Overview Model Context Protocol (MCP) is an open, JSON-RPC–based standard that formalizes how AI clients (assistants, IDEs, web apps) connect to servers exposing three primitives—tools, resources, and prompts—over defined transports (primarily stdio for local and Streamable HTTP for remote). MCP’s value for security work is that it renders agent/tool interactions explicit and auditable, with normative
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October 1, 2025ncountered when running models across systems with different permission levels. The postmark-mcp BCC exfiltration shows that MCP servers need the same supply-chain vetting as any privileged code—running untrusted servers without network isolation increases the risk of credential theft. Prompt injection at tool boundaries is where most implementations fail.
With 128K context windows, adversarial payloads hidden deep in “safe” resources can trigger dangerous tool calls that neither models nor humans notice until execution. Tool schema validation helps, but most deployments treat model-requested calls as inherently trusted rather than applying server-side validation. The stdio transport recommendation for local deployments matters—it completely eliminates the network attack surface while maintaining tool integration. My LLM testing uses stdio-equivalent patterns specifically to avoid securing HTTP endpoints.