Analysis
MCP Security Vulnerabilities and Enterprise AI Agent
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Single-source analysis — one report is available.
Why it matters
MCP is a common way enterprises connect AI agents to internal tools and data, so its structural authorization and identity gaps expose organizations to prompt-injection-driven tool misuse that agent self-restriction cannot prevent.
Trussed AI's write-up analyzes recurring structural security weaknesses in enterprise Model Context Protocol (MCP) deployments, including shared-credential identity, decentralized authorization with no central policy engine, dynamic tool discovery expanding prompt-injection surface, and weak auditability. It argues for external runtime governance—per-session scoped identity, a centralized policy engine intercepting tool calls, independent audit logging, and human approval for high-impact actions.