MCPRadar: A Security Scanner Built for the MCP Ecosystem published: true tags: mcp, security, ai, opensource

Model Context Protocol servers have quickly become the connective tissue between AI agents and the outside world — file systems, databases, APIs, internal tools, you name it. That convenience comes with a catch: the tools, prompts, and schemas an MCP server exposes are a new kind of attack surface, and most traditional scanners simply don't look there. MCPRadar is an open-source project built specifically to close that gap. Why this matters A recent academic study examining nearly 1,900 MCP servers found meaningful security issues in a surprising share of them — general vulnerabilities in roughly 7% and MCP-specific tool poisoning in another 5%. Tool poisoning, prompt injection hidden in tool descriptions, and quietly over-permissioned configurations are easy to miss because they don't look like a "normal" vulnerability — there's no CVE, no obvious buffer overflow, just a tool description that quietly tells an agent to do something it shouldn't. MCPRadar's whole premise is that this class of risk deserves the same rigor as any other part of your CI pipeline. What it actually scans MCPRadar isn't a single-purpose linter — it looks at an MCP server from several angles: Protocol inspection — enumerates tools, prompts, resources, and templates the server exposes, and checks server instructions for suspicious content. Source analysis — walks Python and JavaScript/TypeScript code looking for SSRF, unsafe deserialization, command/SQL injection, Trojan Source tricks, and mismatches between a tool's description and what its code actually does. Configuration review — flags poisoned MCP or agent configuration files, risky hooks, and overly broad permission grants. Supply chain checks — fetches packages without running install scripts, cross-references dependencies against OSV, and can emit a CycloneDX SBOM with hashes and provenance. Change monitoring — stores snapshots in SQLite and diffs them over time, classifying changes as cosmetic, behavioral, or security-relevant so sil