aegis-audit 0.1.3

Behavioral security scanner for AI agent skills and MCP tools — scan, certify, and govern.

Aegis Audit 🦞

Behavioral security scanner for AI agent skills, like on OpenClaw, and MCP tools.

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

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

---

Why Aegis?

AI agents install and run skills with broad system access. Today, you're trusting them blindly. Aegis gives you:

• 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).

Feature	Basic Safety Summary	Aegis Audit
Detection method	LLM reads README	AST + Semgrep + 15 scanners
Deterministic	No	Yes
Capabilities	High-level categories	Scope-resolved (files/URLs/ports)
Vulnerability detection	None	700+ patterns, CWE-mapped
Secret scanning	None	30+ token patterns
Obfuscation detection	None	Base64-exec, homoglyphs, stego
Tamper detection	None	Ed25519-signed Merkle tree
Fix suggestions	None	Actionable remediation per finding

---

Quick Start

1. Install

pip install aegis-audit

2. Scan a skill

# Scan the current directory (deterministic, no API key needed)
aegis scan --no-llm

# Scan a specific path
aegis scan ./some-skill --no-llm

Tip: All commands default to . (the current directory) when no path is given. Most users cd into a skill and run aegis scan from there.

3. (Optional) Add LLM analysis

# Interactive setup — choose provider, model, paste API key
aegis setup

# Then scan with LLM enabled (it's on by default when configured)
aegis scan

aegis setup saves your config to ~/.aegis/config.yaml. You can also set an environment variable instead — env vars always take priority over the config file:

export GEMINI_API_KEY=your-key        # or OPENAI_API_KEY, ANTHROPIC_API_KEY
aegis scan

4. Generate a signed lockfile

aegis lock

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

5. Verify a lockfile

aegis verify

Checks that the current code matches the signed aegis.lock. If any file was modified, the Merkle root won't match and verification fails.

---

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

All commands that take [path] default to . (current directory). Common flags: --no-llm (skip LLM), --json (CI output), -v (verbose). Run aegis scan --help (or aegis lock --help, etc.) for full flags.

---

LLM Setup

Aegis works fully offline with deterministic analysis. LLM analysis is optional — it adds an AI second opinion on intent and risk but is never required.

Option A: Interactive setup (recommended)

aegis setup

This walks you through:

1. Choose a provider — Gemini, Claude, OpenAI, or a local server (Ollama, LM Studio, llama.cpp, vLLM)
2. Pick a model — curated list per provider, or enter a custom model ID
3. Paste your API key — hidden input, tested before saving

Config is saved to ~/.aegis/config.yaml. Run aegis setup again anytime to change it.

Option B: Environment variables

Set one of these and Aegis picks it up automatically:

Variable	Provider
GEMINI_API_KEY	Google Gemini
OPENAI_API_KEY	OpenAI
ANTHROPIC_API_KEY	Anthropic Claude

For local servers:

Variable	Description
OLLAMA_HOST	Ollama server URL (default: http://localhost:11434)
AEGIS_LOCAL_OPENAI_URL	Any OpenAI-compatible server URL
AEGIS_LLM_PROVIDER	Force a specific provider: openai, gemini, claude, ollama, local_openai

---

We've established personas for code repositories that run with our deterministic checks, no LLM is required. Get to know our code personas:

Vibe Check Personas

Aegis assigns each scanned skill a persona based on deterministic analysis. The Vibe Check shows one of these:

🔥 Cracked Dev
10x engineer energy. Clean code, smart patterns, minimal permissions. The kind of skill you'd want to maintain.

✅ LGTM
Looks good to me. Permissions match the intent, scopes are sane, nothing weird. Ship it.

🍌 Trust Me Bro
Polished on the outside, suspicious on the inside. Docs vs code mismatch or unusual permissions. Trust, but verify.

🤔 You Sure About That?
The intern special. Messy code, missing pieces, docs that overpromise. No malicious intent, but it needs a real review.

💕 Co-Dependent Lover
Tiny logic, huge dependency tree. Loves node_modules. Supply chain risk is real here.

👺 Permission Goblin
Wants everything: filesystem, network, secrets, the kitchen sink. Over-scoped and worth a closer look.

🍝 Spaghetti Monster
Unreadable chaos. High complexity, hard to follow. Good luck auditing this.

🐍 The Snake
Warning: This code might look clean, but it isn't. Do not use this skill, it is malicious by design.

---

Example Output

This is actual Aegis output from scanning a skill, this is with the llm set-up and the --verbose details. This is the actual OpenClaw skill that I used for this test: https://clawhub.ai/alirezarezvani/senior-data-scientist

╭─ Aegis Security Audit ──────────────────────────────────────╮
│ AEGIS SECURITY AUDIT                                        │
│   Target: C:\Users\TEST                                     │
│   Files:  8 (3 Python, 1 config, 4 other)                   │
│   Source: directory                                         │
│   Mode:   AST + LLM (gemini)                                │
╰─────────────────────────────────────────────────────────────╯
╭─ Vibe Check ────────────────────────────────────────────────╮
│   🤔  You Sure About That?                                  │
│   The intern special. Messy code, missing pieces,           │
│   docs that overpromise. No malicious intent, but it        │
│   needs a real review.                                      │
│                                                             │
│   ####----------------  22/100 - LOW - minor observations   │
|   only                                                      │
│                                                             │
│   Aegis scored this skill 22/100. The code requests         │
│   minimal permissions and nothing looks unusual. The        │
│   documentation makes claims that don't align with what     │
│   Aegis found in the actual code. This mismatch is the      │
│   most important thing to investigate. Messy code: 1        │
│   missing file ref(s); docs claim production-grade but      │
│   code is minimal. No malicious intent detected, but this   │
│   needs a code review.                                      │
╰─────────────────────────────────────────────────────────────╯

╭─ Trust Analysis ────────────────────────────────────────────╮
│   Aegis cross-referenced SKILL.md against the actual        │
│   code.                                                     │
│                                                             │
│   [ALERT]  The description claims                           │
│   capabilities that don't match what the code provides -    │
│   5 mismatch(es) found.                                     │
│      Claimed cloud: aws, gcp, azure                         │
│      Cloud CLIs in code: none                               │
│      Claimed containers: docker, kubernetes, k8s,           │
│      helm, deployment                                       │
│      Container files in manifest: none                      │
│      ... and 2 more                                         │
│      -> This mismatch suggests the skill either             │
│      won't work as advertised without extra setup that      │
│      isn't included, or the description is overstating      │
│      what the skill actually does. Either way, the          │
│      skill's documentation is not trustworthy               │
│      as-is.                                                 │
│                                                             │
│   [ALERT]  The SKILL.md references                          │
│   13 file(s) or path(s) that don't exist in the package.    │
│      Files referenced but missing: ./charts/,               │
│      config.yaml, data/, k8s/, prod.yaml, project/,         │
│      results/, scripts/, scripts/evaluate.py,               │
│      scripts/health_check.py                                │
│      Files referenced and present:                          │
│      references/experiment_design_frameworks.md,            │
│      references/feature_engineering_patterns.md,            │
│      references/statistical_methods_advanced.md,            │
│      scripts/experiment_designer.py,                        │
│      scripts/feature_engineering_pipeline.py                │
│      Commands referenced: aws, bash, docker, go,            │
│      helm, kubectl, pytest, python                          │
│      -> This means the instructions will cause              │
│      the AI agent to look for files that aren't there.      │
│      The agent may then try to find them elsewhere on       │
│      your system, download them, or create them - all of    │
│      which happen outside the skill's controlled            │
│      scope                                                  │
│                                                             │
│   [WARN]  The skill advertises                              │
│   credential-heavy integrations but declares no required    │
│   credentials.                                              │
│      Integrations needing credentials: aws, gcp,            │
│      azure, postgres, postgresql, database, prometheus,     │
│      monitoring                                             │
│      Code reads secrets: no                                 │
│      Code reads env vars: no                                │
│                                                             │
│   [OK]  Typical configuration -                             │
│   not always-on, not force-installed.                       │
│                                                             │
│   [INFO]  No formal install spec,                           │
│   but the package includes 3 executable script(s).          │
│      Python scripts: 3                                      │
│      Shell scripts: 0                                       │
│                                                             │
│   [INFO]  No tool declarations to                           │
│   verify; code doesn't invoke external binaries.            │
│      No declared or detected binaries                       │
╰─────────────────────────────────────────────────────────────╯
╭──────────────────────── AI Analysis ────────────────────────╮
│   I'm looking at the rap sheet here—three counts of         │
│   `system:sysinfo` with unresolved scopes—but the actual    │
│   code snippets seem to be missing from the dossier! That   │
│   puts me in a bit of a bind for a full forensic            │
│   analysis. However, looking purely at the metadata:        │
│   triggering `system:sysinfo` with an `UNRESOLVED` scope    │
│   usually means the code is accessing system details        │
│   (like `os.uname()`, `platform.system()`, or               │
│   `sys.platform`) via dynamic methods (like                 │
│   `getattr(platform, var)`) rather than direct calls.       │
│                                                             │
│   While system fingerprinting is often step one for         │
│   malware (to tailor the payload), it's also common in      │
│   legitimate cross-platform tools. Without seeing the       │
│   code, I can't confirm if this is clever engineering or    │
│   an evasion attempt, but purely accessing system info is   │
│   generally low-risk compared to file or network access.    │
╰─────────────────────────────────────────────────────────────╯
╭─ Findings ──────────────────────────────────────────────────╮
│   [OK]  Permissions: minimal. No                            │
│   high-risk API usage detected.                             │
╰─────────────────────────────────────────────────────────────╯

╭─ Capabilities ──────────────────────────────────────────────╮
│   Permissions: minimal. No high-risk APIs (network,         │
│   subprocess, credentials) detected. See                    │
│   aegis_report.json.                                        │
╰─────────────────────────────────────────────────────────────╯

╭─ Before You Install ────────────────────────────────────────╮
│   1.  Pin to a specific version: install                    │
│   from a tagged release or commit hash, not 'latest'.       │
│   2.  Check the developer's reputation: look                │
│   at their profile, other published skills, and community   │
│   activity.                                                 │
│   3.  Read the SKILL.md: confirm the skill                  │
│   does what you need and the documentation matches the      │
│   code.                                                     │
╰─────────────────────────────────────────────────────────────╯

╭─ Verbose Risk Briefs ───────────────────────────────────────╮
│ Credential & secret access                                  │
│   None detected. No hardcoded secrets, credential-store     │
│   access, or env-var reads found.                           │
│                                                             │
│ Program execution                                           │
│   None detected. No subprocess, shell, or external binary   │
│   invocations found.                                        │
│                                                             │
│ System-level access                                         │
│   None detected. No platform/sysinfo calls or signal        │
│   handlers found.                                           │
│                                                             │
│ Supply chain risk                                           │
│   None detected. No combination of subprocess +             │
│   unrecognized binaries.                                    │
╰─────────────────────────────────────────────────────────────╯

╭─ Combination Risks ─────────────────────────────────────────╮
│   No dangerous capability combinations detected.            │
╰─────────────────────────────────────────────────────────────╯

╭─ External Programs ─────────────────────────────────────────╮
│   No external programs invoked.                             │
╰─────────────────────────────────────────────────────────────╯

╭─ Sensitive Path Violations ─────────────────────────────────╮
│   No sensitive path violations.                             │
╰─────────────────────────────────────────────────────────────╯

╭─ Scan Complete ─────────────────────────────────────────────╮
│   Report:                                                   │
│   C:\Users\TEST\aegis_report.json                           │
│   This was a read-only scan. Run aegis                      │
│   lock to generate a signed lockfile.                       │
╰─────────────────────────────────────────────────────────────╯

Here is an example of the scan with no AI enabled:

╭─ Aegis Security Audit ──────────────────────────────────────╮
│ AEGIS SECURITY AUDIT                                        │
│   Target: C:\Users\TEST                                     │
│   Files:  8 (3 Python, 1 config, 4 other)                   │
│   Source: directory                                         │
│   Mode:   AST-only                                          │
╰─────────────────────────────────────────────────────────────╯
╭─ Vibe Check ────────────────────────────────────────────────╮
│   🤔  You Sure About That?                                  │
│   The intern special. Messy code, missing pieces,           │
│   docs that overpromise. No malicious intent, but it        │
│   needs a real review.                                      │
│                                                             │
│   ####----------------  22/100 - LOW - minor observations   │
│   only                                                      │
│                                                             │
│   Aegis scored this skill 22/100. The code requests         │
│   minimal permissions and nothing looks unusual. The        │
│   documentation makes claims that don't align with what     │
│   Aegis found in the actual code. This mismatch is the      │
│   most important thing to investigate. Messy code: 1        │
│   missing file ref(s); docs claim production-grade but      │
│   code is minimal. No malicious intent detected, but this   │
│   needs a code review.                                      │
╰─────────────────────────────────────────────────────────────╯

╭─ Trust Analysis ────────────────────────────────────────────╮
│   Aegis cross-referenced SKILL.md against the actual        │
│   code.                                                     │
│                                                             │
│   [ALERT]  The description claims                           │
│   capabilities that don't match what the code provides -    │
│   5 mismatch(es) found.                                     │
│      Claimed cloud: aws, gcp, azure                         │
│      Cloud CLIs in code: none                               │
│      Claimed containers: docker, kubernetes, k8s,           │
│      helm, deployment                                       │
│      Container files in manifest: none                      │
│      ... and 2 more                                         │
│      -> This mismatch suggests the skill either             │
│      won't work as advertised without extra setup that      │
│      isn't included, or the description is overstating      │
│      what the skill actually does. Either way, the          │
│      skill's documentation is not trustworthy               │
│      as-is.                                                 │
│                                                             │
│   [ALERT]  The SKILL.md references                          │
│   13 file(s) or path(s) that don't exist in the package.    │
│      Files referenced but missing: ./charts/,               │
│      config.yaml, data/, k8s/, prod.yaml, project/,         │
│      results/, scripts/, scripts/evaluate.py,               │
│      scripts/health_check.py                                │
│      Files referenced and present:                          │
│      references/experiment_design_frameworks.md,            │
│      references/feature_engineering_patterns.md,            │
│      references/statistical_methods_advanced.md,            │
│      scripts/experiment_designer.py,                        │
│      scripts/feature_engineering_pipeline.py                │
│      Commands referenced: aws, bash, docker, go,            │
│      helm, kubectl, pytest, python                          │
│      -> This means the instructions will cause              │
│      the AI agent to look for files that aren't there.      │
│      The agent may then try to find them elsewhere on       │
│      your system, download them, or create them - all of    │
│      which happen outside the skill's controlled            │
│      scope                                                  │
│                                                             │
│   [WARN]  The skill advertises                              │
│   credential-heavy integrations but declares no required    │
│   credentials.                                              │
│      Integrations needing credentials: aws, gcp,            │
│      azure, postgres, postgresql, database, prometheus,     │
│      monitoring                                             │
│      Code reads secrets: no                                 │
│      Code reads env vars: no                                │
│                                                             │
│   [OK]  Typical configuration -                             │
│   not always-on, not force-installed.                       │
│                                                             │
│   [INFO]  No formal install spec,                           │
│   but the package includes 3 executable script(s).          │
│      Python scripts: 3                                      │
│      Shell scripts: 0                                       │
│                                                             │
│   [INFO]  No tool declarations to                           │
│   verify; code doesn't invoke external binaries.            │
│      No declared or detected binaries                       │
╰─────────────────────────────────────────────────────────────╯
╭─ Findings ──────────────────────────────────────────────────╮
│   [OK]  Permissions: minimal. No                            │
│   high-risk API usage detected.                             │
╰─────────────────────────────────────────────────────────────╯

╭─ Capabilities ──────────────────────────────────────────────╮
│   Permissions: minimal. No high-risk APIs (network,         │
│   subprocess, credentials) detected. See                    │
│   aegis_report.json.                                        │
╰─────────────────────────────────────────────────────────────╯

╭─ Before You Install ────────────────────────────────────────╮
│   1.  Pin to a specific version: install                    │
│   from a tagged release or commit hash, not 'latest'.       │
│   2.  Check the developer's reputation: look                │
│   at their profile, other published skills, and community   │
│   activity.                                                 │
│   3.  Read the SKILL.md: confirm the skill                  │
│   does what you need and the documentation matches the      │
│   code.                                                     │
╰─────────────────────────────────────────────────────────────╯

╭─ Scan Complete ─────────────────────────────────────────────╮
│   Report:                                                   │
│   C:\Users\mhube\aegis_report.json                          │
│   This was a read-only scan. Run aegis                      │
│   lock to generate a signed lockfile.                       │
╰─────────────────────────────────────────────────────────────╯

---

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.)

---

Use as an 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 to Cursor

Add this to your .cursor/mcp.json:

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

Or generate it automatically:

aegis mcp-config

Add to Claude Desktop

Add the same block to your Claude Desktop MCP config. Aegis uses stdio transport — no network server needed.

---

Use as a Cursor Skill (ClawHub)

Aegis is available as a skill on ClawHub. Install it and your agent will automatically audit skills before enabling them.

See SKILL.md for the full skill specification.

---

JSON Output for CI

# Full JSON report to stdout
aegis scan --json --no-llm

# Pipe into jq to extract the risk score
aegis scan --json --no-llm | jq '.deterministic.risk_score_static'

# Fail CI if risk > 50
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)

---

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→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
         │
         ▼
    aegis_report.json + aegis.lock

---

For Skill Developers

Building a skill? See the Skill Developer Best Practices guide for how to make your skills auditable, trustworthy, and easy to verify.

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

---

Project Structure

aegis-audit/
├── aegis-core/          # Python package (pip install aegis-audit)
│   ├── aegis/           # Source code
│   │   ├── cli.py       # CLI entry point
│   │   ├── mcp_server.py # MCP server
│   │   ├── scanner/     # All 15+ analyzers
│   │   ├── crypto/      # Hasher + signer
│   │   ├── models/      # Pydantic models
│   │   ├── policy/      # Rule engine
│   │   └── reporter/    # Output formatters
│   ├── tests/           # Test suite
│   ├── pyproject.toml   # Package config
│   └── README.md        # Detailed CLI reference
├── docs/                # Governance & operational docs
│   ├── CHANGELOG.md
│   ├── SKILL_DEVELOPER_GUIDE.md
│   ├── INCIDENT_RESPONSE.md
│   ├── BCP_DR.md
│   ├── RISK_REGISTER.md
│   └── VENDOR_RISK.md
├── scripts/             # Batch scanning utilities
├── .github/             # CI + issue templates
├── SKILL.md             # ClawHub skill specification
├── LICENSE              # AGPL-3.0
└── LICENSING.md         # Dual license details

---

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. For enterprise inquiries: [e-mail].

---

Contributing

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

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

---

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