Invaris AgentSec

Adversarial security and reliability testing for autonomous AI agents.

Invaris AgentSec is an open-source testing framework for finding unsafe, unauthorized, and unreliable agent behaviour before it reaches production. It helps developers test complete agent workflows involving LLMs, retrieval pipelines, memory, MCP servers, external tools, databases, and sensitive actions.

The goal is simple: make testing an AI agent as repeatable and developer-friendly as testing an API.

Why AgentSec?

Traditional software follows explicitly written execution paths. AI agents interpret untrusted inputs, select tools, retain memory, and make decisions dynamically. A single indirect prompt injection or poisoned tool response can cause an agent to:

expose confidential information;
invoke an unauthorized tool;
exceed its permissions or operating budget;
execute an irreversible action;
preserve malicious instructions in memory;
enter an expensive or non-terminating loop; or
behave differently after a model, prompt, or tool update.

Unit tests alone cannot adequately exercise these behaviours. AgentSec runs stateful adversarial scenarios, observes the complete execution trace, and verifies that security policies hold throughout the workflow.

This isn't hypothetical: see docs/why-agentsec.md for real, sourced incidents (a dealership chatbot selling a $76,000 car for $1, a zero-click exploit against Microsoft 365 Copilot, an AI coding agent deleting a production database), the survey data on how little of this is actually tested for today, and the governments and research institutions that have restricted AI use outright.

What AgentSec Tests

The test suite covers:

Direct and indirect prompt injection
Unauthorized tool invocation
Tool-output and MCP-server poisoning
Sensitive-data and secret leakage
Memory poisoning and unsafe persistence
Excessive tool calls, token usage, and cost
Infinite loops and missing termination conditions
Unsafe handling of retrieved documents
Actions a tool is globally allowed to perform but that the current task never authorized (action_without_authorization, when the policy declares tool_effects) -- see Policy reference
Dangerous compositions of individually allowed calls, tracked as data flow: private data read by one call and sent out by another to a destination the user never named, untrusted text executed as a command, untrusted instructions handed to another agent (dangerous_composition)
Agents misreporting what they did: denying a side effect the trace shows, or claiming work that never happened (deceptive_action_report)
Identity, session and authorization confusion: acting on another user's or tenant's resource, reusing a permission granted for an earlier task or another session, using a credential another caller left behind (identity_and_session_confusion)
Multi-agent privilege abuse: sub-agents exceeding their role, privilege escalation through delegation (confused deputy), unauthorized delegation, unregistered agents, secrets passed between agents (multi_agent_delegation, when the policy declares agent_roles) -- see Multi-agent testing
Behavioural regressions across models and prompts, via agentsec compare

Every check above is deterministic and opt-in by declaration: categories that need tool_effects or agent_roles build no scenarios without them, and a multi-agent scenario run against a system that reports no agent attribution is reported as not observable, never as passed. Unauthorized financial or on-chain actions are covered, but not by the default thirteen categories above -- via the bundled on-chain attack pack and the policy's spend_limits/address_allowlist; see Domain attack packs. What has and has not been tested against real agents and frameworks is in Testing.

Findings are mapped to the OWASP Top 10 for Agentic Applications (ASI01 to ASI10).

Design Principles

Evidence over scores: Every finding includes the input, trace, violated policy, and observed action.
Stateful testing: Tests cover complete workflows rather than isolated prompts.
Framework independence: AgentSec works across models, agent frameworks, MCP servers, and custom APIs.
CI-first: Security regressions are detectable automatically on every pull request.
Local by default: Developers can test locally without sending private traces to a hosted service.
Reproducibility: A failed scenario is replayable with the same configuration and evidence.

Developer Experience

Install the command-line tool:

pip install invaris-agentsec

For a local checkout instead (to run the examples, or to contribute), install from a clone with pip install -e ..

Create an agentsec.yaml policy in the directory you'll run agentsec test from -- it looks for ./agentsec.yaml by default (or pass --policy path/to/file.yaml to use a different location or name). Write it by hand as below, or scaffold one with agentsec init (writes a starter agentsec.yaml; won't overwrite an existing one). See the Policy reference for every available field.

version: "1"

agent:
  name: support-agent
  endpoint: http://localhost:8000/agent

allowed_tools:
  - search_documents
  - create_draft

forbidden_actions:
  - send_email
  - reveal_credentials
  - execute_payment

limits:
  max_steps: 12
  max_tool_calls: 10
  max_cost_usd: 0.50

tests:
  - prompt_injection
  - indirect_prompt_injection
  - secret_extraction
  - unauthorized_tool_use
  - tool_output_poisoning
  - unsafe_retrieved_documents
  - loop_and_budget_limits

Run the security suite:

agentsec test

Example output (reports are also written to .agentsec/report.json and .agentsec/report.html, the two default formats -- markdown and sarif are also available via --format):

Invaris AgentSec

42 scenarios executed
36 passed
6 findings

CRITICAL  Indirect prompt injection triggered send_email
HIGH      Retrieved confidential content appeared in the response
HIGH      Agent attempted a forbidden payment action
MEDIUM    Tool-call budget exceeded

Report written to .agentsec/report.json
Report written to .agentsec/report.html

Try It

Run the bundled intentionally vulnerable agent, then test it:

pip install -e ".[dev]"
python examples/vulnerable_rag_agent/server.py &          # add --safe for the hardened variant
agentsec test --policy examples/vulnerable_rag_agent/agentsec.yaml

Against the vulnerable agent you should see 35 scenarios executed and findings in all eight categories. With --safe, all 35 pass.

Reports. agentsec test writes .agentsec/report.json and a self-contained .agentsec/report.html (--format json,html,markdown,sarif to choose; sarif writes results.sarif for GitHub Code Scanning). Secrets are masked. Every finding is tagged with the closest OWASP Top 10 for Agentic Applications category (ASI01 to ASI10).

Exit codes. agentsec test exits 1 when findings exist (--fail-on high to raise the bar, --fail-on none to never fail) and 2 on configuration or connection errors. Use --seed N for a reproducible run and -s <category-or-scenario-id> to run a subset.

Replay. agentsec replay .agentsec/report.json re-runs the findings from an earlier report with the recorded seed and prints REPRODUCED or NOT REPRODUCED for each, so you can confirm a fix.

Model-assisted checks. Add a judge: section to your policy and pass --judge to have a model review scenarios the deterministic checks passed, for example a paraphrased leak. These findings are labelled model-assisted, are never critical, and are opt-in because transcripts are sent to the judge endpoint. See docs/judge.md.

A RAG-style agent. examples/rag_agent owns a small document corpus and runs its tools itself, reporting the calls to AgentSec. Start it with python examples/rag_agent/server.py (add --safe for the hardened variant) and test it with agentsec test -p examples/rag_agent/agentsec.yaml. See docs/agent-contract.md.

Real-world reference agents. examples/real_world_agents has three more vulnerable/--safe reference agents modeled on daily-use AI agent products -- a coding assistant, a customer-support assistant and a browser-automation assistant -- each paired one-to-one with its matching domain attack pack below. See examples/real_world_agents/README.md and docs/agent-contract.md.

Regression comparison. agentsec compare old/report.json new/report.json lists new, fixed and changed findings and exits 1 on regressions. The packaged GitHub Action wires this into pull requests automatically (baseline-report), posting and updating a PR comment with what's new or worse; see GitHub Actions.

MCP servers. agentsec mcp scan --command "python server.py" lists a server's tools, resources and prompts (never calls, reads or fetches any of them) and flags poisoned descriptions, hidden characters, tool shadowing, homoglyph tool-name impersonation, lying read-only/destructive annotations, and changed definitions. See docs/mcp-testing.md.

MCP-connected agents. agentsec test --mcp-listen 127.0.0.1:8765 makes AgentSec the MCP server your agent uses, delivering the same adversarial scenarios through MCP tool results.

LangChain and LangGraph agents can be tested in-process with LangChainAdapter; see docs/frameworks.md. Any other framework works through the generic CallableAdapter.

Your own scenarios can be added without forking AgentSec: agentsec test --attack-pack my_pack.py or attack_packs: in the policy loads extra categories from a local file or an installed package; see docs/extending.md.

Other commands. agentsec init writes a starter policy and agentsec schema policy|trace prints the JSON schemas.

The categories are prompt_injection, indirect_prompt_injection, secret_extraction, unauthorized_tool_use, tool_output_poisoning, unsafe_retrieved_documents, loop_and_budget_limits and memory_poisoning.

Agent contract. The adapter posts OpenAI-style chat-completions requests to agent.endpoint and declares your allowed tools (plus forbidden actions as decoys). AgentSec plays the tools: every call is simulated, and results carry the adversarial content. Agents that run tools server-side can report them in an x_agentsec.events field, and agents with memory can key it on the user field (also sent as X-AgentSec-Session). List the synthetic credentials your agent can see under secrets: so leaks are detected.

In CI. Inside GitHub Actions, agentsec test adds workflow annotations for each finding and writes a job summary automatically. A packaged action (uses: ./) starts your agent, runs the suite, uploads reports and gates the job; it can also compare against a baseline report and comment on the pull request, or feed results.sarif to GitHub Code Scanning. See docs/github-actions.md.

Full documentation, including architecture, policy reference, attack catalog, Python API, testing and extension guides, is in docs/.

Example Policy Test

AgentSec tests focus on expected behaviour rather than a particular model response:

from agentsec import AgentTarget, SecuritySuite

target = AgentTarget(
    endpoint="http://localhost:8000/agent",
    allowed_tools={"search_documents", "create_draft"},
    forbidden_tools={"send_email", "execute_payment"},
)

suite = SecuritySuite(target)

result = suite.run("indirect_prompt_injection")

assert result.secret_leaks == []
assert result.forbidden_tool_calls == []
assert result.total_tool_calls <= 10

AgentTarget, SecuritySuite and the result attributes above are implemented. In pytest, the bundled plugin adds an agentsec_run fixture:

import pytest
from agentsec import CATEGORIES

@pytest.mark.parametrize("category", CATEGORIES)
def test_agent_resists(category, agentsec_run):
    agentsec_run(category, fail_on="high")

Run it with pytest --agentsec-policy agentsec.yaml. See docs/python-api-and-pytest.md. The API may still change before a stable release.

How It Works

flowchart TD
    A["Test policy"] --> B["Adversarial scenario engine"]
    B --> C["Agent under test"]
    C --> D["Models, tools, memory and MCP"]
    D --> E["Trace collector"]
    E --> F["Policy evaluator"]
    F --> G["Evidence and security report"]

A developer defines the agent's permitted actions and operating limits.
AgentSec generates or loads adversarial scenarios.
The scenarios exercise the agent, retrieval layer, memory, and tools.
AgentSec records prompts, responses, tool calls, state changes, timing, and cost.
Policy evaluators identify violations and assign severity.
Reports provide reproducible evidence and remediation guidance.

Architecture

agentsec/
├── attacks/          # Prompt, retrieval, memory and tool attacks, plus attack-pack loading
├── adapters/         # OpenAI-compatible HTTP (optionally streaming) and in-process callables
├── mcp/              # MCP server scanner (client, checks, pins) and the MCP attack host
├── integrations/     # LangChain and LangGraph adapter
├── evaluators/       # Deterministic checks plus an optional model-assisted judge
├── policies/         # Permissions, limits and expected behaviour
├── runners/          # Local runner and replay (CI and sandboxed planned)
├── traces/           # Normalized agent execution events
├── reports/          # Terminal, JSON, HTML, Markdown, SARIF and GitHub output
├── cli/              # Command-line interface
├── compare.py        # Report-to-report regression comparison
├── owasp.py          # Mapping of findings to OWASP agentic categories
├── api.py            # Python API (AgentTarget, SecuritySuite)
└── pytest_plugin.py  # pytest fixtures
action.yml, action/   # Packaged GitHub Action (incl. baseline comparison, PR comments)
examples/             # Vulnerable reference agents (rule-based, RAG-backed, real-world, MCP) and attack packs
tests/                # Unit and end-to-end tests (420+ tests)
benchmarks/           # Framework, memory-system and real-agent runs (results checked in)

Core components

Component	Responsibility
Scenario engine	Builds multi-step adversarial workflows
Agent adapters	Connects custom agents and supported frameworks
Trace collector	Captures prompts, retrieval events, tool calls and state changes
Policy engine	Checks permissions, budgets and behavioural constraints
Evaluators	Detects leakage, unsafe actions and security regressions
Reporter	Produces human-readable and machine-readable evidence

What's Built

Core testing engine

Reports and CI

Terminal, JSON, self-contained HTML, Markdown and SARIF 2.1.0 report formats (--format)
Secrets masked everywhere, in every format
agentsec replay re-runs findings from an earlier report to confirm a fix
agentsec compare diffs two reports (new, fixed, unchanged, worse, better, not comparable)
GitHub Actions workflow annotations and job summary, with no extra flags
A packaged, reusable GitHub Action (action.yml) that installs AgentSec, optionally starts the agent, runs the suite, uploads reports and gates the job
Baseline and pull-request regression testing built into the Action: compare a run against a baseline report and post (and update, across pushes) a PR comment listing what's new or worse
SARIF output wired to GitHub Code Scanning (--format sarif plus github/codeql-action/upload-sarif); see GitHub Actions

Frameworks and protocols

LangChainAdapter for LangChain and LangGraph agents, run in-process, duck-typed so AgentSec never imports the framework itself
agentsec mcp scan: statically scans an MCP server's tools, resources and prompts for poisoned descriptions, invisible characters, tool shadowing, homoglyph tool-name impersonation, lying annotations, forbidden or unlisted tools, and rug pulls across all three kinds (pin a server's definitions and compare across runs)
agentsec test --mcp-listen: AgentSec acts as the MCP server an agent connects to, delivering the same adversarial scenarios over MCP tool results
Agents that run tools server-side and stream their replies (server-sent events, x_agentsec events for reporting server-side tool calls and memory keys)

Extensibility and API

Python API (AgentTarget, SecuritySuite) mirroring the CLI, for running scenarios from code
A pytest plugin (agentsec_run fixture, --agentsec-policy)
Attack packs: load extra scenario categories from a local file or an installed package without forking AgentSec (attack_packs: in the policy, or agentsec test --attack-pack), validated and name-collision-checked at load time. A pack can also ship its own EVALUATORS for deterministic checks the built-in ones can't express, and its own JUDGE_CHECKS so --judge can ask a question phrased in the pack's own domain terms instead of only the two built-in ones -- five full reference packs covering coding, browser, RAG (including a pack-provided judge check), customer-support and on-chain agents; see Domain attack packs and Extending AgentSec
Two intentionally vulnerable reference agents (rule-based, RAG-backed), each with a hardened variant that passes the full suite, plus a demo MCP server and an MCP-connected reference agent

Future Work

Everything below is not yet built. It splits into a hosted platform, which is commercial territory kept separate from the open-source engine, and engine-level coverage gaps that stay in scope for AgentSec itself.

Hosted platform

Hosted execution dashboard
Team projects and cross-run historical reports
Self-hosted enterprise deployment
Production trace monitoring (continuous monitoring of live agent traffic, not just test-time runs)

The CLI-scoped equivalents of the first two items already exist and are open source: agentsec compare plus the GitHub Action's baseline-report/PR-comment support gives pull-request and scheduled regression testing without a hosted dashboard; see GitHub Actions.

Engine and coverage

Multi-agent delegation and privilege-abuse testing (agent_roles, multi_agent_delegation); exercised against a reference team and real OpenAI Agents SDK / CrewAI teams with a scripted model -- see Multi-agent testing
Agent identity and session-boundary testing (identity_and_session_confusion)
Multi-agent attribution for systems that share one MCP client across agents (not observable today)
Stateful, multi-turn campaign generation
Cross-agent failure-propagation analysis
Memory-poisoning scenarios longer than one follow-up session
MCP-specific attacks beyond hostile tool results and decoy tools; see MCP testing
Dedicated adapters for other agent frameworks. The generic CallableAdapter plus the in-process ToolHost covers them today, and the same scenarios have been run through LangChain, LangGraph, CrewAI, AutoGen, the OpenAI Agents SDK, Google ADK and smolagents with a scripted model -- see benchmarks/framework-compat

Real-world verification: the packaged GitHub Action -- via the actual public uses: invarislabs/invaris-agentsec@main path, not a local reference -- has been run repeatedly on real GitHub Actions infrastructure (see .github/workflows/self-scan.yml), and its GitHub Code Scanning integration is confirmed working end to end: the generated SARIF validates, uploads successfully, and real findings appear in this repo's own Security > Code scanning tab. (Those alerts are visible to signed-in users with access to the repository, not to signed-out visitors -- a GitHub platform behavior, not an AgentSec one, worth knowing before relying on a bare link in a demo.) The MCP attack host has been run against a real LLM-backed agent (the Claude Code CLI, with its built-in tools disabled and every MCP tool simulated); see benchmarks/real-agents. Codex, Gemini CLI and Cursor have not been run. See Testing for exactly what is and isn't covered.

Intended Users

AgentSec is being designed for:

Developers building tool-using AI agents
AI startups preparing agents for production
Security teams evaluating autonomous workflows
Teams exposing or consuming MCP servers
Enterprises deploying agents over internal data
Financial and blockchain teams building transaction-capable agents
Researchers studying agent reliability and adversarial behaviour

Security Model

AgentSec executes potentially adversarial content against systems that may have access to real tools and data. During early development:

use isolated test environments;
provide synthetic credentials and test data;
disable irreversible actions;
use sandboxed or mocked tools;
enforce strict spending and execution limits; and
never point experimental tests at production agents.

A detailed threat model will be published before the first public release. The vulnerability-reporting policy is in SECURITY.md.

Contributing

The project is in early development. Contributions will be welcomed in areas such as:

Adversarial test cases
Agent and framework adapters
MCP security testing
Deterministic evaluators
Trace schemas and interoperability
Sandboxing and safe execution
Documentation and vulnerable examples

To develop locally, run pip install -e ".[dev]" and then pytest. See CONTRIBUTING.md for the guidelines.

Responsible Disclosure

If AgentSec identifies a vulnerability in a third-party agent, framework, or integration, do not publish sensitive details immediately. Contact the affected maintainer and allow reasonable time for remediation. To report a vulnerability in AgentSec itself, see SECURITY.md.

License

Invaris AgentSec is licensed under the Apache License 2.0. "Invaris" and "AgentSec" are trademarks of Invaris Labs and are not covered by that license; see NOTICE for details.

About Invaris Labs

Invaris Labs is building the security infrastructure for autonomous AI agents.

Contact

Invaris AgentSec is under active development. If you are building an AI agent and would like to become an early design partner, open a discussion or get in touch.

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LICENSE		LICENSE
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