The complete guide to MCP security: How to secure MCP servers & clients

The Model Context Protocol (MCP) is a significant advancement in AI infrastructure, allowing large language models to interact with external tools and APIs, transforming them into active agents capable of executing tasks. However, this capability introduces unique security risks, including prompt injection, tool poisoning, command execution, and token theft. These vulnerabilities arise from the potential manipulation of model-generated inputs, the broad permissions often granted, and unverified endpoints. Several documented cases, such as SQL injection flaws and token leakage, highlight the potential for exploitation. To mitigate these risks, best practices are outlined for both MCP servers and clients, emphasizing strong authentication, input sanitization, token security, and endpoint verification. The need for secure discovery, trust anchoring, and robust audit mechanisms is critical, as is a shared responsibility between servers and clients to maintain the integrity and safety of MCP interactions. Tools like WorkOS can help implement these security measures, providing infrastructure that supports authentication, granular permissions, login monitoring, and session control, ensuring a resilient and secure deployment of MCP. Overall, the security of MCP is an ongoing process, requiring vigilance and adaptation to protect against both current and future threats.