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LLM tool-call traces pose significant privacy risks, study finds

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv explores the privacy risks associated with large language model (LLM) agents that interact with external tools. Researchers introduced PrivacyTrace, a benchmark dataset designed to assess how user profiles can be inferred from tool-call traces generated by protocols like the Model Context Protocol (MCP). Experiments demonstrated that these traces can lead to a 26.25% success rate in inferring user attributes, significantly higher than previous methods. The study highlights that tool arguments and returned results are key sources of this privacy leakage, and cross-session trace linking further exacerbates the risk, underscoring the need for privacy-preserving mechanisms in agent-tool interactions. AI

IMPACT Highlights potential privacy vulnerabilities in LLM agent interactions, necessitating the development of privacy-preserving mechanisms.

RANK_REASON The cluster contains a research paper detailing a new benchmark and findings on privacy leakage from LLM tool-call traces. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM tool-call traces pose significant privacy risks, study finds

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The cluster contains a research paper detailing a new benchmark and findings on privacy leakage from LLM tool-call traces. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Donghang Duan, Qingyu Liu, Yifei Liu, Na Zou, Xia Hu, Dongrui Liu ·

    How Much Can Tool Calls Reveal About You? Privacy Leakage from Tool-Call Traces

    arXiv:2610.41885v1 Announce Type: cross Abstract: Large language model (LLM)-based agents interact with external tools through protocols such as the Model Context Protocol (MCP), producing tool-call traces that may expose behavioral signals about users. In this work, we systemati…

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