by HeadyZhang · MCP 服务器 · ★ 182
Agent Audit Find security vulnerabilities in your AI agent code before they reach production. []() Why Agent Security Fails in 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 / 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...
| Stars | 182 |
| Forks | 21 |
| Language | Python |
| Category | MCP 服务器 |
| License | MIT |
| Quality Score | 63.684/100 |
| Open Issues | 4 |
| Last Updated | 2026-06-07 |
| Created | 2026-02-03 |
| Platforms | cli, mcp, python |
| Est. Tokens | ~142k |
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agent-audit is Static security scanner for LLM agents — prompt injection, MCP config auditing, taint analysis. 51 rules mapped to OWASP Agentic Top 10 (2026). Works with LangChain, CrewAI, AutoGen.. It is categorized as a MCP 服务器 with 182 GitHub stars.
agent-audit is primarily written in Python. It covers topics such as ai-agent, ai-security, ai-security-tool.
You can find installation instructions and usage details in the agent-audit GitHub repository at github.com/HeadyZhang/agent-audit. The project has 182 stars and 21 forks, indicating an active community.
agent-audit is released under the MIT license, making it free to use and modify according to the license terms.