Parameters (Type + Description) Large Language Model Action Description In Natural Language 1) Deduce Input Parameters 2) Call Client 3) Deduce Output from Response 4) Return the Result Response (usually in Natural Language) The Generative AI Hype began in 2023 1 2 3 4 5 6
/ Allowed MCP Servers Populate Allowed MCP Servers Searches and Install MCP Server Verify MCP Meta Data Asks the Question MCP Client JSON-RPC / SSE • Semantic Intent Detection • Pick the Tool based on Capabilities • Input Inference based on User Intent • Instantiate MCP Client • Call MCP Server with Inferred Input Parameters • Pass the Output again to LLM for Summarization • Check Output vs User Intent • Summarize the Output 1 2 3 5 4 2 1 3 4 6
through OAuth has been added to the Protocol • SSE and Streamable HTTP provide necessary protocol encryption • No Governance around MCP Providers • GitHub Copilot has introduced MCP Governance through Registry • Non-Determinism (Agent on behalf of User) • Focus on Deterministic Tool Providing through MCP • Start with Read-Only, Information Providing MCPs
This attack manipulates user behavior by feeding the agent misleading logic or fake dependencies — like gaslighting an AI into making dumb decisions on your behalf. • JSON Injection • Think of it like feeding malware via API. If a plugin pulls data from a tampered local source, it can manipulate outputs, leak sensitive info, or even alter execution flow — all without you noticing. • Function Override • This one replaces core system functions with malicious ones. It’s like teaching your AI assistant to “delete files” when you say, “make a backup.” • Cross-MCP Call Attacks • The most devious — it links your AI agent to other malicious agents or services through encoded prompts. One infected plugin can create a chain reaction across multiple protocols.