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name: aegis-audit description: > Deep behavioral security audit for AI agent skills and MCP tools. Performs deterministic static analysis (AST + Semgrep + 15 specialized scanners), cryptographic lockfile generation, and optional LLM-powered intent analysis. Use when installing, reviewing, or approving any skill, tool, plugin, or MCP server — especially before first use. Replaces basic safety summaries with full CWE-mapped, OWASP-tagged, line-referenced security reports. version: 0.1.10 homepage: https://github.com/Aegis-Scan/aegis-scan url: https://pypi.org/project/aegis-audit/ metadata: {"openclaw":{"emoji":"🔍","homepage":"https://github.com/Aegis-Scan/aegis-scan","requires":{"bins":["aegis"],"config":["~/.aegis/config.yaml"]},"install":[{"kind":"uv","package":"aegis-audit","bins":["aegis"]}]}}

Aegis Audit

Behavioral security scanner for AI agent skills and MCP tools.

Aegis is a defensive security auditing tool. It detects malicious patterns in other skills so users can avoid dangerous installs. This skill does not teach or enable attacks — it helps users vet skills before trusting them.

The "SSL certificate" for AI agent skills — scan, certify, and govern before you trust.

Source: github.com/Aegis-Scan/aegis-scan | Package: pypi.org/project/aegis-audit | License: AGPL-3.0

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What Aegis does

Aegis answers the question every agent user should ask: "What can this skill actually do, and should I trust it?"

• Deterministic static analysis — AST parsing + Semgrep + 15 specialized scanners. Same code = same report, every time.
• Scope-resolved capabilities — Not just "accesses the filesystem" but exactly which files, URLs, hosts, and ports.
• Risk scoring — 0-100 composite score with CWE/OWASP-mapped findings and severity tiers.
• Cryptographic proof — Ed25519-signed lockfile with Merkle tree for tamper detection.
• Optional LLM analysis — Bring your own key (Gemini, Claude, OpenAI, Ollama, local). Disabled by default. See the privacy notice below before enabling.

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Install

Install from PyPI using pip or uv:

pip install aegis-audit

uv tool install aegis-audit

Both commands install the same package. Pin to a specific version when possible (e.g. pip install aegis-audit==1.3.0) and verify the publisher on PyPI before installing. The package source is at github.com/Aegis-Scan/aegis-scan.

After install, the aegis CLI is available on your PATH.

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Quick start

Aegis runs fully offline by default. No API keys, no network access, no data leaves your machine.

aegis scan --no-llm

This scans the current directory and produces a security report. All commands default to . (current directory) when no path is given.

aegis scan ./some-skill --no-llm

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CLI reference

Command	Description
aegis scan [path]	Full security scan with risk scoring
aegis lock [path]	Scan + generate signed aegis.lock
aegis verify [path]	Verify lockfile against current code
aegis badge [path]	Generate shields.io badge markdown
aegis setup	Interactive LLM configuration wizard
aegis mcp-serve	Start the MCP server (stdio transport)
aegis mcp-config	Print MCP config JSON for Cursor / Claude Desktop
aegis version	Show the Aegis version

Common flags: --no-llm (skip LLM, the default), --json (CI output), -v (verbose).

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Lockfiles

Generate a signed lockfile after scanning:

aegis lock

This produces aegis.lock — a cryptographically signed snapshot of the skill's security state. Commit it alongside the skill so consumers can verify nothing changed.

Verify a lockfile:

aegis verify

If any file was modified since the lockfile was created, the Merkle root will not match and verification fails.

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Optional: LLM analysis

Privacy notice: LLM analysis is disabled by default. When enabled, Aegis sends scanned code to the configured third-party LLM provider (Google, OpenAI, or Anthropic). No data is transmitted unless you explicitly configure an API key and run a scan without --no-llm. Do not enable LLM mode on repositories containing secrets or sensitive code unless you trust the provider.

To enable LLM analysis, run the interactive setup:

aegis setup

This saves your config to ~/.aegis/config.yaml. Alternatively, set one of these environment variables:

• GEMINI_API_KEY — Google Gemini
• OPENAI_API_KEY — OpenAI
• ANTHROPIC_API_KEY — Anthropic Claude

These environment variables are optional. Aegis works fully offline without them. Only set a key if you want the AI second-opinion feature and accept that scanned code will be sent to the corresponding provider.

For local LLM servers (Ollama, LM Studio, llama.cpp, vLLM), see aegis setup — no third-party data transmission occurs with local models.

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MCP server

Aegis runs as an MCP server for Cursor, Claude Desktop, and any MCP-compatible client. Three tools are exposed: scan_skill, verify_lockfile, and list_capabilities.

Add this to your .cursor/mcp.json:

{
  "mcpServers": {
    "aegis": {
      "command": "aegis",
      "args": ["mcp-serve"]
    }
  }
}

Or generate it automatically:

aegis mcp-config

Aegis uses stdio transport — no network server needed.

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What gets scanned

Scanner	What it detects
AST Parser	750+ Python function/method patterns across 15+ categories
Semgrep Rules	80+ regex rules for Python, JavaScript, and secrets
Secret Scanner	API keys, tokens, private keys, connection strings (30+ patterns)
Shell Analyzer	Pipe-to-shell, reverse shells, inline exec
JS Analyzer	XSS, eval, prototype pollution, dynamic imports
Dockerfile Analyzer	Privilege escalation, secrets in ENV/ARG, unpinned images
Config Analyzer	Dangerous settings in YAML, JSON, TOML, INI
Social Engineering	Misleading filenames, Unicode tricks, trust manipulation
Steganography	Hidden payloads in images, homoglyph attacks
Shadow Module Detector	Stdlib-shadowing files (os.py, sys.py in the skill)
Combo Analyzer	Multi-capability attack chains (exfiltration, C2, ransomware)
Taint Analysis	Source-to-sink data flows (commands, URLs, SQL, paths)
Complexity Analyzer	Cyclomatic complexity warnings for hard-to-audit functions
Skill Meta Analyzer	SKILL.md vs actual code cross-referencing
Persona Classifier	Overall trust profile (LGTM, Permission Goblin, etc.)

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Vibe Check personas

Aegis assigns each scanned skill a persona based on deterministic analysis:

• Cracked Dev — Clean code, smart patterns, minimal permissions.
• LGTM — Permissions match the intent, scopes are sane, nothing weird.
• Trust Me Bro — Polished on the outside, suspicious on the inside.
• You Sure About That? — Messy code, missing pieces, docs that overpromise.
• Co-Dependent Lover — Tiny logic, huge dependency tree. Supply chain risk.
• Permission Goblin — Wants everything: filesystem, network, secrets.
• Spaghetti Monster — Unreadable chaos. High complexity.
• The Snake — Code that looks clean but is not. Potentially malicious.

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JSON output for CI

aegis scan --json --no-llm

aegis scan --json --no-llm | jq '.deterministic.risk_score_static'

aegis scan --json --no-llm | jq -e '.deterministic.risk_score_static <= 50'

The JSON report contains two payloads:

• Deterministic — Merkle tree, capabilities, findings, risk score (reproducible, signed)
• Ephemeral — LLM analysis, risk adjustment (non-deterministic, not signed)

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For skill developers

Run Aegis on your own skill before publishing:

cd ./my-skill
aegis scan --no-llm -v

Fix PROHIBITED findings. Document RESTRICTED ones. Ship with an aegis.lock:

aegis lock

See the Skill Developer Best Practices guide.

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Architecture

aegis scan ./skill
    |
    +-- coordinator.py       File discovery (git-aware / directory walk)
    +-- ast_parser.py        AST analysis + pessimistic scope extraction
    +-- secret_scanner.py    30+ secret patterns
    +-- shell_analyzer.py    Dangerous shell patterns
    +-- js_analyzer.py       JS/TS vulnerability patterns
    +-- config_analyzer.py   YAML/JSON/TOML/INI risky settings
    +-- combo_analyzer.py    Multi-capability attack chains
    +-- taint_analyzer.py    Source-to-sink data flow tracking
    +-- binary_detector.py   External binary classification
    +-- social_eng_scanner   Social engineering detection
    +-- stego_scanner        Steganography + homoglyphs
    +-- hasher.py            Lazy Merkle tree
    +-- signer.py            Ed25519 signing
    +-- rule_engine.py       Policy evaluation
    +-- reporter/            JSON + Rich console output
         |
         v
    aegis_report.json + aegis.lock

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License

Aegis is dual-licensed:

• Open Source: AGPL-3.0 — free to use, modify, and distribute. Network service deployments must release source.
• Commercial: Proprietary license available for embedding in proprietary products, running without source disclosure, SLAs, and support.

See LICENSING.md for full details.

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Contributing

Contributions welcome. By contributing, you agree to the Contributor License Agreement.

cd aegis-core
pip install -e ".[dev]"
pytest

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Python 3.11+ required. No network access needed for deterministic scans. Works offline.