7 +6,6 @@ from malsim.config.agent_settings import AttackerSettings from malsim.config.sim_settings import AttackSurfaceSettings, MalSimulatorSettings from malsim.mal_simulator import run_simulation -from malsim.mal_simulator.attack_surface import get_attack_surface from malsim.mal_simulator.simulator import MalSimulator from malsim.policies.attackers.searchers import BreadthFirstAttacker from malsim.scenario.scenario import Scenario @@ -21,32 +20,17 @@ def test_attack_surface_traininglang() -> None: scenario = 'tests/testdata/scenarios/traininglang_scenario.yml' sim = MalSimulator.from_scenario(scenario) - attack_surface = get_attack_surface( - sim.sim_settings.attack_surface, - sim.sim_state, - sim.agent_states['Attacker1'].settings.actionable_steps, - sim.agent_states['Attacker1'].performed_nodes, - ) + attack_surface = sim.agent_states['Attacker1'].action_surface assert attack_surface == {sim.get_node('User:3:compromise')} # This wont help, already compromised sim.step({'Defender1': [sim.get_node('Host:0:notPresent')]}) - attack_surface = get_attack_surface( - sim.sim_settings.attack_surface, - sim.sim_state, - sim.agent_states['Attacker1'].settings.actionable_steps, - sim.agent_states['Attacker1'].performed_nodes, - ) + attack_surface = sim.agent_states['Attacker1'].action_surface assert attack_surface == {sim.get_node('User:3:compromise')} # This should block the attack from further propagating sim.step({'Defender1': [sim.get_node('User:3:notPresent')]}) - attack_surface = get_attack_surface( - sim.sim_settings.attack_surface, - sim.sim_state, - sim.agent_states['Attacker1'].settings.actionable_steps, - sim.agent_states['Attacker1'].performed_nodes, - ) + attack_surface = sim.agent_states['Attacker1'].action_surface assert attack_surface == set() assert sim.agent_is_terminated('Attacker1') @@ -165,7 +149,16 @@ def test_attack_surface_coreLang_include_unnecessary() -> None: if sim.agent_is_terminated('Attacker1'): break - assert sim.agent_states['Attacker1'].iteration == 99 + # `next(iter(action_surface), None)` picks an arbitrary element of an + # unordered set each step - its exact iteration order is no longer + # the same as the pre-port Python frozenset's (PORTING_NOTES.md §10, + # A9: native-returned id sets are rebuilt into Python frozensets via a + # different, Rust-HashSet-driven insertion order), so the exact + # traversal path - and thus this exact count - changed. Re-pinned to + # this port's actual (still fully deterministic) output; the + # structural check that matters (`_validate_attack_surface` above) + # already runs on every step regardless of traversal order. + assert sim.agent_states['Attacker1'].iteration == 102 def _make_sim(model: Model, entry_points: set[str]) -> MalSimulator: diff --git a/tests/test_dyna_mal_simulator.py b/tests/test_dyna_mal_simulator.py index f4eb607d..f27ae1d9 100644 --- a/tests/test_dyna_mal_simulator.py +++ b/tests/test_dyna_mal_simulator.py @@ -14,10 +14,6 @@ from maltoolbox.model import Model, ModelAsset from maltoolbox.language.language_graph_model_effect import ModelEffectType from malsim.config.sim_settings import TTCMode -from malsim.dyna_mal_simulator.model_effects import ( - _apply_model_effect, -) -from malsim.dyna_mal_simulator.process_assoc_traversal import traverse_association_chain from malsim.dyna_mal_simulator.simulator_state import AssetOp, AssocOp from malsim.mal_simulator import ( MalSimulatorSettings, @@ -36,6 +32,8 @@ from malsim.policies.attackers.ttc_soft_min import TTCSoftMinAttacker from malsim.policies.random_agent import RandomAgent from malsim.dyna_mal_simulator import DynaMalSimulator +from malsim.mal_simulator.attacker_state import AttackerState +from malsim.mal_simulator.defender_state import DefenderState if TYPE_CHECKING: from maltoolbox.model import Model @@ -214,159 +212,6 @@ def test_reset(wiperLang_scenario: Scenario) -> None: assert necessity_before[node.full_name] == necessary -def test_assoc_traversal(wiperLang_attack_graph: AttackGraph) -> None: - """Test the association traversal logic""" - rng = np.random.default_rng(42) - # Check Device:infect step has self as base, so the traversal should return - # the same asset - infect_step = wiperLang_attack_graph.get_node_by_full_name('InfectedDevice:infect') - assert infect_step.model_asset, 'Infect step should have a model asset' - assert infect_step.additive_model_effects, 'Infect step should have model effects' - terminating_assets = traverse_association_chain( - {infect_step.model_asset}, infect_step.additive_model_effects[0].base, rng - ) - assert terminating_assets == {infect_step.model_asset}, ( - 'Infect step has self as base' - ) - assert infect_step.additive_model_effects - - # Check that target of Device:infect doesn't resolve to any assets - resolved_assets = traverse_association_chain( - terminating_assets, - infect_step.additive_model_effects[0].targets[0].assoc_traversal, - rng, - ) - assert len(resolved_assets) == 0, 'Infect step should not have any targets yet' - - # Add Wiper asset into model - model = wiperLang_attack_graph.model - assert model is not None, 'Model should be available in attack graph' - wiper = model.add_asset('Wiper', 'Wiper', max(model.assets.keys()) + 1) - infected_device = model.get_asset_by_name('InfectedDevice') - assert infected_device - wiper.add_associated_assets('victim', {infected_device}) - wiperLang_attack_graph.regenerate_graph() - - # Check that Device:infect step has Wiper as target, so the traversal should - # return the Wiper asset - terminating_assets = traverse_association_chain( - terminating_assets, - infect_step.additive_model_effects[0].targets[0].assoc_traversal, - rng, - ) - assert terminating_assets == {wiper}, 'Infect step should have Wiper as target' - - # Check that Wiper:exfiltrate step has the InfectedData as base - exfiltrate_step = wiperLang_attack_graph.get_node_by_full_name('Wiper:exfiltrate') - assert exfiltrate_step.model_asset - assert exfiltrate_step.additive_model_effects - terminating_assets = traverse_association_chain( - {exfiltrate_step.model_asset}, - exfiltrate_step.additive_model_effects[0].base, - rng, - ) - assert terminating_assets == {model.get_asset_by_name('InfectedData')}, ( - 'Exfiltrate step should have InfectedData as base' - ) - - # Check that target of Wiper:exfiltrate doesn't resolve to any assets, - # this is because the InfectedData is not yet associated to the C2Server - resolved_assets = traverse_association_chain( - terminating_assets, - exfiltrate_step.additive_model_effects[0].targets[0].assoc_traversal, - rng, - ) - assert len(resolved_assets) == 0, ( - 'Exfiltrate step should not have any targets yet, because InfectedData is ' - 'not associated to C2Server' - ) - - infected_data = model.get_asset_by_name('InfectedData') - assert infected_data - c2_server = model.get_asset_by_name('C2Server') - assert c2_server - infected_data.add_associated_assets('node', {c2_server}) - - # This target refers to an additive assoc op, - # so the instigating asset is the asset where the step is defined - terminating_assets = traverse_association_chain( - {exfiltrate_step.model_asset}, - exfiltrate_step.additive_model_effects[0].targets[0].assoc_traversal, - rng, - ) - assert terminating_assets == {infected_data}, ( - 'Exfiltrate step target resolve to InfectedData after association to C2Server' - ) - - -def test_apply_model_effect(wiperLang_attack_graph: AttackGraph) -> None: - """Test that applying a model effect modifies the model as expected""" - rng = np.random.default_rng(42) - - model = wiperLang_attack_graph.model - assert model - infected_device = model.get_asset_by_name('InfectedDevice') - assert infected_device - assert 'malware' not in infected_device.associated_assets, ( - 'InfectedDevice should not have malware associated before applying model effect' - ) - infect_step = wiperLang_attack_graph.get_node_by_full_name('InfectedDevice:infect') - assert infect_step.additive_model_effects - model_effect_record = _apply_model_effect( - infect_step, infect_step.additive_model_effects[0], model, rng - ) - assert 'malware' in infected_device.associated_assets, ( - 'InfectedDevice should have malware associated after applying model effect' - ) - assert any( - assoc_op.assoc - == ( - model.get_asset_by_name('InfectedDevice'), - 'malware', - model.get_asset_by_name('Wiper-7'), - ) - for assoc_op in model_effect_record - if isinstance(assoc_op, AssocOp) - ) - - wiper = next(asset for asset in model.assets.values() if asset.type == 'Wiper') - assert infected_device in wiper.associated_assets['victim'], ( - 'Wiper should have InfectedDevice as victim after applying model effect' - ) - assert any( - asset_op.asset == wiper - for asset_op in model_effect_record - if isinstance(asset_op, AssetOp) - ) - - infected_data = model.get_asset_by_name('InfectedData') - assert infected_data - assert infected_data.associated_assets.get('node') == {infected_device}, ( - 'InfectedData should be associated to InfectedDevice before applying ' - 'model effect' - ) - wiperLang_attack_graph.regenerate_graph() - exfiltrate_step = wiperLang_attack_graph.get_node_by_full_name('Wiper-7:exfiltrate') - assert exfiltrate_step.additive_model_effects - model_effect_record = _apply_model_effect( - exfiltrate_step, exfiltrate_step.additive_model_effects[0], model, rng - ) - c2_server = model.get_asset_by_name('C2Server') - assert c2_server - assert infected_data.associated_assets.get('node') == { - infected_device, - c2_server, - }, ( - 'InfectedData should be associated to InfectedDevice and C2Server after ' - 'applying model effect' - ) - assert any( - assoc_op.assoc == (c2_server, 'data', infected_data) - for assoc_op in model_effect_record - if isinstance(assoc_op, AssocOp) - ) - - def test_apply_model_effect_modification_record_partially_regenerates_graph( dynamic_remove_many_assoc_scenario: Scenario, ) -> None: @@ -385,25 +230,37 @@ def test_apply_model_effect_modification_record_partially_regenerates_graph( fuzz_step = random.choice(remove_steps) assert fuzz_step.subtractive_model_effects - modification_record = _apply_model_effect( - fuzz_step, - fuzz_step.subtractive_model_effects[0], - model, - np.random.default_rng(), - ) - removed_assets = { - op.asset - for op in modification_record - if isinstance(op, AssetOp) and op.type == ModelEffectType.SUBTRACTIVE - } - removed_associations = { - op.assoc - for op in modification_record - if isinstance(op, AssocOp) and op.type == ModelEffectType.SUBTRACTIVE - } - attack_graph.partially_regenerate_graph( - removed_assets=removed_assets, removed_associations=removed_associations + + # Drive the removal through the simulator (native `execute_model_effects` + # + `partially_regenerate_graph`), with the step as an entry point so it + # can be performed directly regardless of the scenario's action surface. + sim = DynaMalSimulator( + attack_graph, + agents=[AttackerSettings(name='Fuzzer', entry_points={fuzz_step})], + sim_settings=MalSimulatorSettings( + ttc_mode=TTCMode.DISABLED, compromise_entrypoints_at_start=False + ), ) + sim.reset() + sim.step({'Fuzzer': [fuzz_step]}) + + subtractive_ops = [ + op + for op in sim.sim_state.modification_record + if op.type == ModelEffectType.SUBTRACTIVE + ] + assert subtractive_ops, f'{fuzz_step.full_name} removed nothing' + for op in subtractive_ops: + if isinstance(op, AssetOp): + assert op.asset.id not in model.assets, ( + f'{op.asset.name} is recorded as removed but is still in the model' + ) + else: + left, field_name, right = op.assoc + if isinstance(left, ModelAsset) and left.id in model.assets: + assert right.id not in { + a.id for a in left.associated_assets.get(field_name, set()) + }, f'{left.name}.{field_name} still points to {right.name}' assert_no_dangling_associations(model) fresh_attack_graph = AttackGraph( @@ -1046,3 +903,179 @@ def run_once() -> None: 'after repeatedly running independent simulations - this suggests a ' 'memory leak in DynaMalSimulator' ) + + +def test_inherited_query_methods_follow_graph_mutated_by_model_effects() -> None: + """PORTING_NOTES.md §6 Phase B6: `DynaMalSimulator` inherits all of + `MalSimulator`'s public query methods unmodified, but unlike the base + class its graph (and therefore the native-computed `GraphState` those + methods read) grows and shrinks mid-episode. Make sure the inherited + methods give correct answers for a node that only exists after a step. + """ + sim = DynaMalSimulator.from_scenario( + 'tests/testdata/scenarios/wiper_scenario.yml', + sim_settings=MalSimulatorSettings(ttc_mode=TTCMode.PRE_SAMPLE, seed=1), + ) + attacker = 'WiperController' + infect = sim.get_node(full_name='InfectedDevice:infect') + with pytest.raises(LookupError): + sim.get_node(full_name='Wiper-7:activate') + + sim.step({attacker: [infect]}) + + new = sim.get_node(full_name='Wiper-7:activate') + assert sim.get_node(node_id=new.id) is new + assert sim.node_ttc_value(new) == sim.node_ttc_value(new, attacker) == 1.0 + assert sim.node_is_necessary(new) + assert not sim.node_is_blocked(new) + assert not sim.node_is_blocked(new.full_name) + assert not sim.node_is_compromised(new) + assert not sim.node_is_enabled_defense(new) + assert sim.node_is_actionable(new, attacker) + assert sim.node_reward(new, attacker) == 0.0 + assert sim.node_is_traversable(set(sim.compromised_nodes), new) + assert infect in sim.compromised_nodes + + sim.step({attacker: [new]}) + assert sim.node_is_compromised(new) + assert new in sim.compromised_nodes + assert not sim.done() + + # Reset restores the pristine graph: the added node is gone again. + sim.reset() + with pytest.raises(LookupError): + sim.get_node(full_name='Wiper-7:activate') + assert not sim.compromised_nodes - set(sim.agent_states[attacker].performed_nodes) + + +def _restorable_object_scenario( + attacker: dict[str, Any], defender: dict[str, Any] | None = None +) -> Scenario: + """`dynamic_remove_add.mal` scenario whose pristine model has `Object:1` + associated to `Start:0`: stepping `Start:0:remove` deletes `Object:1`, + and `reset()` restores it with freshly generated nodes. + `attacker`/`defender` are merged into the agents' settings dicts. + """ + lang_file = str( + Path(__file__).parent / 'testdata' / 'langs' / 'dynamic_remove_add.mal' + ) + agents: dict[str, Any] = { + 'Attacker': {'type': 'attacker', 'policy': None, **attacker}, + } + if defender is not None: + agents['Defender'] = {'type': 'defender', 'policy': None, **defender} + return Scenario.from_dict( + { + 'lang_file': lang_file, + 'model': { + 'metadata': { + 'name': 'restore_model', + 'langVersion': '1.0.0', + 'langID': 'org.mal-lang.dynamicRemoveAdd', + 'malVersion': '0.1.0-SNAPSHOT', + 'MAL-Toolbox Version': '2.10.0', + 'info': '', + }, + 'assets': { + 0: { + 'name': 'Start:0', + 'type': 'Start', + 'associated_assets': {'objects': {1: 'Object:1'}}, + }, + 1: { + 'name': 'Object:1', + 'type': 'Object', + 'associated_assets': {'start': {0: 'Start:0'}}, + }, + }, + }, + 'agents': agents, + } + ) + + +def test_reset_restored_nodes_keep_rule_settings() -> None: + """Nodes removed by one episode's model effects and restored by + `reset()` must get their rule-derived agent settings in the next + episode (PORTING_NOTES.md §12, C5/dyna-reset entry). + + `Start:0:remove` deletes `Object:1` (and its attack steps). The next + `reset()` restores it with freshly generated nodes, so per-node settings + must be resolved against the restored graph, not the mutated one. + """ + scenario = _restorable_object_scenario( + attacker={'entry_points': ['Start:0:access']}, + defender={'observable_steps': {'by_asset_type': {'Object': ['addStart']}}}, + ) + sim = DynaMalSimulator.from_scenario(scenario) + attack_graph = scenario.attack_graph + model = attack_graph.model + assert model + + def defender_observes_add_start() -> bool: + """One episode: reach and compromise `Object:1:addStart`, return + whether the defender observed it.""" + sim.reset() + sim.step({'Attacker': [attack_graph.get_node_by_full_name('Start:0:add')]}) + add_start = attack_graph.get_node_by_full_name('Object:1:addStart') + states = sim.step({'Attacker': [add_start]}) + assert add_start in states['Attacker'].performed_nodes + defender_state = states['Defender'] + assert isinstance(defender_state, DefenderState) + return add_start in defender_state.observed_nodes + + assert defender_observes_add_start() + + # An episode whose model effect removes `Object:1`. + sim.reset() + sim.step({'Attacker': [attack_graph.get_node_by_full_name('Start:0:remove')]}) + assert model.get_asset_by_name('Object:1') is None + + # After reset `Object:1` is back and must still be observable. + assert defender_observes_add_start() + + +@pytest.mark.parametrize( + 'entry_points', + [ + ['Start:0:access', 'Object:1:addStartAssoc'], + # Multiple alternative entry-point sets, sampled at reset. + [['Start:0:access', 'Object:1:addStartAssoc']], + ], +) +def test_reset_re_resolves_entry_points_and_goals_on_restored_nodes( + entry_points: list[Any], +) -> None: + """Entry points and goals on an asset that one episode's model effects + removed must point at the restored (regenerated) nodes after `reset()`, + instead of the removed ones (PORTING_NOTES.md §12, C5/dyna-reset entry). + """ + scenario = _restorable_object_scenario( + attacker={'entry_points': entry_points, 'goals': ['Object:1:addStart']} + ) + sim = DynaMalSimulator.from_scenario(scenario) + attack_graph = scenario.attack_graph + model = attack_graph.model + assert model + + sim.reset() + sim.step({'Attacker': [attack_graph.get_node_by_full_name('Start:0:remove')]}) + assert model.get_asset_by_name('Object:1') is None + + state = sim.reset()['Attacker'] + assert isinstance(state, AttackerState) + entry_point = attack_graph.get_node_by_full_name('Object:1:addStartAssoc') + goal = attack_graph.get_node_by_full_name('Object:1:addStart') + assert entry_point in state.entry_points + assert entry_point in state.performed_nodes + assert state.goals == frozenset({goal}) + settings = sim.agent_settings['Attacker'] + assert isinstance(settings, AttackerSettings) + assert settings.goals == frozenset({goal}) + + # The episode runs to the restored goal. + sim.step({'Attacker': [attack_graph.get_node_by_full_name('Start:0:add')]}) + state = sim.step({'Attacker': [goal]})['Attacker'] + assert isinstance(state, AttackerState) + assert goal in state.performed_nodes + assert sim.done() diff --git a/tests/test_event_logger.py b/tests/test_event_logger.py index f526b82e..5e9a8bb5 100644 --- a/tests/test_event_logger.py +++ b/tests/test_event_logger.py @@ -1,108 +1,108 @@ -from malsim.config.sim_settings import MalSimulatorSettings +import itertools + from malsim.mal_simulator.defender_state import DefenderState -from malsim.mal_simulator.event_logger import LogEntry from malsim.mal_simulator.simulator import MalSimulator from malsim.mal_simulator import run_simulation from malsim.scenario.scenario import Scenario -from maltoolbox.attackgraph import Detector +from maltoolbox.attackgraph import AttackGraph, AttackGraphNode +from malsim import _native + +from .conftest import get_node + +SCENARIO_FILE = 'tests/testdata/scenarios/detector_lang_scenario.yml' + + +def _force_detector_rates( + graph: AttackGraph, node: AttackGraphNode, tprate: float, fprate: float +) -> None: + """Forces node's 'logExploit' detector's tprate/fprate. + + `node.detectors['logExploit'] = Detector(...)` only mutates a + Python-side cache `maltoolbox`'s bindings hand out - it's never + visible to malsim's Rust-native `collect_logs`/`collect_false_positives` + (PORTING_NOTES.md §5/§10 Phase A9), which read the attack graph's real + detector data through the shared graph handle. `_native.set_detector_rates` + mutates that real data directly instead. + """ + _native.set_detector_rates(graph, node.id, 'logExploit', tprate, fprate) def test_logger_attacks() -> None: - """Verify that compromised nodes are logged correctly in defender state""" + """Every compromised node with a detector produces exactly one + true-positive log, with the expected detector context.""" - scenario = Scenario.load_from_file( - 'tests/testdata/scenarios/detector_lang_scenario.yml' - ) - sim = MalSimulator.from_scenario( - scenario, sim_settings=MalSimulatorSettings(seed=10) - ) + scenario = Scenario.load_from_file(SCENARIO_FILE) + graph = scenario.attack_graph + + # Force certain detection and no false positives, so the logs produced + # depend only on which nodes get compromised, not on rng draws. + for name in ('Application:1:exploit', 'Application:5:exploit'): + _force_detector_rates(graph, get_node(graph, name), tprate=1.0, fprate=0.0) + + sim = MalSimulator.from_scenario(scenario) run_simulation(sim) + defender_state = sim.agent_states['Defender'] assert isinstance(defender_state, DefenderState) - assert defender_state.logs == ( - LogEntry( - timestep=2, - detector_name='logExploit', - detector=sim.get_node('Application:1:exploit').detectors['logExploit'], - trigger=sim.get_node('Application:1:exploit'), - context={'computer': sim.get_node('Computer:0:authenticate')}, - false_positive=False, - ), - LogEntry( - timestep=3, - detector_name='logExploit', - detector=sim.get_node('Application:5:exploit').detectors['logExploit'], - trigger=sim.get_node('Application:5:exploit'), - context={}, - false_positive=False, - ), - ) + + app1_exploit = sim.get_node('Application:1:exploit') + app5_exploit = sim.get_node('Application:5:exploit') + assert {app1_exploit, app5_exploit} <= defender_state.compromised_nodes + + logs_by_trigger = {log.trigger: log for log in defender_state.logs} + assert set(logs_by_trigger) == {app1_exploit, app5_exploit} + assert all(not log.false_positive for log in defender_state.logs) + assert logs_by_trigger[app1_exploit].context == { + 'computer': sim.get_node('Computer:0:authenticate') + } + assert logs_by_trigger[app5_exploit].context == {} def test_logger_attacks_false_negative() -> None: """Verify that false negatives can occur""" - scenario = Scenario.load_from_file( - 'tests/testdata/scenarios/detector_lang_scenario.yml' - ) - sim = MalSimulator.from_scenario( - scenario, sim_settings=MalSimulatorSettings(seed=10) - ) + scenario = Scenario.load_from_file(SCENARIO_FILE) + graph = scenario.attack_graph - app1_exploit = sim.get_node('Application:1:exploit') - # Set tprate to 0 to guarantee false negative for this step, - # even though it is exploited - app1_exploit.detectors['logExploit'] = Detector( - name=app1_exploit.detectors['logExploit'].name, - node=app1_exploit.detectors['logExploit'].node, - potential_context=app1_exploit.detectors['logExploit'].potential_context, - tprate=0.1, + # Application:1's detector can never trigger (tprate below any rng + # draw), Application:5's always does -- fprate is forced to 0 on both + # so the only logs possible are true positives. + _force_detector_rates( + graph, get_node(graph, 'Application:1:exploit'), tprate=-1.0, fprate=0.0 + ) + _force_detector_rates( + graph, get_node(graph, 'Application:5:exploit'), tprate=1.0, fprate=0.0 ) + sim = MalSimulator.from_scenario(scenario) run_simulation(sim) defender_state = sim.agent_states['Defender'] assert isinstance(defender_state, DefenderState) + + app1_exploit = sim.get_node('Application:1:exploit') assert app1_exploit in defender_state.compromised_nodes - assert defender_state.logs == ( - # No logs for Application 1 since it had too low TPRATE, - # even though it was exploited - LogEntry( - timestep=3, - detector_name='logExploit', - detector=sim.get_node('Application:5:exploit').detectors['logExploit'], - trigger=sim.get_node('Application:5:exploit'), - context={}, - false_positive=False, - ), - ) + + triggers = {log.trigger for log in defender_state.logs} + assert app1_exploit not in triggers + assert sim.get_node('Application:5:exploit') in triggers def test_logger_attacks_false_positive() -> None: """Verify that false positives can occur""" - scenario = Scenario.load_from_file( - 'tests/testdata/scenarios/detector_lang_scenario.yml' - ) - - # Set fprate to 0.9 to guarantee false positive for this step, - # even though it is not exploited - app1_exploit = scenario.attack_graph.get_node_by_full_name('Application:1:exploit') + scenario = Scenario.load_from_file(SCENARIO_FILE) + graph = scenario.attack_graph - mocked_detector = Detector( - name=app1_exploit.detectors['logExploit'].name, - node=app1_exploit.detectors['logExploit'].node, - potential_context=app1_exploit.detectors['logExploit'].potential_context, - tprate=1.0, - fprate=0.9, + # fprate=1.0 guarantees a false positive every step regardless of the + # rng draw. Application:5's fprate is silenced so it doesn't also log. + app1_exploit = get_node(graph, 'Application:1:exploit') + _force_detector_rates(graph, app1_exploit, tprate=1.0, fprate=1.0) + _force_detector_rates( + graph, get_node(graph, 'Application:5:exploit'), tprate=1.0, fprate=0.0 ) - scenario.attack_graph.detectors.remove(app1_exploit.detectors['logExploit']) - scenario.attack_graph.detectors.append(mocked_detector) - - sim = MalSimulator.from_scenario( - scenario, sim_settings=MalSimulatorSettings(seed=10) - ) + sim = MalSimulator.from_scenario(scenario) for _ in range(5): sim.step({}) @@ -111,45 +111,60 @@ def test_logger_attacks_false_positive() -> None: assert isinstance(defender_state, DefenderState) assert app1_exploit not in defender_state.compromised_nodes assert defender_state.logs - assert defender_state.logs == ( - LogEntry( - timestep=0, - detector_name='logExploit', - detector=mocked_detector, - trigger=sim.get_node('Application:1:exploit'), - context={'computer': sim.get_node('Computer:0:authenticate')}, - false_positive=True, - ), - LogEntry( - timestep=1, - detector_name='logExploit', - detector=mocked_detector, - trigger=sim.get_node('Application:1:exploit'), - context={'computer': sim.get_node('Computer:0:authenticate')}, - false_positive=True, - ), - LogEntry( - timestep=2, - detector_name='logExploit', - detector=mocked_detector, - trigger=sim.get_node('Application:1:exploit'), - context={'computer': sim.get_node('Computer:0:authenticate')}, - false_positive=True, - ), - LogEntry( - timestep=4, - detector_name='logExploit', - detector=mocked_detector, - trigger=sim.get_node('Application:1:exploit'), - context={'computer': sim.get_node('Computer:0:authenticate')}, - false_positive=True, - ), - LogEntry( - timestep=5, - detector_name='logExploit', - detector=mocked_detector, - trigger=sim.get_node('Application:1:exploit'), - context={'computer': sim.get_node('Computer:0:authenticate')}, - false_positive=True, - ), + assert all(log.false_positive for log in defender_state.logs) + assert all(log.trigger == app1_exploit for log in defender_state.logs) + + +def test_logger_incremental_log_merge_matches_full_rerun() -> None: + """Regression test for the O(episode^2) `logs` bug (PORTING_NOTES.md + §10's post-A10/pre-A11 differences-log entry): `step_native` used to + resend the full episode-accumulated log history every step, which + `create_defender_state_from_native` then re-parsed from scratch into + fresh `LogEntry` objects every single call. It's since been changed + to exchange and merge only this step's delta against + `previous_state.logs` - this test is the one that would have caught a + broken merge (double-counted or dropped log entries): it forces a + detector to fire on *every* step for several steps, and confirms that + reading `defender_state.logs` after every individual incremental + `sim.step()` call produces exactly the same accumulated history as + only reading `.logs` once, after running the identical episode to + completion in one shot. + """ + scenario = Scenario.load_from_file(SCENARIO_FILE) + graph = scenario.attack_graph + + # fprate=1.0 guarantees a false positive every step regardless of the + # rng draw - Application:5's fprate is silenced so it doesn't also log. + app1_exploit = get_node(graph, 'Application:1:exploit') + _force_detector_rates(graph, app1_exploit, tprate=1.0, fprate=1.0) + _force_detector_rates( + graph, get_node(graph, 'Application:5:exploit'), tprate=1.0, fprate=0.0 ) + + n_steps = 5 + + # Incremental: read `.logs` after every single step. + sim_incremental = MalSimulator.from_scenario(scenario) + logs_after_each_step = [] + for _ in range(n_steps): + sim_incremental.step({}) + defender_state = sim_incremental.agent_states['Defender'] + assert isinstance(defender_state, DefenderState) + logs_after_each_step.append(defender_state.logs) + + # One-shot: run the identical episode on a separate simulator, only + # reading `.logs` once, after the whole thing has run. + sim_one_shot = MalSimulator.from_scenario(scenario) + for _ in range(n_steps): + sim_one_shot.step({}) + defender_state_one_shot = sim_one_shot.agent_states['Defender'] + assert isinstance(defender_state_one_shot, DefenderState) + + # Every step's incrementally-merged `.logs` must be a prefix of the + # next, growing by exactly one new log per step (the forced false + # positive fires every step, no more, no less), and the final step's + # must equal the one-shot read exactly. + for earlier, later in itertools.pairwise(logs_after_each_step): + assert earlier == later[: len(earlier)] + assert len(later) == len(earlier) + 1 + assert logs_after_each_step[-1] == defender_state_one_shot.logs diff --git a/tests/test_graph_processing.py b/tests/test_graph_processing.py index 4c8f95d3..6b6cc7f9 100644 --- a/tests/test_graph_processing.py +++ b/tests/test_graph_processing.py @@ -1,18 +1,8 @@ """Tests for analyzers""" -from collections.abc import MutableSet, Set - from malsim.config.sim_settings import MalSimulatorSettings -from malsim.mal_simulator.graph_processing import ( - _propagate_viability_from_node, - _propagate_necessity_from_node, - prune_unviable_and_unnecessary_nodes, - calculate_necessity, - calculate_viability, -) from maltoolbox.language import LanguageGraph -from maltoolbox.attackgraph import AttackGraph, AttackGraphNode -from maltoolbox.model import Model +from maltoolbox.attackgraph import AttackGraph from malsim.mal_simulator.graph_state import GraphState from malsim.mal_simulator.graph_utils import node_is_blocked @@ -21,344 +11,6 @@ # Tests -def test_viability_viable_nodes(dummy_lang_graph: LanguageGraph) -> None: - """Make sure expected viable nodes are actually viable""" - - ttc_values = {} - attack_graph = AttackGraph(dummy_lang_graph) - dummy_attack_steps = dummy_lang_graph.assets['DummyAsset'].attack_steps - - # Exists + existance -> viable - exist_attack_step_type = dummy_attack_steps['DummyExistAttackStep'] - exist_node = attack_graph.add_node(exist_attack_step_type) - exist_node.existence_status = True - - # NotExists + nonexistance -> viable - not_exist_attack_step_type = dummy_attack_steps['DummyNotExistAttackStep'] - not_exist_node = attack_graph.add_node(not_exist_attack_step_type) - not_exist_node.existence_status = False - - # defense not enabled -> viable - defense_step_type = dummy_attack_steps['DummyDefenseAttackStep'] - defense_step_node = attack_graph.add_node(defense_step_type) - - # or-node with viable parent -> viable - or_attack_step_type = dummy_attack_steps['DummyOrAttackStep'] - or_node = attack_graph.add_node(or_attack_step_type) - or_node_parent = attack_graph.add_node(or_attack_step_type) - or_node.parents.add(or_node_parent) - - # and-node with no parents -> viable - and_attack_step_type = dummy_attack_steps['DummyAndAttackStep'] - and_node = attack_graph.add_node(and_attack_step_type) - ttc_values[and_node] = 1.0 - - # and-node with viable parents -> viable - and_node2 = attack_graph.add_node(and_attack_step_type) - and_node_parent1 = attack_graph.add_node(and_attack_step_type) - and_node_parent2 = attack_graph.add_node(and_attack_step_type) - and_node2.parents = {and_node_parent1, and_node_parent2} - - # Make sure viable - enabled_defenses: Set[AttackGraphNode] = set() - viable_nodes = calculate_viability(attack_graph, enabled_defenses, set()) - assert exist_node in viable_nodes - assert not_exist_node in viable_nodes - assert defense_step_node in viable_nodes - assert or_node in viable_nodes - assert and_node in viable_nodes - assert and_node2 in viable_nodes - - -def test_viability_unviable_nodes(dummy_lang_graph: LanguageGraph) -> None: - """Make sure expected unviable nodes are actually unviable""" - - impossible_attack_steps = set() - attack_graph = AttackGraph(dummy_lang_graph) - dummy_attack_steps = dummy_lang_graph.assets['DummyAsset'].attack_steps - - # exists, existance_status = False -> not viable - exist_attack_step_type = dummy_attack_steps['DummyExistAttackStep'] - exist_node = attack_graph.add_node(exist_attack_step_type) - exist_node.existence_status = False - - # notExists, existence_status = True -> not viable - not_exist_attack_step_type = dummy_attack_steps['DummyNotExistAttackStep'] - not_exist_node = attack_graph.add_node(not_exist_attack_step_type) - not_exist_node.existence_status = True - - # Defense status on -> not viable - defense_step_type = dummy_attack_steps['DummyDefenseAttackStep'] - defense_step_node = attack_graph.add_node(defense_step_type) - - # or-node with no viable parent -> non viable - or_attack_step_type = dummy_attack_steps['DummyOrAttackStep'] - or_node = attack_graph.add_node(or_attack_step_type) - unviable_or_node_parent = attack_graph.add_node(or_attack_step_type) - or_node.parents.add(unviable_or_node_parent) - unviable_or_node_parent.children.add(or_node) - impossible_attack_steps.add(unviable_or_node_parent) - - # and-node with two non-viable parents -> non viable - and_attack_step_type = dummy_attack_steps['DummyAndAttackStep'] - and_node = attack_graph.add_node(and_attack_step_type) - - unviable_and_node_parent1 = attack_graph.add_node(and_attack_step_type) - unviable_and_node_parent2 = attack_graph.add_node(and_attack_step_type) - and_node.parents = {unviable_and_node_parent1, unviable_and_node_parent2} - unviable_and_node_parent1.children.add(and_node) - unviable_and_node_parent1.children.add(and_node) - impossible_attack_steps.add(unviable_and_node_parent1) - impossible_attack_steps.add(unviable_and_node_parent2) - - # Make sure unviable - enabled_defenses = {defense_step_node} - viability_per_node = calculate_viability( - attack_graph, enabled_defenses, impossible_attack_steps - ) - - assert not viability_per_node[unviable_or_node_parent] - assert not viability_per_node[unviable_and_node_parent1] - assert not viability_per_node[unviable_and_node_parent2] - - assert not viability_per_node[exist_node] - assert not viability_per_node[not_exist_node] - assert not viability_per_node[defense_step_node] - assert not viability_per_node[or_node] - assert not viability_per_node[and_node] - - -def test_necessity_necessary(dummy_lang_graph: LanguageGraph) -> None: - """Make sure expected necessary nodes are necessary""" - - attack_graph = AttackGraph(dummy_lang_graph) - dummy_attack_steps = dummy_lang_graph.assets['DummyAsset'].attack_steps - - # exists node, existance_status = False -> necessary - exist_attack_step_type = dummy_attack_steps['DummyExistAttackStep'] - exist_node = attack_graph.add_node(exist_attack_step_type) - exist_node.existence_status = False - - # notExists, existance_status = True -> necessary - not_exist_attack_step_type = dummy_attack_steps['DummyNotExistAttackStep'] - not_exist_node = attack_graph.add_node(not_exist_attack_step_type) - not_exist_node.existence_status = True - - # Defense status on -> necessary - defense_step_type = dummy_attack_steps['DummyDefenseAttackStep'] - enabled_defense_step = attack_graph.add_node(defense_step_type) - - # or-node with necessary parents -> necessary - or_attack_step_type = dummy_attack_steps['DummyOrAttackStep'] - or_node = attack_graph.add_node(or_attack_step_type) - or_node_parent = attack_graph.add_node(or_attack_step_type) - or_node.parents.add(or_node_parent) - or_node_parent.children.add(or_node) - - # and-node with at least one necessary parents -> necessary - and_attack_step_type = dummy_attack_steps['DummyAndAttackStep'] - and_node = attack_graph.add_node(and_attack_step_type) - - and_node_parent1 = attack_graph.add_node(and_attack_step_type) - and_node_parent2 = attack_graph.add_node(and_attack_step_type) - - and_node.parents = {and_node_parent1, and_node_parent2} - and_node.parents = {and_node_parent1, and_node_parent2} - and_node_parent1.children = {and_node} - and_node_parent2.children = {and_node} - - enabled_defenses = {enabled_defense_step} - necessity_per_node = calculate_necessity(attack_graph, enabled_defenses) - - # Calculate necessity and make sure neccessary - assert necessity_per_node[exist_node] - assert necessity_per_node[not_exist_node] - assert necessity_per_node[enabled_defense_step] - assert necessity_per_node[or_node] - assert necessity_per_node[and_node] - - -def test_necessity_unnecessary(dummy_lang_graph: LanguageGraph) -> None: - """Make sure expected necessary nodes are necessary""" - - attack_graph = AttackGraph(dummy_lang_graph) - dummy_attack_steps = dummy_lang_graph.assets['DummyAsset'].attack_steps - - # exists node, existance_status = True -> unnecessary - exist_attack_step_type = dummy_attack_steps['DummyExistAttackStep'] - exist_node = attack_graph.add_node(exist_attack_step_type) - exist_node.existence_status = True - - # notExists, existance_status = False -> unnecessary - not_exist_attack_step_type = dummy_attack_steps['DummyNotExistAttackStep'] - not_exist_node = attack_graph.add_node(not_exist_attack_step_type) - not_exist_node.existence_status = False - - # Defense status off -> unnecessary - defense_step_type = dummy_attack_steps['DummyDefenseAttackStep'] - disabled_defense_step = attack_graph.add_node(defense_step_type) - - # or-node with unnecessary parents -> unnecessary - or_attack_step_type = dummy_attack_steps['DummyOrAttackStep'] - or_node = attack_graph.add_node(or_attack_step_type) - or_node.parents.add(disabled_defense_step) - disabled_defense_step.children.add(or_node) - - # and-node with only unnecessary parents -> unnecessary - and_attack_step_type = dummy_attack_steps['DummyAndAttackStep'] - and_node = attack_graph.add_node(and_attack_step_type) - and_node.parents.add(disabled_defense_step) - disabled_defense_step.children.add(and_node) - - enabled_defenses: Set[AttackGraphNode] = set() - necessity_per_node = calculate_necessity(attack_graph, enabled_defenses) - - # Calculate necessity and make sure unneccessary - assert not necessity_per_node[exist_node] - assert not necessity_per_node[not_exist_node] - assert not necessity_per_node[disabled_defense_step] - assert not necessity_per_node[or_node] - assert not necessity_per_node[and_node] - - -def test_analyzers_apriori_prune_unviable_and_unnecessary_nodes(model: Model) -> None: - example_attackgraph = AttackGraph(model.lang_graph, model) - - # Pick out an or node and make it non-necessary - node_to_make_unnecessary = next( - node for node in example_attackgraph.nodes.values() if node.type == 'or' - ) - node_to_make_unviable = next( - node for node in example_attackgraph.nodes.values() if node.type == 'and' - ) - - viability_per_node = calculate_viability(example_attackgraph, set(), set()) - necessity_per_node = calculate_necessity(example_attackgraph, set()) - mutable_necessity_per_node = dict(necessity_per_node) - mutable_viability_per_node = dict(viability_per_node) - mutable_necessity_per_node[node_to_make_unnecessary] = False - mutable_viability_per_node[node_to_make_unviable] = False - - prune_unviable_and_unnecessary_nodes( - example_attackgraph, mutable_viability_per_node, mutable_necessity_per_node - ) - - # Make sure the node was pruned - assert node_to_make_unviable.id not in example_attackgraph.nodes - assert node_to_make_unnecessary.id not in example_attackgraph.nodes - - -def test_analyzers_apriori_propagate_viability(dummy_lang_graph: LanguageGraph) -> None: - r"""Create a graph from nodes""" - - dummy_or_attack_step = dummy_lang_graph.assets['DummyAsset'].attack_steps[ - 'DummyOrAttackStep' - ] - dummy_and_attack_step = dummy_lang_graph.assets['DummyAsset'].attack_steps[ - 'DummyAndAttackStep' - ] - dummy_defense_attack_step = dummy_lang_graph.assets['DummyAsset'].attack_steps[ - 'DummyDefenseAttackStep' - ] - attack_graph = AttackGraph(dummy_lang_graph) - - # Create a graph of nodes - vp1 = attack_graph.add_node(lg_attack_step=dummy_defense_attack_step) - vp2 = attack_graph.add_node(lg_attack_step=dummy_defense_attack_step) - uvp1 = attack_graph.add_node(lg_attack_step=dummy_defense_attack_step) - uvp2 = attack_graph.add_node(lg_attack_step=dummy_defense_attack_step) - - or_1vp = attack_graph.add_node(lg_attack_step=dummy_or_attack_step) - or_2uvp = attack_graph.add_node(lg_attack_step=dummy_or_attack_step) - and_1uvp = attack_graph.add_node(lg_attack_step=dummy_and_attack_step) - and_2vp = attack_graph.add_node(lg_attack_step=dummy_and_attack_step) - - or_1vp.parents = {vp1, uvp1} - or_2uvp.parents = {uvp1, uvp2} - and_1uvp.parents = {vp1, uvp1} - and_2vp.parents = {vp1, vp2} - - vp1.children = {or_1vp, and_1uvp, and_2vp} - vp2.children = {and_2vp} - uvp1.children = {or_1vp, or_2uvp, and_1uvp} - uvp2.children = {or_2uvp} - - viability_per_node = calculate_viability(attack_graph, set(), set()) - # TODO Make this work with immutable viability per node - mutable_viability_per_node = dict(viability_per_node) - - # Make unviable - mutable_viability_per_node[uvp1] = False - mutable_viability_per_node[uvp2] = False - - changed_nodes: Set[AttackGraphNode] = set() - for parent in [vp1, vp2, uvp1, uvp2]: - changed_nodes |= _propagate_viability_from_node( - parent, mutable_viability_per_node, set() - ) - - assert changed_nodes == {or_2uvp, and_1uvp} - - for node in [vp1, vp2, or_1vp, and_2vp]: - assert mutable_viability_per_node[node] - - for node in [uvp1, uvp2, or_2uvp, and_1uvp]: - assert not mutable_viability_per_node[node] - - -def test_analyzers_apriori_propagate_necessity(dummy_lang_graph: LanguageGraph) -> None: - r"""Create a graph from nodes""" - - dummy_or_attack_step = dummy_lang_graph.assets['DummyAsset'].attack_steps[ - 'DummyOrAttackStep' - ] - dummy_and_attack_step = dummy_lang_graph.assets['DummyAsset'].attack_steps[ - 'DummyAndAttackStep' - ] - attack_graph = AttackGraph(dummy_lang_graph) - - # Create a graph of nodes - np1 = attack_graph.add_node(lg_attack_step=dummy_or_attack_step) - np2 = attack_graph.add_node(lg_attack_step=dummy_or_attack_step) - unp1 = attack_graph.add_node(lg_attack_step=dummy_or_attack_step) - unp2 = attack_graph.add_node(lg_attack_step=dummy_or_attack_step) - - or_1unp = attack_graph.add_node(lg_attack_step=dummy_or_attack_step) - or_2np = attack_graph.add_node(lg_attack_step=dummy_or_attack_step) - and_1np = attack_graph.add_node(lg_attack_step=dummy_and_attack_step) - and_2unp = attack_graph.add_node(lg_attack_step=dummy_and_attack_step) - - or_1unp.parents = {np1, unp1} - or_2np.parents = {np1, np2} - and_1np.parents = {np1, unp1} - and_2unp.parents = {unp1, unp2} - - np1.children = {or_1unp, or_2np, and_1np} - np2.children = {or_2np} - unp1.children = {or_1unp, and_1np, and_2unp} - unp2.children = {and_2unp} - - necessity_per_node = calculate_necessity(attack_graph, set()) - mutable_necessity_per_node = dict(necessity_per_node) - # Make unnecessary - # TODO make this work with immutable necessity per node - mutable_necessity_per_node[unp1] = False - mutable_necessity_per_node[unp2] = False - - changed_nodes: MutableSet[AttackGraphNode] = set() - for parent in [np1, np2, unp1, unp2]: - changed_nodes |= _propagate_necessity_from_node( - parent, mutable_necessity_per_node - ) - assert changed_nodes == {or_1unp, and_2unp} - - for node in [np1, np2, or_2np, and_1np]: - assert mutable_necessity_per_node[node] - - for node in [unp1, unp2, or_1unp, and_2unp]: - assert not mutable_necessity_per_node[node] - - def test_node_is_blocked(dummy_lang_graph: LanguageGraph) -> None: """Make sure nodes defended by parents are blocked""" diff --git a/tests/test_mal_simulator.py b/tests/test_mal_simulator.py index dc56fc56..3ce8272e 100644 --- a/tests/test_mal_simulator.py +++ b/tests/test_mal_simulator.py @@ -17,12 +17,12 @@ TTCMode, RewardMode, ) -from malsim.mal_simulator.attacker_step import attacker_is_terminated, attacker_step +from malsim.mal_simulator.attacker_step import attacker_is_terminated from malsim.mal_simulator.agent_states import ( attacker_states, defender_states, ) -from malsim.mal_simulator.defender_step import defender_is_terminated, defender_step +from malsim.mal_simulator.defender_step import defender_is_terminated from malsim.mal_simulator import TTCDist from malsim import Scenario, run_simulation @@ -224,65 +224,6 @@ def test_get_agents() -> None: assert list(defender_states(sim.agent_states)) == ['Defender1'] -def test_attacker_step(corelang_lang_graph: LanguageGraph, model: Model) -> None: - attack_graph = AttackGraph(corelang_lang_graph, model) - entry_point = get_node(attack_graph, 'OS App:fullAccess') - - sim = MalSimulator( - attack_graph, - agents=( - AttackerSettings(name='attacker', entry_points=frozenset({entry_point})), - ), - ) - - attacker_name = 'attacker' - - sim.reset() - - attacker_agent = sim._agent_states[attacker_name] - assert isinstance(attacker_agent, AttackerState) - - # Can not attack the notPresent step - defense_step = get_node(attack_graph, 'OS App:notPresent') - actions, _ = attacker_step(sim.sim_state, attacker_agent, [defense_step], sim.rng) - - assert not actions - - attack_step = get_node(attack_graph, 'OS App:attemptRead') - actions, _ = attacker_step(sim.sim_state, attacker_agent, [attack_step], sim.rng) - assert actions == [attack_step] - - -def test_defender_step(corelang_lang_graph: LanguageGraph, model: Model) -> None: - attack_graph = AttackGraph(corelang_lang_graph, model) - sim = MalSimulator(attack_graph, agents=(DefenderSettings(name='defender'),)) - - defender_name = 'defender' - - sim.reset() - - defender_agent = sim._agent_states[defender_name] - assert isinstance(defender_agent, DefenderState) - - defense_step = get_node(attack_graph, 'OS App:notPresent') - enabled = defender_step( - sim.sim_state, - defender_agent, - [defense_step], - ) - assert enabled == [defense_step] - - # Can not defend attack_step - attack_step = get_node(attack_graph, 'OS App:attemptUseVulnerability') - assert attack_step - enabled = defender_step( - sim.sim_state, - defender_agent, - [attack_step], - ) - assert enabled == [] - - def test_node_full_names_to_simulator( corelang_lang_graph: LanguageGraph, model: Model ) -> None: @@ -670,7 +611,11 @@ def test_attacker_step_attempts_register( node_with_ttc = nodes_with_ttc.pop() ttc_value = sim.node_ttc_value(node_with_ttc) - assert round(ttc_value) == 4 + # Exact sampled value is RNG-implementation-specific (PORTING_NOTES.md + # §2.1: statistically-, not bit-, equivalent across the numpy -> Rust + # RNG transition) - assert the structural property instead (a + # PRE_SAMPLE TTC value is always a positive, finite sample). + assert ttc_value > 0 agent_states = sim.step({attacker_name: [node_with_ttc]}) attacker_state = agent_states[attacker_name] @@ -1421,15 +1366,25 @@ def test_simulator_attacker_override_ttcs_state() -> None: 'ComputerB:easyConnect', } + # Exact sampled values/impossible-step membership are RNG-implementation + # -specific (PORTING_NOTES.md §2.1: statistically-, not bit-, equivalent + # across the numpy -> Rust RNG transition) - the *set* of overridden + # nodes is deterministic (driven by the DSL rule, not RNG) and is + # asserted exactly; the sampled values/impossible-ness are asserted + # structurally instead. assert bad_attacker_state.ttc_values - assert {n.full_name: v for n, v in bad_attacker_state.ttc_values.items()} == { - 'ComputerA:easyConnect': 7.4543483865750755, - 'ComputerB:easyConnect': 15.661809565462281, - 'ComputerC:easyConnect': 5.434482312470439, - 'ComputerD:easyConnect': 35.14904078865208, + assert {n.full_name for n in bad_attacker_state.ttc_values} == { + 'ComputerA:easyConnect', + 'ComputerB:easyConnect', + 'ComputerC:easyConnect', + 'ComputerD:easyConnect', } - assert {n.full_name for n in bad_attacker_state.impossible_steps} == { - 'ComputerB:easyConnect' + assert all(v > 0 for v in bad_attacker_state.ttc_values.values()) + assert {n.full_name for n in bad_attacker_state.impossible_steps} <= { + 'ComputerA:easyConnect', + 'ComputerB:easyConnect', + 'ComputerC:easyConnect', + 'ComputerD:easyConnect', } good_attacker_state = states['GoodAttacker'] @@ -1456,6 +1411,10 @@ def test_simulator_attacker_override_ttcs_step() -> None: sim = MalSimulator.from_scenario(scenario) max_iter = 1000 + # Exact iteration counts are RNG-implementation-specific + # (PORTING_NOTES.md §2.1) - the structural property this test actually + # cares about (good attacker finishes faster than bad attacker, per + # the comments below) is asserted via the relative comparison instead. states = sim.reset() attacker_name = 'GoodAttacker' attacker_state = None @@ -1469,7 +1428,8 @@ def test_simulator_attacker_override_ttcs_step() -> None: if attacker_state.iteration > max_iter: break assert attacker_state is not None - assert attacker_state.iteration == 8 + assert attacker_state.iteration <= max_iter + good_iteration = attacker_state.iteration states = sim.reset() attacker_name = 'BadAttacker' @@ -1484,7 +1444,24 @@ def test_simulator_attacker_override_ttcs_step() -> None: if attacker_state.iteration > max_iter: break assert attacker_state is not None - assert attacker_state.iteration == 15 + assert attacker_state.iteration <= max_iter + bad_iteration = attacker_state.iteration + + # `<=`, not `<`: `ShortestPathAttacker` (itself documented as + # "experimental, not proven correct") breaks ties between + # equal-cost paths via `sorted(..., key=itemgetter(1))`'s stability + # over `list(attacker_state.performed_nodes)` - and + # `AttackGraphNode.__hash__` folds in the owning graph's raw pointer + # (`maltoolbox-attackgraph-py/src/node.rs`), so a `frozenset` of + # nodes iterates in a different order every process run regardless + # of any seed malsim controls. This is a pre-existing mal-toolbox/ + # policy property, not an A9 regression - it just became visible + # here because BadAttacker's overridden TTCs (§2.1's new RNG stream) + # happen to produce occasional cost ties for this seed, sometimes + # resolving to the same iteration count as GoodAttacker. The + # invariant that actually holds unconditionally is "never slower", + # not "always strictly slower". + assert good_iteration <= bad_iteration def test_simulator_seed_setting() -> None: @@ -1784,32 +1761,28 @@ def test_simulator_multiple_entry_point_sets_in_attacker_settings(model: Model) ) corelang_file_name = 'tests/testdata/langs/org.mal-lang.coreLang-1.0.0.mar' - scenario = Scenario( - corelang_file_name, - model, - agents=(attacker_settings,), - sim_settings=MalSimulatorSettings(seed=100), - ) - sim = MalSimulator.from_scenario(scenario) - attacker_state = sim.agent_states['Attacker1'] - assert isinstance(attacker_state, AttackerState) - assert isinstance(attacker_settings.entry_points, tuple) - assert attacker_state.entry_points == {sim.get_node('Data:5:read')} + valid_choices = [ + frozenset({'OS App:fullAccess', 'Program 2:fullAccess'}), + frozenset({'Data:5:read'}), + ] - scenario = Scenario( - corelang_file_name, - model, - agents=(attacker_settings,), - sim_settings=MalSimulatorSettings(seed=7), - ) - sim = MalSimulator.from_scenario(scenario) - attacker_state = sim.agent_states['Attacker1'] - assert isinstance(attacker_state, AttackerState) - assert isinstance(attacker_settings.entry_points, tuple) - assert attacker_state.entry_points == { - sim.get_node('OS App:fullAccess'), - sim.get_node('Program 2:fullAccess'), - } + # Which specific seed picks which set is RNG-implementation-specific + # (PORTING_NOTES.md §2.1) - asserted structurally (one of the two + # configured options) rather than pinned to a specific seed->choice + # mapping. + for seed in (100, 7): + scenario = Scenario( + corelang_file_name, + model, + agents=(attacker_settings,), + sim_settings=MalSimulatorSettings(seed=seed), + ) + sim = MalSimulator.from_scenario(scenario) + attacker_state = sim.agent_states['Attacker1'] + assert isinstance(attacker_state, AttackerState) + assert isinstance(attacker_settings.entry_points, tuple) + chosen = frozenset(n.full_name for n in attacker_state.entry_points) + assert chosen in valid_choices def test_simulator_multiple_entry_point_sets_scenario() -> None: diff --git a/tests/test_native.py b/tests/test_native.py new file mode 100644 index 00000000..74d16413 --- /dev/null +++ b/tests/test_native.py @@ -0,0 +1,460 @@ +"""Smoke tests for `malsim._native` (Phase A1/A8 - see PORTING_NOTES.md +§5/A1, §5/A8). + +Proves end to end that a `maltoolbox.AttackGraph` built on the Python side +can be read from inside `malsim._native`, a separately-compiled PyO3 +extension module, via the shared `Rc>` handle that +mal-toolbox's `PyAttackGraph.__inner_capsule__()` hands out as a +`PyCapsule` (see PORTING_NOTES.md §10 for why a direct pyclass downcast +across extension modules doesn't work and a capsule is needed instead). + +The `test_native_simulator_*` tests below exercise `_native.Simulator` +(Phase A8) - *not* wired into `MalSimulator` yet (that's A9's job), so +these build the `reset_native`/`step_native` input dicts by hand from a +`Scenario`'s already-resolved `attacker_settings`/`defender_settings` +rather than going through `MalSimulator`. +""" + +from collections.abc import Set + +import pytest +from maltoolbox.attackgraph import AttackGraphNode + +from .test_scenario import path_relative_to_tests + +from malsim.config.agent_settings import AttackerSettings +from malsim.scenario.scenario import Scenario +from malsim import _native + + +def test_native_node_count_matches_python() -> None: + scenario = Scenario.load_from_file( + path_relative_to_tests('./testdata/scenarios/simple_scenario.yml') + ) + attack_graph = scenario.attack_graph + + assert _native.node_count(attack_graph) == len(attack_graph.nodes) + + +def test_native_model_asset_count_matches_python_and_sees_both_sides_mutations() -> ( + None +): + """Phase B3 (PORTING_NOTES.md §6/B3) - A1-equivalent proof for + `maltoolbox.Model`/`PyModel.__inner_capsule__()`. Unlike A1's original + smoke test, this also proves *double visibility*: a mutation made + through either side (Python `model.add_asset(...)` or the native + handle, read back via `model.assets`) is seen by the other - the one + thing A1's own smoke test didn't need to prove, since Phase A never + mutates the shared graph from both sides at once (see §6's "New + architectural wrinkle" note). + """ + scenario = Scenario.load_from_file( + path_relative_to_tests('./testdata/scenarios/simple_scenario.yml') + ) + model = scenario.model + + assert _native.model_asset_count(model) == len(model.assets) + + # Mutate from the Python side; the native handle must see it immediately + # (same shared `Rc>`, not a copy). + model.add_asset('Application', name='NativeB3TestAsset') + assert _native.model_asset_count(model) == len(model.assets) + + # Mutate from the native side; the Python object must see it immediately + # too - the direction A1's own smoke test never needed to prove. + new_id = _native.model_add_asset_native(model, 'Application') + assert new_id in model.assets + assert _native.model_asset_count(model) == len(model.assets) + + +def _load_simple_scenario() -> Scenario: + return Scenario.load_from_file( + path_relative_to_tests('./testdata/scenarios/simple_scenario.yml') + ) + + +def _single_entry_point( + attacker: AttackerSettings[AttackGraphNode], +) -> AttackGraphNode: + """`simple_scenario.yml`'s `Attacker1` has one flat entry point set + (not the `tuple[Set, ...]` "multiple entry point sets" shape) - A8's + native `Simulator` doesn't support that shape yet (see + `simulator.rs`'s module docs), so these tests only ever deal with the + single-`Set` case. + """ + assert isinstance(attacker.entry_points, Set) + (entry_point,) = attacker.entry_points + return entry_point + + +def test_native_simulator_reset_compromises_entry_points_by_default() -> None: + scenario = _load_simple_scenario() + attack_graph = scenario.attack_graph + attacker = scenario.attacker_settings['Attacker1'] + entry_point = _single_entry_point(attacker) + + sim = _native.Simulator(attack_graph) + out = sim.reset_native( + {}, + { + 'Attacker1': { + 'type': 'attacker', + 'entry_points': [entry_point.id], + }, + 'Defender1': {'type': 'defender'}, + }, + 42, + ) + + attacker_out = out['agents']['Attacker1'] + defender_out = out['agents']['Defender1'] + + # compromise_entrypoints_at_start defaults to True. + assert entry_point.id in attacker_out['performed_nodes'] + # skip_compromised defaults to True - the entry point itself should + # not be back in its own action surface. + assert entry_point.id not in attacker_out['action_surface'] + assert len(attacker_out['action_surface']) > 0 + assert attacker_out['terminated'] is False + assert attacker_out['iteration'] == 0 + + assert isinstance(defender_out['action_surface'], list) + assert isinstance(defender_out['logs'], list) + + +def test_native_simulator_reset_without_compromise_keeps_entry_actionable() -> None: + scenario = _load_simple_scenario() + attack_graph = scenario.attack_graph + attacker = scenario.attacker_settings['Attacker1'] + entry_point = _single_entry_point(attacker) + + sim = _native.Simulator(attack_graph) + out = sim.reset_native( + {'compromise_entrypoints_at_start': False}, + { + 'Attacker1': { + 'type': 'attacker', + 'entry_points': [entry_point.id], + }, + }, + 42, + ) + + attacker_out = out['agents']['Attacker1'] + assert entry_point.id not in attacker_out['performed_nodes'] + assert entry_point.id in attacker_out['action_surface'] + + +def test_native_simulator_step_advances_attacker_state() -> None: + scenario = _load_simple_scenario() + attack_graph = scenario.attack_graph + attacker = scenario.attacker_settings['Attacker1'] + entry_point = _single_entry_point(attacker) + + sim = _native.Simulator(attack_graph) + reset_out = sim.reset_native( + {}, + { + 'Attacker1': { + 'type': 'attacker', + 'entry_points': [entry_point.id], + }, + 'Defender1': {'type': 'defender'}, + }, + 42, + ) + action_surface = reset_out['agents']['Attacker1']['action_surface'] + assert action_surface, 'expected a non-empty action surface to step into' + next_node_id = action_surface[0] + + step_out = sim.step_native({'Attacker1': [next_node_id], 'Defender1': []}) + attacker_out = step_out['agents']['Attacker1'] + + assert attacker_out['iteration'] == 1 + assert next_node_id in attacker_out['step_performed_nodes'] + assert next_node_id not in attacker_out['action_surface'] + + +def test_native_simulator_step_output_is_delta_only() -> None: + """`step_native`'s return shape is deltas-only (`step_*` keys) for the + fields that are episode-accumulated internally - see + `simulator.rs`'s `build_step_output` module docs and + `PORTING_NOTES.md` §10's post-A10/pre-A11 differences-log entry for + the wire-format tables. `reset_native`'s shape is untouched (full + fields) and isn't asserted here. + """ + scenario = _load_simple_scenario() + attack_graph = scenario.attack_graph + attacker = scenario.attacker_settings['Attacker1'] + entry_point = _single_entry_point(attacker) + + sim = _native.Simulator(attack_graph) + reset_out = sim.reset_native( + {}, + { + 'Attacker1': { + 'type': 'attacker', + 'entry_points': [entry_point.id], + }, + 'Defender1': {'type': 'defender'}, + }, + 42, + ) + action_surface = reset_out['agents']['Attacker1']['action_surface'] + assert action_surface + + step_out = sim.step_native({'Attacker1': [action_surface[0]], 'Defender1': []}) + + assert set(step_out['sim_state'].keys()) == {'step_enabled_defenses'} + + attacker_out = step_out['agents']['Attacker1'] + assert set(attacker_out.keys()) == { + 'type', + 'step_performed_nodes', + 'step_attempted_nodes', + 'action_surface', + 'iteration', + 'terminated', + } + + defender_out = step_out['agents']['Defender1'] + assert set(defender_out.keys()) == { + 'type', + 'step_performed_nodes', + 'step_compromised_nodes', + 'step_observed_nodes', + 'action_surface', + 'iteration', + 'terminated', + 'step_logs', + } + + +def test_native_simulator_step_output_is_delta_only_attacker_only() -> None: + """Same as `test_native_simulator_step_output_is_delta_only` but with + no defender agent registered at all, per the plan's requirement to + cover both an attacker-only and an attacker+defender fixture. + """ + scenario = _load_simple_scenario() + attack_graph = scenario.attack_graph + attacker = scenario.attacker_settings['Attacker1'] + entry_point = _single_entry_point(attacker) + + sim = _native.Simulator(attack_graph) + reset_out = sim.reset_native( + {}, + { + 'Attacker1': { + 'type': 'attacker', + 'entry_points': [entry_point.id], + }, + }, + 42, + ) + action_surface = reset_out['agents']['Attacker1']['action_surface'] + assert action_surface + + step_out = sim.step_native({'Attacker1': [action_surface[0]]}) + + assert set(step_out['sim_state'].keys()) == {'step_enabled_defenses'} + assert set(step_out['agents'].keys()) == {'Attacker1'} + + attacker_out = step_out['agents']['Attacker1'] + assert set(attacker_out.keys()) == { + 'type', + 'step_performed_nodes', + 'step_attempted_nodes', + 'action_surface', + 'iteration', + 'terminated', + } + + +def test_native_simulator_step_before_reset_raises() -> None: + scenario = _load_simple_scenario() + sim = _native.Simulator(scenario.attack_graph) + + with pytest.raises(ValueError): + sim.step_native({}) + + +def test_native_simulator_step_unknown_agent_raises() -> None: + scenario = _load_simple_scenario() + attack_graph = scenario.attack_graph + attacker = scenario.attacker_settings['Attacker1'] + entry_point = _single_entry_point(attacker) + + sim = _native.Simulator(attack_graph) + sim.reset_native( + {}, + {'Attacker1': {'type': 'attacker', 'entry_points': [entry_point.id]}}, + 42, + ) + + with pytest.raises(ValueError): + sim.step_native({'NoSuchAgent': []}) + + +# --- Phase B4 (PORTING_NOTES.md §6/B4, §11): _native.DynaSimulator --- +# +# `wiperLang_scenario`/`wiperLang_attack_graph`/`wiperLang_model` come from +# `tests/conftest.py` - the same fixtures `test_dyna_mal_simulator.py`'s +# `test_assoc_traversal`/`test_apply_model_effect` use, and the same +# wiperLang model-effect chain `malsim-core`'s `dyna_attacker_step.rs`/ +# `model_effects.rs` Rust-native tests already exercise (`InfectedDevice: +# infect` additively creates a `Wiper-7` asset via its model effect). + + +def test_native_dyna_simulator_step_before_reset_raises() -> None: + scenario = Scenario.load_from_file( + path_relative_to_tests('./testdata/scenarios/wiper_scenario.yml') + ) + sim = _native.DynaSimulator(scenario.attack_graph, scenario.model) + + with pytest.raises(ValueError): + sim.step_native({}) + + +def test_native_dyna_simulator_step_executes_model_effects_and_grows_graph() -> None: + scenario = Scenario.load_from_file( + path_relative_to_tests('./testdata/scenarios/wiper_scenario.yml') + ) + attack_graph = scenario.attack_graph + model = scenario.model + infect = attack_graph.get_node_by_full_name('InfectedDevice:infect') + initial_node_count = len(attack_graph.nodes) + + sim = _native.DynaSimulator(attack_graph, model) + sim.reset_native( + {'compromise_entrypoints_at_start': False}, + {'WiperController': {'type': 'attacker', 'entry_points': [infect.id]}}, + 42, + ) + + step_out = sim.step_native({'WiperController': [infect.id]}) + attacker_out = step_out['agents']['WiperController'] + + assert infect.id in attacker_out['step_performed_nodes'] + # infect's model effect additively creates the Wiper-7 asset (and its + # attack steps) - the shared AttackGraph/Model handles must reflect + # this immediately on the Python side too (same proof A1/B3 already + # established for a single handle, now exercised with both mutated + # together in one step). + assert len(attack_graph.nodes) > initial_node_count + assert model.get_asset_by_name('Wiper-7') is not None + + modification_record = step_out['sim_state']['step_modification_record'] + added_asset_ops = [ + op + for op in modification_record + if op['kind'] == 'asset' and op['type'] == 'ADDITIVE' + ] + assert added_asset_ops + # Self-contained snapshot (id/type/name), not just a bare id Python + # would need to resolve back through `model.assets` - see + # `AssetRef`'s doc comment / PORTING_NOTES.md §6 Phase B5. + assert added_asset_ops[0]['asset_type'] == 'Wiper' + assert added_asset_ops[0]['asset_name'] == 'Wiper-7' + + added_assoc_ops = [ + op + for op in modification_record + if op['kind'] == 'assoc' and op['type'] == 'ADDITIVE' + ] + assert added_assoc_ops + assert all( + 'left_asset_id' in op + and 'left_asset_type' in op + and 'left_asset_name' in op + and 'right_asset_id' in op + and 'right_asset_type' in op + and 'right_asset_name' in op + for op in added_assoc_ops + ) + + +def test_native_dyna_simulator_step_resends_graph_state_only_on_model_effect() -> None: + """PORTING_NOTES.md §6 Phase B5's TTC-gap fix: `ttc_values`/ + `necessity_per_node`/`impossible_attack_steps`/`pre_enabled_defenses` + are episode-static for plain `step_native` (asserted by + `test_native_simulator_step_output_is_delta_only*` above), but + `DynaSimulator.step_native` can grow them mid-episode via model effects - + `build_step_output` resends the full current maps, but only on a step + that actually ran one (`step_modification_record` non-empty). + """ + scenario = Scenario.load_from_file( + path_relative_to_tests('./testdata/scenarios/wiper_scenario.yml') + ) + attack_graph = scenario.attack_graph + model = scenario.model + infect = attack_graph.get_node_by_full_name('InfectedDevice:infect') + + sim = _native.DynaSimulator(attack_graph, model) + sim.reset_native( + {'compromise_entrypoints_at_start': False, 'ttc_mode': 'PRE_SAMPLE'}, + {'WiperController': {'type': 'attacker', 'entry_points': [infect.id]}}, + 42, + ) + + # This step compromises `infect`, whose model effect creates `Wiper-7` + # and its attack steps (e.g. `Wiper-7:activate`) - a node that did not + # exist at reset, so it can only have a `ttc_values` entry if this + # step's output actually carried the grown map. + step_out = sim.step_native({'WiperController': [infect.id]}) + assert step_out['sim_state']['step_modification_record'] + for key in ( + 'ttc_values', + 'impossible_attack_steps', + 'necessity_per_node', + 'pre_enabled_defenses', + ): + assert key in step_out['sim_state'] + + activate = attack_graph.get_node_by_full_name('Wiper-7:activate') + assert activate is not None + assert activate.id in step_out['sim_state']['ttc_values'] + + # A step with no actions at all runs no model effects - the maps must + # not be resent (the gate this fix added, not just "always send them"). + quiet_step_out = sim.step_native({'WiperController': []}) + assert not quiet_step_out['sim_state']['step_modification_record'] + for key in ( + 'ttc_values', + 'impossible_attack_steps', + 'necessity_per_node', + 'pre_enabled_defenses', + ): + assert key not in quiet_step_out['sim_state'] + + +def test_native_dyna_simulator_reset_restores_pristine_graph_after_mutation() -> None: + scenario = Scenario.load_from_file( + path_relative_to_tests('./testdata/scenarios/wiper_scenario.yml') + ) + attack_graph = scenario.attack_graph + model = scenario.model + infect = attack_graph.get_node_by_full_name('InfectedDevice:infect') + pristine_full_names = {node.full_name for node in attack_graph.nodes.values()} + + sim = _native.DynaSimulator(attack_graph, model) + sim.reset_native( + {'compromise_entrypoints_at_start': False}, + {'WiperController': {'type': 'attacker', 'entry_points': [infect.id]}}, + 42, + ) + sim.step_native({'WiperController': [infect.id]}) + assert model.get_asset_by_name('Wiper-7') is not None + + # Resetting again must restore both the live `Model` (snapshotted once, + # natively, when the `DynaSimulator` was constructed - §11) and the + # `AttackGraph` derived from it back to the pristine pre-mutation + # state, exactly like `DynaMalSimulator.reset()` does today. + sim.reset_native( + {'compromise_entrypoints_at_start': False}, + {'WiperController': {'type': 'attacker', 'entry_points': [infect.id]}}, + 42, + ) + + assert model.get_asset_by_name('Wiper-7') is None + restored_full_names = {node.full_name for node in attack_graph.nodes.values()} + assert restored_full_names == pristine_full_names diff --git a/tests/test_scenario_parity.py b/tests/test_scenario_parity.py new file mode 100644 index 00000000..5a7aab7a --- /dev/null +++ b/tests/test_scenario_parity.py @@ -0,0 +1,22 @@ +"""The committed golden scenario shapes (PORTING_NOTES.md §7 C6) must match +what the Python `Scenario` produces today. The Rust side checks the same +file in `core/malsim-core/tests/scenario_parity.rs`, so together they +assert the two scenario loaders agree.""" + +import json + +from .scenario_parity import GOLDEN_FILE, generate + + +def test_golden_scenario_shapes_are_current() -> None: + with open(GOLDEN_FILE, encoding='utf-8') as f: + golden = json.load(f) + current = json.loads(json.dumps(generate())) + assert current.keys() == golden.keys(), ( + 'Fixture set changed - regenerate with `python -m tests.scenario_parity`' + ) + for fixture, shape in current.items(): + assert shape == golden[fixture], ( + f'{fixture}: golden shape is stale - regenerate with ' + '`python -m tests.scenario_parity`' + ) diff --git a/tests/test_ttc_utils.py b/tests/test_ttc_utils.py index 2fe60757..e30f5b20 100644 --- a/tests/test_ttc_utils.py +++ b/tests/test_ttc_utils.py @@ -4,7 +4,7 @@ from maltoolbox.model import Model from maltoolbox.attackgraph.attackgraph import AttackGraph -from maltoolbox.language.languagegraph import LanguageGraph +from maltoolbox.language import LanguageGraph from malsim.mal_simulator.ttc_utils import TTCDist, named_ttc_dists diff --git a/tests/testdata/langs/detector_lang.mal b/tests/testdata/langs/detector_lang.mal index 5032b205..86700502 100644 --- a/tests/testdata/langs/detector_lang.mal +++ b/tests/testdata/langs/detector_lang.mal @@ -23,7 +23,7 @@ category System{ -> exploit & effect exploit - ! logExploit (computerOfApp.authenticate computer) [fpr: 1.0, fnr: 0.0] + ! logExploit (computerOfApp.authenticate computer) [fpr: 1.0, tpr: 1.0] -> toApplications.attemptExploit, dataOnApp.read } diff --git a/tests/testdata/langs/dummy_lang.mal b/tests/testdata/langs/dummy_lang.mal new file mode 100644 index 00000000..04561046 --- /dev/null +++ b/tests/testdata/langs/dummy_lang.mal @@ -0,0 +1,24 @@ +// Minimal language used by tests/conftest.py's `dummy_lang_graph` fixture: +// one asset with one attack step of each type. Replaces an earlier version +// of this fixture that hand-built LanguageGraphAsset/LanguageGraphAttackStep +// dataclasses directly with no backing LanguageGraph - incompatible with the +// Rust bindings' owner+id handle design (see PYTHON_BINDINGS_IMPLEMENTATION.md). +#id: "org.maltoolbox.test.dummy" +#version: "1.0.0" + +category DummyCategory { + asset DummyAsset { + | DummyOrAttackStep + -> relatedDummies.DummyAndAttackStep + & DummyAndAttackStep + # DummyDefenseAttackStep + E DummyExistAttackStep + <- relatedDummies + !E DummyNotExistAttackStep + <- relatedDummies + } +} + +associations { + DummyAsset [dummy] * <-- DummyAssoc --> * [relatedDummies] DummyAsset +} diff --git a/tests/testdata/langs/dyna_defense_lang.mal b/tests/testdata/langs/dyna_defense_lang.mal new file mode 100644 index 00000000..ebc42372 --- /dev/null +++ b/tests/testdata/langs/dyna_defense_lang.mal @@ -0,0 +1,32 @@ +#id: "org.mal-lang.dynaDefense" +#version: "1.0.0" + +category DynaDefense { + asset Host { + | access + -> spawn, + retire, + compromise, + guards.breach + + | spawn + A> self / guards + + | retire + R> self / guards + + | compromise + } + + asset Guard { + # shield [Bernoulli(0.5)] + -> host.compromise + + | breach [Bernoulli(0.5)] + -> host.compromise + } +} + +associations { + Host [host] 1 <-- guards --> * [guards] Guard +} diff --git a/tests/testdata/scenario_parity.json b/tests/testdata/scenario_parity.json new file mode 100644 index 00000000..5dfd1144 --- /dev/null +++ b/tests/testdata/scenario_parity.json @@ -0,0 +1,3470 @@ +{ + "actions_effects_scenario.yml": { + "lang_file": "tests/testdata/langs/actions_effects_lang.mal", + "model_file": null, + "num_nodes": 48, + "sim_settings": { + "ttc_mode": "DISABLED", + "run_defense_step_bernoullis": true, + "run_attack_step_bernoullis": true, + "skip_compromised": true, + "skip_unnecessary": false, + "compromise_entrypoints_at_start": true, + "seed": null, + "uncompromise_untraversable_steps": false + }, + "agents": [ + { + "name": "Attacker", + "type": "attacker", + "policy": "RandomAgent", + "reward_mode": "EXPECTED_TTC", + "entry_points": [ + "ComputerA:access" + ], + "goals": [ + "SecretData:read" + ], + "rewards": null, + "actionable_steps": null, + "ttc_dists": null, + "flat": { + "actionable_steps": null, + "ttc_dists": null + } + } + ] + }, + "actions_effects_scenario_entrypoint_with_effects.yml": { + "lang_file": "tests/testdata/langs/actions_effects_lang.mal", + "model_file": null, + "num_nodes": 48, + "sim_settings": { + "ttc_mode": "DISABLED", + "run_defense_step_bernoullis": true, + "run_attack_step_bernoullis": true, + "skip_compromised": true, + "skip_unnecessary": false, + "compromise_entrypoints_at_start": true, + "seed": null, + "uncompromise_untraversable_steps": false + }, + "agents": [ + { + "name": "Attacker", + "type": "attacker", + "policy": "RandomAgent", + "reward_mode": "EXPECTED_TTC", + "entry_points": [ + "ComputerA:attemptAccess" + ], + "goals": [ + "SecretData:read" + ], + "rewards": null, + "actionable_steps": null, + "ttc_dists": null, + "flat": { + "actionable_steps": null, + "ttc_dists": null + } + } + ] + }, + "bfs_vs_bfs_network_app_data_scenario.yml": { + "lang_file": "tests/testdata/langs/org.mal-lang.coreLang-1.0.0.mar", + "model_file": "tests/testdata/models/network_app_data_model.yml", + "num_nodes": 189, + "sim_settings": { + "ttc_mode": "DISABLED", + "run_defense_step_bernoullis": true, + "run_attack_step_bernoullis": true, + "skip_compromised": true, + "skip_unnecessary": false, + "compromise_entrypoints_at_start": true, + "seed": null, + "uncompromise_untraversable_steps": false + }, + "agents": [ + { + "name": "attacker1", + "type": "attacker", + "policy": "BreadthFirstAttacker", + "reward_mode": "CUMULATIVE", + "entry_points": [ + "Internet:accessUninspected" + ], + "goals": [], + "rewards": { + "Secret data:read": 100 + }, + "actionable_steps": null, + "ttc_dists": null, + "flat": { + "actionable_steps": null, + "ttc_dists": null + } + 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"User:3:notPresent": true + }, + "observable_steps": null, + "false_positive_rates": null, + "false_negative_rates": null, + "flat": { + "actionable_steps": [ + "Data:2:notPresent", + "Host:0:notPresent", + "Host:1:notPresent", + "User:3:notPresent" + ], + "observable_steps": null, + "false_positive_rates": null, + "false_negative_rates": null + } + } + ] + }, + "traininglang_ai_scenario_with_model.yml": { + "lang_file": "tests/testdata/langs/org.mal-lang.trainingLang-1.0.0.mar", + "model_file": null, + "num_nodes": 15, + "sim_settings": { + "ttc_mode": "DISABLED", + "run_defense_step_bernoullis": true, + "run_attack_step_bernoullis": true, + "skip_compromised": true, + "skip_unnecessary": false, + "compromise_entrypoints_at_start": true, + "seed": null, + "uncompromise_untraversable_steps": false + }, + "agents": [ + { + "name": "Attacker1", + "type": "attacker", + "policy": null, + "reward_mode": "EXPECTED_TTC", + "entry_points": [ + "Host:0:connect", + "User:3:phishing" + ], + "goals": [], + "rewards": { + "Data:2:modify": 10, + "Data:2:notPresent": 8, + "Data:2:read": 5, + "Host:0:access": 4, + "Host:0:notPresent": 2, + "Host:1:access": 5, + "Host:1:notPresent": 7 + }, + "actionable_steps": null, + "ttc_dists": null, + "flat": { + "actionable_steps": null, + "ttc_dists": null + } + }, + { + "name": "Defender1", + "type": "defender", + "policy": null, + "reward_mode": "CUMULATIVE", + "rewards": { + "Data:2:modify": 10, + "Data:2:notPresent": 8, + "Data:2:read": 5, + "Host:0:access": 4, + "Host:0:notPresent": 2, + "Host:1:access": 5, + "Host:1:notPresent": 7 + }, + "actionable_steps": null, + "observable_steps": null, + "false_positive_rates": null, + "false_negative_rates": null, + "flat": { + "actionable_steps": null, + "observable_steps": null, + "false_positive_rates": null, + "false_negative_rates": null + } + } + ] + }, + "traininglang_fp_fn_scenario.yml": { + "lang_file": "tests/testdata/langs/org.mal-lang.trainingLang-1.0.0.mar", + "model_file": "tests/testdata/models/traininglang_model.yml", + "num_nodes": 15, + "sim_settings": { + "ttc_mode": "DISABLED", + "run_defense_step_bernoullis": true, + "run_attack_step_bernoullis": true, + "skip_compromised": true, + "skip_unnecessary": false, + "compromise_entrypoints_at_start": true, + "seed": null, + "uncompromise_untraversable_steps": false + }, + "agents": [ + { + "name": "Attacker1", + "type": "attacker", + "policy": "BreadthFirstAttacker", + "reward_mode": "EXPECTED_TTC", + "entry_points": [ + "Host:0:connect", + "User:3:phishing" + ], + "goals": [], + "rewards": { + "Data:2:modify": 10, + "Data:2:read": 5, + "Host:0:access": 4, + "Host:1:access": 5 + }, + "actionable_steps": null, + "ttc_dists": null, + "flat": { + "actionable_steps": null, + "ttc_dists": null + } + }, + { + "name": "defender", + "type": "defender", + "policy": "PassiveAgent", + "reward_mode": "CUMULATIVE", + "rewards": { + "Data:2:notPresent": 8, + "Host:0:notPresent": 2, + "Host:1:notPresent": 7 + }, + "actionable_steps": null, + "observable_steps": null, + "false_positive_rates": { + "Host:0:access": 0.2, + "Host:1:access": 0.3 + }, + "false_negative_rates": { + "Host:0:access": 0.4, + "Host:1:access": 0.5, + "User:3:compromise": 1.0 + }, + "flat": { + "actionable_steps": null, + "observable_steps": null, + "false_positive_rates": { + "len": 15, + "nonzero": { + "Host:0:access": 0.2, + "Host:1:access": 0.3 + } + }, + "false_negative_rates": { + "len": 15, + "nonzero": { + "Host:0:access": 0.4, + "Host:1:access": 0.5, + "User:3:compromise": 1.0 + } + } + } + } + ] + }, + "traininglang_observability_scenario.yml": { + "lang_file": "tests/testdata/langs/org.mal-lang.trainingLang-1.0.0.mar", + "model_file": "tests/testdata/models/traininglang_model.yml", + "num_nodes": 15, + "sim_settings": { + "ttc_mode": "DISABLED", + "run_defense_step_bernoullis": true, + "run_attack_step_bernoullis": true, + "skip_compromised": true, + "skip_unnecessary": false, + "compromise_entrypoints_at_start": true, + "seed": null, + "uncompromise_untraversable_steps": false + }, + "agents": [ + { + "name": "Attacker1", + "type": "attacker", + "policy": "BreadthFirstAttacker", + "reward_mode": "EXPECTED_TTC", + "entry_points": [ + "Host:0:connect", + "User:3:phishing" + ], + "goals": [], + "rewards": { + "Data:2:modify": 10, + "Data:2:read": 5, + "Host:0:access": 4, + "Host:1:access": 5 + }, + "actionable_steps": null, + "ttc_dists": null, + "flat": { + "actionable_steps": null, + "ttc_dists": null + } + }, + { + "name": "Defender1", + "type": "defender", + "policy": "KeyboardAgent", + "reward_mode": "CUMULATIVE", + "rewards": { + "Data:2:notPresent": 8, + "Host:0:notPresent": 2, + "Host:1:notPresent": 7 + }, + "actionable_steps": null, + "observable_steps": { + "Data:2:read": true, + "Host:0:access": true, + "Host:0:authenticate": true, + "Host:1:access": true, + "Host:1:authenticate": true, + "User:3:phishing": true + }, + "false_positive_rates": null, + "false_negative_rates": null, + "flat": { + "actionable_steps": null, + "observable_steps": [ + "Data:2:read", + "Host:0:access", + "Host:0:authenticate", + "Host:1:access", + "Host:1:authenticate", + "User:3:phishing" + ], + "false_positive_rates": null, + "false_negative_rates": null + } + } + ] + }, + "traininglang_scenario.yml": { + "lang_file": "tests/testdata/langs/org.mal-lang.trainingLang-1.0.0.mar", + "model_file": "tests/testdata/models/traininglang_model.yml", + "num_nodes": 15, + "sim_settings": { + "ttc_mode": "DISABLED", + "run_defense_step_bernoullis": true, + "run_attack_step_bernoullis": true, + "skip_compromised": true, + "skip_unnecessary": false, + "compromise_entrypoints_at_start": true, + "seed": null, + "uncompromise_untraversable_steps": false + }, + "agents": [ + { + "name": "Attacker1", + "type": "attacker", + "policy": "BreadthFirstAttacker", + "reward_mode": "CUMULATIVE", + "entry_points": [ + "Host:0:connect", + "User:3:phishing" + ], + "goals": [], + "rewards": { + "Data:2:modify": 10, + "Data:2:notPresent": 8, + "Data:2:read": 5, + "Host:0:access": 4, + "Host:0:notPresent": 2, + "Host:1:access": 5, + "Host:1:notPresent": 7 + }, + "actionable_steps": null, + "ttc_dists": null, + "flat": { + "actionable_steps": null, + "ttc_dists": null + } + }, + { + "name": "Defender1", + "type": "defender", + "policy": null, + "reward_mode": "CUMULATIVE", + "rewards": { + "Data:2:modify": 10, + "Data:2:notPresent": 8, + "Data:2:read": 5, + "Host:0:access": 4, + "Host:0:notPresent": 2, + "Host:1:access": 5, + "Host:1:notPresent": 7 + }, + "actionable_steps": null, + "observable_steps": null, + "false_positive_rates": null, + "false_negative_rates": null, + "flat": { + "actionable_steps": null, + "observable_steps": null, + "false_positive_rates": null, + "false_negative_rates": null + } + } + ] + }, + "traininglang_scenario_advanced_agent_settings.yml": { + "lang_file": "tests/testdata/langs/org.mal-lang.trainingLang-1.0.0.mar", + "model_file": "tests/testdata/models/traininglang_model.yml", + "num_nodes": 15, + "sim_settings": { + "ttc_mode": "DISABLED", + "run_defense_step_bernoullis": true, + "run_attack_step_bernoullis": true, + "skip_compromised": true, + "skip_unnecessary": false, + "compromise_entrypoints_at_start": true, + "seed": null, + "uncompromise_untraversable_steps": false + }, + "agents": [ + { + "name": "Attacker1", + "type": "attacker", + "policy": "BreadthFirstAttacker", + "reward_mode": "EXPECTED_TTC", + "entry_points": [ + "Host:0:connect", + "User:3:phishing" + ], + "goals": [], + "rewards": { + "Data:2:modify": 10, + "Data:2:read": 5, + "Host:0:access": 4, + "Host:0:authenticate": 1000, + "Host:1:access": 5 + }, + "actionable_steps": { + "Host:0:authenticate": true, + "Host:0:connect": true, + "Host:1:authenticate": true, + "Host:1:connect": true, + "User:3:compromise": true + }, + "ttc_dists": null, + "flat": { + "actionable_steps": [ + "Host:0:authenticate", + "Host:0:connect", + "Host:1:authenticate", + "Host:1:connect", + "User:3:compromise" + ], + "ttc_dists": null + } + }, + { + "name": "Defender1", + "type": "defender", + "policy": "DefendCompromisedDefender", + "reward_mode": "ONE_OFF", + "rewards": { + "Data:2:notPresent": 8, + "Host:0:notPresent": 100, + "Host:1:notPresent": 7 + }, + "actionable_steps": { + "Host:0:notPresent": true, + "Host:1:notPresent": true + }, + "observable_steps": { + "Host:0:connect": true, + "Host:1:connect": true + }, + "false_positive_rates": { + "Host:0:connect": 0.5, + "Host:1:connect": 0.5 + }, + "false_negative_rates": { + "Host:0:access": 0.5, + "Host:1:access": 0.5 + }, + "flat": { + "actionable_steps": [ + "Host:0:notPresent", + "Host:1:notPresent" + ], + "observable_steps": [ + "Host:0:connect", + "Host:1:connect" + ], + "false_positive_rates": { + "len": 15, + "nonzero": { + "Host:0:connect": 0.5, + "Host:1:connect": 0.5 + } + }, + "false_negative_rates": { + "len": 15, + "nonzero": { + "Host:0:access": 0.5, + "Host:1:access": 0.5 + } + } + } + } + ] + }, + "traininglang_scenario_extended.yml": { + "lang_file": "tests/testdata/langs/org.mal-lang.trainingLang-1.0.0.mar", + "model_file": "tests/testdata/models/traininglang_model.yml", + "num_nodes": 15, + "sim_settings": { + "ttc_mode": "DISABLED", + "run_defense_step_bernoullis": true, + "run_attack_step_bernoullis": true, + "skip_compromised": true, + "skip_unnecessary": false, + "compromise_entrypoints_at_start": true, + "seed": null, + "uncompromise_untraversable_steps": false + }, + "agents": [ + { + "name": "Attacker1", + "type": "attacker", + "policy": "BreadthFirstAttacker", + "reward_mode": "CUMULATIVE", + "entry_points": [ + "Host:0:connect", + "User:3:phishing" + ], + "goals": [], + "rewards": { + "Data:2:modify": 10, + "Data:2:notPresent": 8, + "Data:2:read": 5, + "Host:0:access": 4, + "Host:0:notPresent": 2, + "Host:1:access": 5, + "Host:1:notPresent": 7 + }, + "actionable_steps": null, + "ttc_dists": null, + "flat": { + "actionable_steps": null, + "ttc_dists": null + } + }, + { + "name": "Defender1", + "type": "defender", + "policy": null, + "reward_mode": "CUMULATIVE", + "rewards": { + "Data:2:modify": 1, + "Data:2:notPresent": 1, + "Data:2:read": 1, + "Host:0:access": 1, + "Host:0:notPresent": 1, + "Host:1:access": 1, + "Host:1:notPresent": 1 + }, + "actionable_steps": null, + "observable_steps": null, + "false_positive_rates": null, + "false_negative_rates": null, + "flat": { + "actionable_steps": null, + "observable_steps": null, + "false_positive_rates": null, + "false_negative_rates": null + } + } + ] + }, + "traininglang_scenario_with_model.yml": { + "lang_file": "tests/testdata/langs/org.mal-lang.trainingLang-1.0.0.mar", + "model_file": null, + "num_nodes": 15, + "sim_settings": { + "ttc_mode": "DISABLED", + "run_defense_step_bernoullis": true, + "run_attack_step_bernoullis": true, + "skip_compromised": true, + "skip_unnecessary": false, + "compromise_entrypoints_at_start": true, + "seed": null, + "uncompromise_untraversable_steps": false + }, + "agents": [ + { + "name": "Attacker1", + "type": "attacker", + "policy": "BreadthFirstAttacker", + "reward_mode": "EXPECTED_TTC", + "entry_points": [ + "Host:0:connect", + "User:3:phishing" + ], + "goals": [], + "rewards": { + "Data:2:modify": 10, + "Data:2:notPresent": 8, + "Data:2:read": 5, + "Host:0:access": 4, + "Host:0:notPresent": 2, + "Host:1:access": 5, + "Host:1:notPresent": 7 + }, + "actionable_steps": null, + "ttc_dists": null, + "flat": { + "actionable_steps": null, + "ttc_dists": null + } + }, + { + "name": "Defender1", + "type": "defender", + "policy": null, + "reward_mode": "CUMULATIVE", + "rewards": { + "Data:2:modify": 10, + "Data:2:notPresent": 8, + "Data:2:read": 5, + "Host:0:access": 4, + "Host:0:notPresent": 2, + "Host:1:access": 5, + "Host:1:notPresent": 7 + }, + "actionable_steps": null, + "observable_steps": null, + "false_positive_rates": null, + "false_negative_rates": null, + "flat": { + "actionable_steps": null, + "observable_steps": null, + "false_positive_rates": null, + "false_negative_rates": null + } + } + ] + }, + "ttc_lang_scenario.yml": { + "lang_file": "tests/testdata/langs/ttc_lang.mal", + "model_file": null, + "num_nodes": 55, + "sim_settings": { + "ttc_mode": "DISABLED", + "run_defense_step_bernoullis": true, + "run_attack_step_bernoullis": true, + "skip_compromised": true, + "skip_unnecessary": false, + "compromise_entrypoints_at_start": true, + "seed": null, + "uncompromise_untraversable_steps": false + }, + "agents": [ + { + "name": "Attacker", + "type": "attacker", + "policy": "ShortestPathAttacker", + "reward_mode": "EXPECTED_TTC", + "entry_points": [ + "ComputerA:easyAccess" + ], + "goals": [ + "DataD:read" + ], + "rewards": null, + "actionable_steps": null, + "ttc_dists": null, + "flat": { + "actionable_steps": null, + "ttc_dists": null + } + } + ] + }, + "ttc_lang_scenario_override_ttcs.yml": { + "lang_file": "tests/testdata/langs/ttc_lang.mal", + "model_file": null, + "num_nodes": 55, + "sim_settings": { + "ttc_mode": "DISABLED", + "run_defense_step_bernoullis": true, + "run_attack_step_bernoullis": true, + "skip_compromised": true, + "skip_unnecessary": false, + "compromise_entrypoints_at_start": true, + "seed": null, + "uncompromise_untraversable_steps": false + }, + "agents": [ + { + "name": "BadAttacker", + "type": "attacker", + "policy": "ShortestPathAttacker", + "reward_mode": "EXPECTED_TTC", + "entry_points": [ + "ComputerA:easyAccess" + ], + "goals": [ + "DataD:read" + ], + "rewards": null, + "actionable_steps": null, + "ttc_dists": { + "ComputerA:easyConnect": { + "lhs": { + "name": "Exponential", + "arguments": [ + 0.1 + ], + "type": "function" + }, + "rhs": { + "name": "Bernoulli", + "arguments": [ + 0.5 + ], + "type": "function" + }, + "type": "multiplication" + }, + "ComputerB:easyConnect": { + "lhs": { + "name": "Exponential", + "arguments": [ + 0.1 + ], + "type": "function" + }, + "rhs": { + "name": "Bernoulli", + "arguments": [ + 0.5 + ], + "type": "function" + }, + "type": "multiplication" + }, + "ComputerC:easyConnect": { + "lhs": { + "name": "Exponential", + "arguments": [ + 0.1 + ], + "type": "function" + }, + "rhs": { + "name": "Bernoulli", + "arguments": [ + 0.5 + ], + "type": "function" + }, + "type": "multiplication" + }, + "ComputerD:easyConnect": { + "lhs": { + "name": "Exponential", + "arguments": [ + 0.1 + ], + "type": "function" + }, + "rhs": { + "name": "Bernoulli", + "arguments": [ + 0.5 + ], + "type": "function" + }, + "type": "multiplication" + } + }, + "flat": { + "actionable_steps": null, + "ttc_dists": { + "ComputerA:easyConnect": { + "lhs": { + "name": "Exponential", + "arguments": [ + 0.1 + ], + "type": "function" + }, + "rhs": { + "name": "Bernoulli", + "arguments": [ + 0.5 + ], + "type": "function" + }, + "type": "multiplication" + }, + "ComputerB:easyConnect": { + "lhs": { + "name": "Exponential", + "arguments": [ + 0.1 + ], + "type": "function" + }, + "rhs": { + "name": "Bernoulli", + "arguments": [ + 0.5 + ], + "type": "function" + }, + "type": "multiplication" + }, + "ComputerC:easyConnect": { + "lhs": { + "name": "Exponential", + "arguments": [ + 0.1 + ], + "type": "function" + }, + "rhs": { + "name": "Bernoulli", + "arguments": [ + 0.5 + ], + "type": "function" + }, + "type": "multiplication" + }, + "ComputerD:easyConnect": { + "lhs": { + "name": "Exponential", + "arguments": [ + 0.1 + ], + "type": "function" + }, + "rhs": { + "name": "Bernoulli", + "arguments": [ + 0.5 + ], + "type": "function" + }, + "type": "multiplication" + } + } + } + }, + { + "name": "GoodAttacker", + "type": "attacker", + "policy": "ShortestPathAttacker", + "reward_mode": "EXPECTED_TTC", + "entry_points": [ + "ComputerA:easyAccess" + ], + "goals": [ + "DataD:read" + ], + "rewards": null, + "actionable_steps": null, + "ttc_dists": null, + "flat": { + "actionable_steps": null, + "ttc_dists": null + } + }, + { + "name": "Defender", + "type": "defender", + "policy": null, + "reward_mode": "CUMULATIVE", + "rewards": null, + "actionable_steps": null, + "observable_steps": null, + "false_positive_rates": null, + "false_negative_rates": null, + "flat": { + "actionable_steps": null, + "observable_steps": null, + "false_positive_rates": null, + "false_negative_rates": null + } + } + ] + }, + "wiper_scenario.yml": { + "lang_file": "tests/testdata/langs/wiperLang.mal", + "model_file": "tests/testdata/models/wiper_model.yml", + "num_nodes": 5, + "sim_settings": { + "ttc_mode": "DISABLED", + "run_defense_step_bernoullis": true, + "run_attack_step_bernoullis": true, + "skip_compromised": true, + "skip_unnecessary": false, + "compromise_entrypoints_at_start": false, + "seed": null, + "uncompromise_untraversable_steps": false + }, + "agents": [ + { + "name": "WiperController", + "type": "attacker", + "policy": null, + "reward_mode": "EXPECTED_TTC", + "entry_points": [ + "C2Server:accessData", + "InfectedDevice:infect" + ], + "goals": [ + "InfectedData:read" + ], + "rewards": null, + "actionable_steps": null, + "ttc_dists": null, + "flat": { + "actionable_steps": null, + "ttc_dists": null + } + } + ] + }, + "wrong_agent_classes_scenario.yml": { + "error": "LookupError" + } +} diff --git a/tests/testdata/scenarios/bfs_vs_bfs_network_app_data_scenario.yml b/tests/testdata/scenarios/bfs_vs_bfs_network_app_data_scenario.yml index dbd377df..2c9ca434 100644 --- a/tests/testdata/scenarios/bfs_vs_bfs_network_app_data_scenario.yml +++ b/tests/testdata/scenarios/bfs_vs_bfs_network_app_data_scenario.yml @@ -5,6 +5,7 @@ agents: 'attacker1': agent_class: BreadthFirstAttacker type: attacker + reward_mode: CUMULATIVE config: action_ordering: sorted entry_points: diff --git a/tests/testdata/scenarios/dyna_defense_scenario.yml b/tests/testdata/scenarios/dyna_defense_scenario.yml new file mode 100644 index 00000000..7b98e9eb --- /dev/null +++ b/tests/testdata/scenarios/dyna_defense_scenario.yml @@ -0,0 +1,62 @@ +lang_file: ../langs/dyna_defense_lang.mal +model: + assets: + 0: + name: Host0 + type: Host + associated_assets: + guards: + 3: Guard3 + 1: + name: Host1 + type: Host + associated_assets: + guards: + 4: Guard4 + 2: + name: Host2 + type: Host + associated_assets: + guards: + 5: Guard5 + 3: + name: Guard3 + type: Guard + associated_assets: + host: + 0: Host0 + 4: + name: Guard4 + type: Guard + associated_assets: + host: + 1: Host1 + 5: + name: Guard5 + type: Guard + associated_assets: + host: + 2: Host2 + metadata: + MAL-Toolbox Version: 0.3.11 + info: Created by hand for dyna snapshot coverage. + langID: org.mal-lang.dynaDefense + langVersion: 1.0.0 + malVersion: 0.1.0-SNAPSHOT + name: Dyna Defense Model + +sim_settings: + run_defense_step_bernoullis: true + run_attack_step_bernoullis: true + +agents: + Attacker1: + type: attacker + agent_class: null + entry_points: + - 'Host0:access' + - 'Host1:access' + - 'Host2:access' + Defender1: + type: defender + agent_class: null