How to Audit Your MCP Servers for Security Risks

TL;DR: MCP servers run with significant privileges inside AI agent pipelines, and most teams ship them without any security review. mcp-security-scan is an open-source CLI and GitHub Action that checks for credential theft patterns, data exfiltration, unsafe execution, and code obfuscation — and outputs a 0-100 trust score that integrates with AgentGraph's identity layer. The Moltbook breach last year is still the clearest example of what happens when you scale agent infrastructure without thinking about trust. 770,000 agents, zero identity verification, and when it went down it exposed 35,000 emails and 1.5 million API tokens. The tokens were the real problem — many of them were credentials passed through MCP servers that nobody had audited. MCP (Model Context Protocol) servers are the connective tissue of modern agent systems. They sit between your LLM and the outside world, handling tool calls, filesystem access, API requests. That position gives them a lot of power. It also makes them an obvious target. And yet most teams treat MCP servers like they treat npm packages circa 2015: install and trust. What Actually Goes Wrong Before getting into the scanner, it's worth being specific about the threat categories. There are four that show up most often in real codebases: Credential theft — MCP servers that read environment variables indiscriminately, log request/response payloads, or forward tool call arguments to external endpoints. This one is subtle because the server might be doing legitimate work and exfiltrating credentials. Data exfiltration — Outbound HTTP calls to domains that weren't declared in the server's manifest, or calls that happen inside tool handlers where the LLM can influence the destination URL. Prompt injection into tool parameters is the attack vector here. Unsafe execution — eval() , exec() , subprocess calls, or dynamic require() / import() where the argument comes from tool call input. If an LLM can influence what gets executed, you have a