Find security vulnerabilities in your AI agent code before they reach production.

AI agents are not just chatbots. They execute code, call tools, and touch real systems, so one unsafe input path can become a production incident.

• Prompt injection rewrites agent intent through user-controlled context
• Unsafe tool inputs can reach subprocess/eval and become command execution
• MCP configuration mistakes can leak credentials and expand access unintentionally

If your team ships agent features, owns CI security gates, or operates MCP servers and tool integrations, this is a high-probability risk surface rather than an edge case. You likely need this before every merge if agent code can trigger tools, commands, or external systems.

Agent Audit catches these issues before deployment with an analysis core designed for agent workflows today: tool-boundary taint tracking, MCP configuration auditing, and semantic secret detection, with room to extend into learning-assisted detection over time.

Think of it as security linting for AI agents, with 60+ rules mapped to the OWASP Agentic Top 10 (2026). Includes a dedicated DeFi Agent Security profile for blockchain interactions.

1. Install

pip install agent-audit

1. Scan your project

agent-audit scan ./your-agent-project

1. Interpret and gate in CI

# Show only high+ findings
agent-audit scan . --severity high

# Fail CI when high+ findings exist
agent-audit scan . --fail-on high

--severity controls what is reported. --fail-on controls when the command exits with code 1.

Sample report output:

╭──────────────────────────────────────────────────────────────────────────────╮
│ Agent Audit Security Report                                                  │
│ Scanned: ./your-agent-project                                                │
│ Files analyzed: 2                                                            │
│ Risk Score: 8.4/10 (HIGH)                                                    │
╰──────────────────────────────────────────────────────────────────────────────╯

BLOCK -- Tier 1 (Confidence >= 90%) -- 16 findings

  AGENT-001: Command Injection via Unsanitized Input
    Location: agent.py:21
    Code: result = subprocess.run(command, shell=True, capture_output=True, text=True)

  AGENT-010: System Prompt Injection Vector in User Input Path
    Location: agent.py:13
    Code: system_prompt = f"You are a helpful {user_role} assistant..."

  AGENT-041: SQL Injection via String Interpolation
    Location: agent.py:31
    Code: cursor.execute(f"SELECT * FROM users WHERE name = '{query}'")

  AGENT-031: Mcp Sensitive Env Exposure
    Location: mcp_config.json:1
    Code: env: {"API_KEY": "sk-a***"}

  ... and 15 more

Summary:
  BLOCK: 16 | WARN: 2 | INFO: 1
  Risk Score: =========================----- 8.4/10 (HIGH)

Full coverage of all 10 OWASP Agentic Security categories. See all rules ->

• Agent developers building with LangChain, CrewAI, AutoGen, OpenAI Agents SDK, or raw function-calling -- run it before every deploy
• Security engineers reviewing agent codebases -- get a structured report in SARIF for GitHub Security tab
• Teams shipping MCP servers -- validate your mcp.json / claude_desktop_config.json for secrets, auth gaps, and supply chain risks

# Scan a project
agent-audit scan ./my-agent

# JSON output for scripting
agent-audit scan ./my-agent --format json

# SARIF output for GitHub Code Scanning
agent-audit scan . --format sarif --output results.sarif

# Only fail CI on critical findings
agent-audit scan . --fail-on critical

# Inspect a live MCP server (read-only, never calls tools)
agent-audit inspect stdio -- npx -y @modelcontextprotocol/server-filesystem /tmp

Agent Audit includes specialized detection rules for AI Agent x DeFi/Blockchain interactions. These rules cover vulnerabilities unique to agents that interact with on-chain protocols:

• Transaction signing without validation -- agent tool calls that sign and submit transactions without verifying amounts, recipients, or gas parameters
• Overprivileged DeFi tool definitions -- agent tools with unnecessarily broad access to wallet operations
• Credential leakage in agent configs -- private keys, API secrets, or wallet mnemonics exposed in agent orchestration code
• Gas limit governance -- on-chain transactions submitted without explicit gas limits, vulnerable to manipulation
• Weak randomness in settlement code -- use of non-cryptographic random number generators in transaction flows
• Oracle manipulation vectors -- agent pipelines that consume price oracle data without staleness or deviation checks

# Standard agent security scan
agent-audit scan .

# DeFi-specific agent security scan (includes all standard rules + DeFi rules)
agent-audit scan . --profile defi

The DeFi profile adds 20 additional detection rules (AGENT-090 through AGENT-109) mapped to the OWASP Agentic Security Index, specifically targeting the agent-to-chain interaction boundary that traditional SAST tools (Semgrep, Bandit) cannot detect.

Track only new findings across commits:

# Save current state as baseline
agent-audit scan . --save-baseline baseline.json

# Only report new findings not in baseline
agent-audit scan . --baseline baseline.json --fail-on-new

name: Security Scan
on: [push, pull_request]

jobs:
  agent-audit:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Run Agent Audit
        uses: HeadyZhang/agent-audit@v1
        with:
          path: '.'
          fail-on: 'high'
          upload-sarif: 'true'

Create .agent-audit.yaml in your project root:

# Ignore specific rules for certain paths
ignore:
  - rule_id: AGENT-003
    paths:
      - "auth/**"
    reason: "Auth module legitimately communicates externally"

# Scan settings
scan:
  exclude:
    - "tests/**"
    - "venv/**"
  min_severity: low
  fail_on: high

Agent Audit combines three analysis engines:

1. Python AST Scanner -- walks the abstract syntax tree to trace data flow from @tool parameters to dangerous sinks (subprocess, eval, cursor.execute), with intra-procedural taint tracking and sanitization detection
2. MCP Config Scanner -- parses mcp.json / claude_desktop_config.json / YAML configs to check filesystem permissions, supply chain integrity, credential exposure, and auth gaps
3. Secret Detector -- pattern-matches hardcoded API keys (AWS, OpenAI, Anthropic, GitHub, etc.) with framework-aware suppression to reduce false positives from Pydantic schema definitions

For technical details on detection methodology and benchmark results, see ARCHITECTURE.md.

git clone https://github.com/HeadyZhang/agent-audit
cd agent-audit/packages/audit
poetry install
poetry run pytest tests/ -v
poetry run agent-audit scan .

See CONTRIBUTING.md for guidelines.

MIT License - see LICENSE for details.

• Based on the OWASP Agentic Top 10 (2026)
• Inspired by the need for better AI agent security tooling