Securing Autonomous Agents: Mitigating Secret Exfiltration in Model Context Protocol MCP
The transition of Large Language Models (LLMs) from passive chat interfaces to autonomous agents via the Model Context Protocol (MCP) has fundamentally shifted the threat landscape from prompt injection to automated data exfiltration. Traditional security measures, primarily deterministic regex-based scrubbing, are failing to detect "unknown unknown" secrets, such as high-entropy, rotated API keys or dynamic session tokens. To mitigate this risk, security architectures must shift from pattern matching to probabilistic detection. By implementing Shannon Entropy calculations and sliding-window analysis, organizations can identify high-density information within agentic tool outputs, allowing for effective sanitization and isolation via V8 sandboxing without compromising the LLM's functional context.