🤖 CyberAI

OOB-driven, agent-trust-aware AI pentest platform

Built by someone who red-teams AI, not just with it.

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What is CyberAI?

CyberAI is a multi-agent orchestration layer for offensive security. Five specialized agents — Recon, Intel, Exploit, Report, Web3 — run a typed, auditable pipeline that turns a target into actionable attack paths and a validated report.

Two things set it apart from "LLM wrapper over nmap":

• OOB-driven exploitation. Blind vulns (SSRF, XXE, blind injection) are confirmed through out-of-band callbacks captured by phantom-grid, not guessed from response diffs.
• Agent-trust-aware design. Every banner and tool output is treated as untrusted input: sanitized, injection-scanned, and parsed before it ever reaches the LLM context. Adversarial thinking is a design input, not a disclaimer.

Reach beyond the network: the Web3 agent runs Slither static analysis and maps detectors to Immunefi severity tiers for smart-contract audits.

Quick Start

pip install cyberai

# dry-run: full pipeline, no real network calls
cyberai scan example.com --dry-run

# real scan with a local model (air-gapped, no cloud) and scope
cyberai scan app.target.com --provider ollama --scope "*.target.com"

cyberai status          # config and tool availability
cyberai replay <id>     # re-run a saved session

Trust-aware in one sentence: if Nmap reads a malicious SSH banner crafted to hijack the LLM context, the orchestrator neutralizes that vector before the data ever reaches the model.

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Architecture

flowchart LR
    T([target]) --> O[Orchestrator<br/>typed · dry-run · budget]
    O --> R[Recon] --> I[Intel] --> E[Exploit] --> RP[Report] --> V([validated report])
    E <-->|inject ↔ correlate| PG[(phantom-grid<br/>OOB callbacks)]
    O --> W3[Web3 track<br/>Slither · Immunefi]

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Trust boundary — injection-scan + banner sanitizer at every phase edge. Findings reach confidence = 1.0 only when confirmed out-of-band via phantom-grid.

Observability: SQLite audit log · session export/import · cyberai replay Interfaces: CLI · FastAPI dashboard (SSE) · MCP server (Claude Desktop)

Agents

Agent	Input	Output	Key tools
Recon	target	open ports, DNS, WHOIS, subdomains	nmap (flag-whitelisted), async DNS, subdomain enum
Intel	recon kb	ranked CVEs	NVD client, EPSS enrichment, risk prioritizer
Exploit	intel kb	attack paths, OOB findings	nuclei, searchsploit, OOB/SSRF/XXE workflows
Report	session kb	structured Markdown / H1 export	LLM summary + LLM-as-judge validation
Web3	.sol path / address	severity-tiered findings	Slither, Etherscan, Immunefi classifier

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Security design

• Agent trust boundaries — each agent runs with minimal permissions.
• Untrusted input handling — banners sanitized, length-capped, marked UNTRUSTED before LLM context.
• Prompt-injection detection — 33-pattern detector at every phase boundary; hits become MEDIUM findings, visible in the report.
• Scope enforcement — wildcard + !-exclusion matching honors HackerOne / Bugcrowd briefs (cyberai scope import).
• Audit trail — every agent action logged (JSONL or SQLite) with full inputs/outputs; sessions are replayable.

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Quick start

git clone https://github.com/evkir/CyberAI.git
cd CyberAI
pip install -e .

cp config.example.yml config.yml
cp .env.example .env
# Edit .env — add OPENAI_API_KEY or ANTHROPIC_API_KEY (not needed for --dry-run)

# Dry-run: walks all 4 phases, no network, no API key
python -m cyberai scan example.com --dry-run

# Real scan, scope-restricted
python -m cyberai scan target.htb --scope '*.target.htb'

# Replay a saved session deterministically
python -m cyberai replay <session_id>

# Import a bug-bounty scope
python -m cyberai scope import h1 --program acme

# Status / config
python -m cyberai status

Web dashboard

uvicorn cyberai.web.app:app --reload
# http://127.0.0.1:8000  — session list, live SSE progress, report view

MCP server (Claude Desktop / Cursor)

python -m cyberai.mcp.server

Exposes recon/intel tools (nmap_scan, dns_enum, cve_search, epss_score, …) over the Model Context Protocol. See docs/mcp/integration.md.

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Configuration

# config.yml
llm:
  provider: openai        # openai | anthropic
  model: gpt-4o
  max_tokens: 4096
  temperature: 0.2

phantom:
  grid_url: http://127.0.0.1:9090

output_dir: reports/
max_cost_usd: 0.0         # 0 = disabled; set to enforce a budget

Optional feature flags (default off, no-regression): use_native_tools, use_nuclei, use_llm_summary, use_judge.

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Documentation

Doc	What
docs/api/agents.md	Agent API reference
docs/exploit/oob-exploitation-workflow.md	OOB / SSRF walkthrough
docs/web3/web3-audit.md	Smart-contract audit for Immunefi
docs/mcp/integration.md	MCP server setup

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Related tools

Tool	Role
phantom-grid	OOB interaction capture
phantom-intel	CVE intelligence feed
reality-probe	TLS analysis & config auditing

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Requirements

• Python 3.11+
• OpenAI or Anthropic API key (not required for --dry-run)
• Optional: phantom-grid (OOB), nuclei, slither, NVD API key

⚠️ Legal / Authorized Use

CyberAI is an offensive-security tool intended strictly for authorized security testing, research, and education. Use it only against systems you own or for which you hold explicit, written permission (e.g. a signed engagement, an in-scope bug-bounty program, or a lab you control).

• Unauthorized scanning, exploitation, or access of systems is illegal in most jurisdictions and is not condoned by this project.
• You are solely responsible for ensuring your use complies with all applicable laws and with the rules of any target program.
• The software is provided "as is", without warranty of any kind. The authors and contributors accept no liability for misuse or for any damage arising from its use.

By using CyberAI you agree to operate within these bounds.

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License

MIT — see LICENSE

_{Part of the evkir security toolchain.}