Shannon Entropy Tool Aims to Stop AI Agents From Leaking Secrets in LLM Logs

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AI agents with database or API access via MCP servers can inadvertently expose sensitive credentials—such as AWS keys or session tokens—directly into LLM context windows and provider logs. Unlike personally identifiable information, secrets lack a fixed format, making traditional regex-based redaction unreliable when developers rotate or change token structures. A tool called the Tool Output Entropy Sanitizer addresses this by applying Shannon entropy calculation, flagging high-randomness text segments that exceed a threshold of 4.5 before they reach the LLM. It uses a sliding-window analysis, constrained to between 16 and 64 characters, to pinpoint entropy spikes within longer outputs rather than evaluating entire strings at once. Redacted segments are replaced with a structured placeholder like [REDACTED_HIGH_ENTROPY:length], preserving structural context for the model while keeping sensitive data out of its input.

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