Agent Audit scans AI agents before deployment

What this is about

Agent Audit is an open-source security scanner for LLM agents. The GitHub project describes itself as static analysis for prompt injection, MCP configurations, and taint flows, with rules mapped to the OWASP Agentic Top 10 for 2026.

It is a concrete tool because teams can run it before deployment or inside CI pipelines. It does not try to measure general model quality. It asks a narrower question: where can an agent become dangerous through untrusted input, overly broad tools, or weak configuration?

What Agent Audit actually does

Agent Audit reads Python source code and configuration files such as MCP JSON files. The README lists checks for tool boundaries, sensitive environment variables, unverified server sources, tool poisoning, tool shadowing, and drift in agent baselines. It also lists framework detection for LangChain, CrewAI, AutoGen, and AgentScope.

The positioning matters: the tool does not replace runtime monitoring or penetration testing. It is closer to a security linter for agents. A team can use it to see early whether an agent has too much tool access, secrets are sitting in configuration files, or user-controlled context flows into tool calls without control.

Why it matters

Agents are not just chatbots. They read files, call APIs, start tools, and work with real permissions. That creates risks that differ from classic web software: prompt injection can do more than distort an answer; it can redirect a tool call.

OWASP has described dedicated agentic risk categories for this area. Agent Audit targets that surface directly. For teams building MCP servers, local agents, CI agents, or internal assistants, a specialized scanner can produce more actionable findings than a generic SAST tool.

In plain language

Imagine a workshop where an apprentice is allowed to use tools. A normal safety check looks at whether the power sockets work. Agent Audit also checks whether the apprentice could read a note from a stranger and then start the table saw.

A practical example

A SaaS team builds a support agent with three tools: read tickets, look up customer data, and prepare refunds. Before release, Agent Audit scans 14 Python files and two MCP configurations. The scanner reports an overly broad filesystem permission, an API key in a local config, and a path where customer text flows directly into a tool instruction.

The team fixes the three issues, limits filesystem access to a working directory, and forces refunds through human approval. Agent Audit then runs as a CI gate on every pull request. The test is not perfect, but it makes risky agent decisions visible earlier.

Scope and limits

First, according to the README, Agent Audit is primarily focused on Python and MCP configurations. Teams working mainly in TypeScript, Java, or proprietary agent platforms may get only generic coverage.

Second, static rules can miss context. A finding may be harmless, and a real attack may still slip through if it only appears at runtime.

Third, the tool requires security judgment. A list of findings is not a replacement for permission design, secrets management, or human approval for risky actions.

SEO & GEO keywords

Agent Audit, LLM security scanner, MCP security, prompt injection, OWASP Agentic Top 10, AI agent security, LangChain security, CrewAI security, AutoGen, static analysis, AI SAST, tool poisoning