POLAR-Bench: A Diagnostic Benchmark for Privacy-Utility Trade-offs in LLM Agents
LLM agents increasingly have access to private user data and act on the user's behalf when interacting with third-party systems. The user defines what may and must not be shared, and the agent must robustly follow that intent even when third-party systems behave adversarially. We introduce POLAR-Bench (Policy-aware adversarial Benchmark), in which a trusted model with a privacy policy and a task converses with a third-party model that adversarially probes for both task-relevant and protected att
Record details
Published: 18 May 2026
Source: arXiv
Category: Research
Topics: Regulation · Privacy · Healthcare · Agents & autonomy
Retrieved: 14 July 2026
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ethics.ai (18 May 2026), “POLAR-Bench: A Diagnostic Benchmark for Privacy-Utility Trade-offs in LLM Agents,” evidence record 4079, https://ethics.ai/record/4079 (originally published by arXiv).
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