Securing the Model Context Protocol. Defend agentic AI systems from supply chain, runtime, and code execution threats

As agentic AI shifts from text generation to operational roles, it relies on the Model Context Protocol (MCP) to interface with databases and execute code. While MCP provides essential connectivity, it introduces a sophisticated attack surface. Securing MCP offers a hands-on framework for protecting these autonomous systems throughout their lifecycle. The book begins by deconstructing MCP architecture to establish a rigorous threat model, categorizing risks across supply chain integrity, runtime execution, and code-execution attack vectors. Readers will learn to map these vulnerabilities to testable security controls that mirror adversary behavior. It then details the technical implementation of OAuth 2. 1 and scoped authorization, ensuring every interaction is authenticated and auditable. Beyond identity, the guide explores specialized threats like prompt injection, tool poisoning, and rug pull malicious updates. For enterprise production, it covers deployment hardening - including sandboxing, I/O validation, and secrets management - before addressing governance through RBAC, policies, and human-in-the-loop (HITL) mechanisms. Complete with Python implementations and verification checklists, this book provides the professional roadmap required to deploy agentic AI with institutional-grade security.