MCP Sentinel Scanner: Seven-Layer Detection Pipeline for AI Agent Security
An open-source security analysis tool applying a seven-layer detection pipeline to identify vulnerabilities in AI agent-to-agent communication frameworks, addressing an emerging and rapidly evolving attack surface.
How would your security team detect a malicious MCP server that passes every traditional security scan?
Seven-Layer Detection Pipeline
The MCP Sentinel Scanner addresses critical security gaps in Model Context Protocol implementations through a seven-layer detection pipeline: Semgrep taint scanning, LLM-based metadata analysis, AST deep inspection, cross-file flow extraction, secret detection, pattern matching, and risk judgment with Attack Success Rate scoring.
Control Plane & Enforcement Patterns
Traditional security scanners lack MCP protocol understanding, miss semantic attacks hidden in tool descriptions, and cannot perform the taint analysis needed to track data flow through AI agent communication channels. The Sentinel pipeline combines static, semantic, and structural analysis techniques to detect vulnerabilities that existing tools cannot identify.
Observability & ASR Scoring
Based on peer-reviewed research (Zhao et al., 2025), the scanner addresses all 12 attack categories in the MCP threat taxonomy. With 96% test coverage and 652 vulnerability findings at v1.5, it provides production-ready scanning via Docker, PyPI, and CI/CD integration with five output formats including SARIF for IDE integration.
Your next move
Integrate the MCP Sentinel Scanner into your AI agent validation pipeline — the seven-layer detection pipeline addresses all 12 attack categories identified in peer-reviewed MCP security research.