Individual skill
auditing-mcp-servers
Audit Model Context Protocol servers for injection surfaces, excessive tool scope, authorization gaps, resource over-exposure, and transport weaknesses across stdio, SSE, and Streamable HTTP deployments. Use when reviewing an MCP server implementation, assessing tool definitions for injection or description manipulation risk, auditing the trust boundary between an AI agent and MCP tools, or reviewing MCP server deployment for authentication and authorization controls.
secskills-core/skills/auditing-mcp-servers/SKILL.md
Source commit ca53957bcd8e3b83b5d4d190bb3ad008a4f15c84
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- No
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Not checked yet
- Last checked
- Nov 6, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 188
- Repository last updated
- Sep 4, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
Read excerpt
# Auditing MCP Servers
An MCP server is a privilege boundary — it translates model-generated requests
into real actions (database queries, file operations, API calls, shell
commands). The model's output is shaped by user input, which makes every tool
parameter an indirect injection surface. The security question is not whether
the MCP server is well-coded, but whether an adversarial input to the model can
cause the server to do something the user did not intend.
## When to Use
- Reviewing an MCP server implementation for security issues
- Assessing tool definitions for injection risk or description manipulation
- Auditing the trust boundary between an AI agent and MCP tools