This ebook examines how MCP servers expand AI capability while creating new attack paths. It explains MCP as the protocol layer that lets AI systems connect to resources, prompts, and tools, then shows why that power raises risk at the point where model intent becomes real action. The guide details major threats including tool poisoning, rug pulls, shadowed tool descriptions, toxic flows, over-broad roots, and observability gaps. It backs those risks with concrete case studies, including GitHub-based private repo leakage and WhatsApp message-history exfiltration through tool shadowing. Its main message is that MCP security must be flow-aware, not just prompt-aware. The final sections recommend explicit consent, least privilege, pinning and integrity checks, runtime monitoring, and MCP-Scan

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