The Model Context Protocol promises seamless AI integration, but are we opening security holes? I explore common MCP vulnerabilities and how to detect them.

marmelab

Model Context Protocol (MCP) servers enable AI agent integration but introduce security vulnerabilities through prompt injection attacks. Three main attack vectors are explored: external prompt injection (hidden malicious instructions in parsed content), tool prompt injection (malicious instructions in tool descriptions), and cross-tool hijacking (one tool contaminating another through concatenated descriptions). Testing with Claude Sonnet 4.5 shows modern models can detect some attacks but remain vulnerable, especially to cross-tool hijacking. Mitigation strategies include carefully reviewing agent actions before approval, regularly auditing installed MCP servers, and preferring self-developed servers over third-party ones.

MCP Security: Understanding Vulnerabilities in Model Context Protocol