Known MCP Security Flaws and How a Centralized Gateway Can Block Them

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The Model Context Protocol (MCP), which connects large language models to external tools, introduces significant security risks including tool poisoning, command injection, and credential theft. Publicly disclosed vulnerabilities such as CVE-2025-54073 highlight how unvalidated tool metadata and unsanitized transport parameters can compromise host systems. Direct point-to-point connections between AI agents and MCP servers lack centralized access control, making it difficult to detect excessive permissions or data leakage. An MCP gateway addresses these gaps by acting as an inline control plane that enforces input sanitization, tool filtering, and scoped authentication. Open-source solutions like Bifrost, built in Go, offer an architectural approach to governing both model routing and tool execution in production AI environments.

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