Flagged: spawns subprocesses. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by apisec-inc · MCP Server · ★ 153
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is mcp-audit safe to install? View the security audit →
MCP Audit See what your AI agents can access - before they go live. Web App CLI Quick Start What It Does MCP Audit scans your AI development tools (Claude Desktop, Cursor, VS Code) and reveals: Secrets - Exposed API keys, tokens, database passwords APIs - Every endpoint your AI agents connect to AI Models - Which LLMs are configured (GPT-4, Claude, Llama) Risk Flags - Shell access, filesystem access, unverified sources Source-level Sc
| Stars | 153 |
| Forks | 44 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 77.3516767754917/100 |
| Open Issues | 5 |
| Last Updated | 2026-08-18 |
| Created | 2025-12-12 |
| Platforms | claude-code, mcp, python |
| Est. Tokens | ~16k |
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mcp-audit is See what your AI agents can access. Scan MCP configs for exposed secrets, shadow APIs, and AI models. Generate AI-BOMs for compliance.. It is categorized as a MCP Server with 153 GitHub stars.
mcp-audit is primarily written in Python. It covers topics such as agent-security, ai, ai-bom.
You can find installation instructions and usage details in the mcp-audit GitHub repository at github.com/apisec-inc/mcp-audit. The project has 153 stars and 44 forks, indicating an active community.
mcp-audit is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to mcp-audit on Agent Skills Hub include hol-guard, Wazuh-MCP-Server, toolhive-studio. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: