The Model Context Protocol (MCP) is rapidly becoming the standard for connecting Large Language Models (LLMs) to external data and real-world actions through tools and agents. While this standardization accelerates deployment, it introduces novel and critical security vulnerabilities, many of which stem from classic software flaws now amplified by the non-deterministic nature of AI. This article, aimed at professional developers and researchers, delves into the architectural pitfalls of unsecured MCP implementations, covering issues like the Confused Deputy problem, indirect prompt injection,