AI Security Proxy Coverage Jumps from 9% to 63% Using Iterative Benchmark Testing

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A developer built an open-source benchmark called mcp-defense-bench to measure how well security tools protect against threats targeting the Model Context Protocol (MCP), the system modern AI agents use to interact with files, databases, and APIs. The benchmark evaluates 24 known MCP attack vectors and scores defenses based on actual runtime behavior rather than theoretical capability. Using repeated benchmark results as feedback, the developer iteratively improved their own proxy tool, mcp-bastion, from 9% attack coverage to 63% across seven development steps. Key improvements included scanning tool results, validating parameters, blocking DNS-rebinding, and adding cross-tool correlation to catch split-payload attacks like ShareLock. The project highlights how measurable, reproducible testing can drive meaningful progress in the still-emerging field of AI agent security.

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