The Model Context Protocol (MCP) has rapidly become the universal standard for integrating AI systems with external tools and data, embraced by major tech giants like Microsoft, Google, and OpenAI. However, its swift adoption has introduced significant security challenges, as researchers have identified extensive command injection vulnerabilities across numerous MCP servers, leading to real-world attacks involving credential theft and remote code execution. The protocol creates new attack surfaces, allowing AI agents to execute unauthorized commands or exfiltrate data if MCP servers are inadequately secured. Additionally, the storage of OAuth tokens on MCP servers elevates enterprise-wide risks, as a single breach could compromise access to multiple connected platforms. As attacks have already been carried out, safeguarding MCP implementations requires a comprehensive security strategy, including robust authentication, input validation, runtime monitoring, and human oversight for high-risk activities.