The article examines the security risks associated with the Model Context Protocol (MCP), which enables dynamic interactions between AI systems and external applications. It highlights vulnerabilities such as content injection, supply-chain attacks, and the potential for agents to unintentionally cause harm. The authors propose practical controls and outline gaps in current AI governance frameworks.

MCP (Model Context Protocol) facilitates connections between AI agents and tools but lacks inherent security, exposing users to risks like command injection, tool poisoning, and silent redefinitions. Recommendations for developers and users emphasize the necessity of input validation, tool integrity, and cautious server connections to mitigate these vulnerabilities. Until MCP incorporates security as a priority, tools like ScanMCP.com may offer essential oversight.