Story · arXiv

ToolMinimize: Auditing and Rewriting LLM Agent Tool Calls to Minimize Privacy Exposure (arXiv)

paper · Story page

A controlled measurement on GPT-4o, Claude 3.5 Sonnet and Llama-3.3-70B found 81-88% of tool calls carrying privacy-sensitive data the tool didn't need, and explicit privacy instructions still left 36-76% over-sharing. The proposed middleware intercepts each call and rewrites its arguments (remove, generalize, substitute, truncate) instead of allowing or blocking it.

In plain words

  • A new privacy layer rewrites requests sent to outside services so they contain only the information needed.
  • It checks each detail against the service's needs before sending the request.
  • It can remove details, make them less specific, replace them, or shorten them.
  • Tests on 307 requests cut measured privacy exposure by 81.2-92.0% while keeping every request usable for its task.
  • People using action-taking artificial intelligence could share less private information without losing the service they requested.

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