The provided texts collectively address the Model Context Protocol (MCP), an open standard designed to enable AI agents to interact with external tools and services. Multiple sources highlight significant security vulnerabilities within MCP implementations, including issues like OAuth discovery flaws, command injection, unrestricted network access, tool poisoning attacks, and secret exposure. Discussions also cover confused deputy problems and session hijacking as specific attack vectors. Proposed mitigation strategies involve secure authentication (HTTPS, JWT), principle of least privilege (PoLP), comprehensive logging and monitoring, and input sanitization. Several entities, including Docker and various open-source initiatives, are actively working on enterprise-grade security solutions, often emphasizing containerization, secure secret management, and strict network controls to address these inherent risks and foster safer AI integrations.