{
  "id": 17775,
  "url": "https://arxiv.org/abs/2606.00152",
  "title": "PrivacyPeek: Auditing What LLM-Based Agents Acquire, Not Just What They Say",
  "summary": "LLM-based agents are rapidly advancing, autonomously invoking external tools to complete multi-step tasks for users. However, agents often acquire more sensitive information than the task requires. Existing privacy benchmarks audit what the agent's response or outgoing actions disclose, but overlook the acquisition stage where data first enters the agent's context. The over-acquired information is then one careless action or one attack away from an outright leak. To assess its prevalence, we int",
  "authors": "Mingxuan Zhang, Jiahui Han, Dadi Guo, Songze Li, Guanchu Wang, Na Zou",
  "category": "research",
  "topics": "privacy-surveillance,agents-autonomy,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-05T20:00:00.000Z",
  "fetched_at": "2026-08-10T05:10:00.488Z",
  "source_slug": "hf-daily",
  "source_name": "HuggingFace Daily Papers",
  "source_homepage": "https://huggingface.co/papers",
  "ethics_ai_record_url": "https://ethics.ai/record/17775",
  "original_url": "https://arxiv.org/abs/2606.00152",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}