Hardening Agentic AI & Model Context Protocol (MCP) Tool Execution in Production
As autonomous AI agents shift from passive text generators to active systems capable of executing database queries, invoking REST APIs, and modifying infrastructure via the Model Context Protocol (MCP), the threat landscape shifts dramatically.
In this research article, we analyze the mechanics of Indirect Prompt Injections in agentic workflows, demonstrate how untrusted data smuggles commands into tool parameters, and provide production-grade Python guardrails to sandbox MCP tool execution.
