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New benchmark reveals agentic AI models leak more sensitive data with increased capability

By PulseAugur Editorial · [1 sources] ·

A new benchmark, CIMEMORIES DIALOGUE, has been developed to assess privacy leakage in agentic dialogue systems. This benchmark, comprising 4,410 dialogue instances, evaluates how well models handle sensitive information according to contextual norms. The study found that more capable models, particularly those employing chain-of-thought reasoning, tend to leak more sensitive information, and this leakage increases significantly with conversation length. AI

IMPACT Highlights a fundamental trade-off between AI agent capability and privacy, suggesting current systems may structurally leak sensitive data as they become more advanced.

RANK_REASON Research paper introducing a new benchmark and evaluation of LLM agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

New benchmark reveals agentic AI models leak more sensitive data with increased capability

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Research paper introducing a new benchmark and evaluation of LLM agents. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Soumya Sharma, Ayana Hussain, Heber Hwang Arcolezi, Ulrich A\"ivodji, Spandana Gella, Adriana Romero-Soriano, Golnoosh Farnadi ·

    Loose Lips: Understanding Privacy Leakage in Agentic Dialogue

    arXiv:2610.14071v1 Announce Type: cross Abstract: As LLM agents are increasingly deployed in high-stakes enterprise settings, understanding how they handle sensitive information in agentic dialogue has become critical. Privacy leakage in such settings requires reasoning about whe…

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