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agentsec - AI Agent Security Scanner

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agentsec

Static configuration scanner and hardener for AI agent installations. Detects misconfigurations, credential leaks, supply-chain risks, and MCP tool poisoning — then maps every finding to the OWASP Top 10 for Agentic Applications (2026).

Supports OpenClaw, Claude Code, Cursor, Windsurf, Gemini CLI, and MCP server ecosystems.

Scope: agentsec performs static configuration and file analysis. It does not provide runtime protection, behavioral monitoring, or real-time threat detection. Use as one layer in a defense-in-depth strategy.

Quick Start

pip install agentsec-ai
agentsec scan

Scan a specific installation:

agentsec scan ~/.openclaw

What agentsec checks

Module Scope
installation Gateway exposure, auth posture, DM/group policy, tool/sandbox settings, SSRF and safety checks, known CVE version checks, sensitive file/dir permissions
skill Instruction malware, risky code patterns (eval/exec/subprocess) for Python and JS/TS, identity-file (MEMORY.md/AGENTS.md/SOUL.md) write detection, prompt-injection patterns, frontmatter capability risk, dependency/install-hook risk
mcp Tool poisoning patterns, launch-command injection, known-CVE package detection, auth gaps and non-TLS transport on remote endpoints, dangerous schema/permissions, unverified npx usage
credential detect-secrets engine (23 plugins) + 16 custom patterns (OpenAI, Anthropic, AWS, Databricks, HuggingFace, Groq, Mistral, etc.), base64/hex encoded-secret decoding, high-entropy detection, git credential leakage

Reference catalog:

Core Commands

# Full installation scan (all scanners, default target=. )
agentsec scan

# JSON report for CI parsing and pipelines
agentsec scan -o json -f report.json

# SARIF output for GitHub code scanning upload
agentsec scan -o sarif -f results.sarif

# Run only selected scanners for focused checks
agentsec scan -s installation,mcp

# Fail build only at/above selected severity
agentsec scan --fail-on critical
# Preview profile changes without writing config
agentsec harden -p workstation

# Apply profile and write hardened config values
agentsec harden -p workstation --apply

# Watch files and auto re-scan on security-relevant changes
agentsec watch ~/.openclaw -i 2

# Pre-install package gate (scan before install)
agentsec gate npm install express

# Pin MCP tool descriptions for rug-pull detection
agentsec pin-tools

# Generate shell hook wrappers for npm/pip install flows
agentsec hook --shell zsh

List available scanners (names + descriptions):

agentsec list-scanners

Hardening Profiles

Profile Intended use
workstation Single-owner local usage
vps Remote/self-hosted deployment
public-bot Highest restriction for untrusted public input

Use agentsec show-profile <name> to inspect exact changes before applying.

Output and Exit Codes

Output formats:

  • terminal (default)
  • json
  • sarif

Exit codes:

  • 0: no findings at/above threshold
  • 1: findings found at/above threshold
  • 2: usage error (e.g., unknown scanner name)
  • 3: runtime error (e.g., file access failure)

GitHub Actions

Use the bundled composite action:

name: Agent Security
on: [push, pull_request]

jobs:
  agentsec:
    runs-on: ubuntu-latest
    permissions:
      contents: read
      security-events: write
    steps:
      - uses: actions/checkout@v4
      - uses: debu-sinha/agentsec@v0.5.0
        with:
          fail-on: high
          output: sarif
          upload-sarif: 'true'

Action definition:

Screenshots

Screenshots below show the experimental demo sandbox flow (intentionally insecure configuration for detection and hardening demonstration). Gate and hook behavior are documented in docs/case-studies/003-preinstall-gate-blocked-malicious-package.md and docs/adr/ADR-0004-pre-install-gate.md.

Step 1: Initial scan on intentionally insecure demo config

agentsec scan - intentionally insecure demo config

Step 2: Apply workstation hardening profile

agentsec harden - workstation profile applied

Step 3: Re-scan findings after hardening

agentsec scan - post-hardening findings

Step 4: OWASP posture view after hardening

agentsec scan - OWASP posture after hardening

MCP Ecosystem Security Dashboard

Weekly automated security scan of the top 50 MCP server repositories, graded A through F.

Ecosystem Grade Repos Scanned

View the full dashboard - updated every Monday via GitHub Actions.

Benchmarks and Studies

  • Fixture benchmark — precision/recall/F1 over a 20-fixture suite (self-authored fixtures, not independently validated).
  • Top-50 MCP study — agentsec-only repro run with normalized findings output.
  • Top-50 study kit — schema, selection CSV, JSONL findings, and summary JSON.

Current checked-in Top-50 summary data:

  • docs/benchmarks/top50/reports/top50_summary_20260215.json
  • docs/benchmarks/top50/reports/top50_findings_20260215.jsonl

Reproducibility

Fixture benchmark:

python docs/benchmarks/run_benchmark.py

Top-50 study repro:

powershell -ExecutionPolicy Bypass -File scripts\reproduce_top50_study.ps1 -DateStamp 20260215

Note: semgrep and gitleaks baseline runs are optional in the PowerShell repro script and require those tools on PATH.

Adversarial consistency audit (docs vs artifacts):

python scripts/repo_consistency_audit.py

Case Studies

Launch Evidence

Development

git clone https://github.com/debu-sinha/agentsec.git
cd agentsec
pip install -c requirements/constraints-dev.txt -e ".[dev]"
ruff check src tests
pytest

Contribution guide:

Security policy:

Governance

Issue intake is template-driven under .github/ISSUE_TEMPLATE/ to keep triage and reproduction quality high.

License

Apache-2.0

Metadata

Release files for agentsec-ai 0.5.1

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PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

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