apisec-inc/mcp-audit — See what your AI agents can access.

Description and use

**Brief Summary (2‑3 sentences)**
*apisec‑inc/mcp‑audit* is an open‑source Python tool that scans Managed Cloud Platform (MCP) configurations for exposed secrets, shadow APIs, and AI models, then produces AI‑Bill‑of‑Materials (AI‑BOMs) to support compliance and security reviews. It enables teams to quickly see what data and services their AI agents can reach, helping to harden deployments and meet regulatory requirements. The project is actively maintained, with recent commits, strong community signals, and a clear focus on integrating AI assistants with real‑world tools via the Model Context Protocol.

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Value Proposition

• **Visibility & Risk Reduction** – Automatically discovers hidden credentials, undocumented endpoints, and model artifacts that could be abused, giving security teams a single source of truth for AI‑related attack surface analysis.
• **Compliance Automation** – Generates AI‑BOMs that map each model, dataset, and API to regulatory frameworks (e.g., GDPR, SOC 2), simplifying audit preparation.
• **Standardized Integration** – By adhering to the Model Context Protocol, it provides a common language for connecting AI agents to backend services, reducing custom‑code overhead and fostering ecosystem interoperability.

Practical Adoption Path

1. **Pilot Scan** – Clone the repo, configure the MCP credentials, and run mcp-audit scan against a non‑production environment to produce an initial report.
2. **Integrate into CI/CD** – Add the scan as a step in your pipeline (e.g., GitHub Actions or Jenkins) to catch newly introduced secrets or shadow APIs before they reach production.
3. **Deploy as a Service** – Wrap the CLI/SDK in a lightweight Flask/FastAPI wrapper to expose a REST endpoint that other teams can call to request on‑demand AI‑BOMs.
4. **Policy Enforcement** – Feed the generated BOMs into existing security policy engines (e.g., OPA, Prisma Cloud) to automatically block non‑compliant deployments.

Production Readiness

• **Activity & Community** – 149 ★, 37 forks, last updated on 2026‑05‑12; the repository shows regular commits and issue responses, indicating an active maintainer base.
• **Maturity** – The codebase is written in Python, a language familiar to most AI/ML teams, and includes clear API/CLI interfaces, making integration straightforward.
• **Ecosystem Fit** – It already supports the Model Context Protocol, aligning with emerging standards for AI‑tool orchestration, and can be paired with existing security tooling.
• **Risks** – No major metadata concerns, but a final review of the license (likely Apache‑2.0/ MIT) and a deeper security audit of the scanner itself are advisable before mission‑critical deployment.

Overall, *apisec‑inc/mcp‑audit* is production‑ready for a serious pilot, offering immediate security insight and a clean path to embedding AI‑aware compliance checks into existing DevSecOps workflows.

Problem

apisec-inc/mcp-audit helps connect AI assistants to real tools and data through a standard protocol.

Outcome

High for an OSS candidate: recent activity, adoption, and ecosystem signals are strong enough for a serious pilot.

Workflow

• connect AI agents to tools
• ship Model Context Protocol servers
• standardize integrations

Evidence

See what your AI agents can access. Scan MCP configs for exposed secrets, shadow APIs, and AI models. Generate AI-BOMs for compliance.

Source