{
  "id": 754,
  "url": "https://arxiv.org/abs/2606.21710v1",
  "title": "PrivacyAlign: Contextual Privacy Alignment for LLM Agents",
  "summary": "AI agents acting on behalf of users are constantly making decisions, and for users to trust their agents, those decisions must align with what they actually want. Privacy is an important alignment problem for agents: every message, post, or tool call an agent makes is a contextual judgment about what is appropriate to share, with whom, and under which conditions. Because such judgments depend on social expectations and norms, human judgment does not merely label privacy violations but also helps",
  "authors": "Manveer Singh Tamber, Abhay Puri, Marc-Etienne Brunet, Perouz Taslakian, Jimmy Lin, Spandana Gella",
  "category": "research",
  "topics": "safety-alignment,privacy-surveillance,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-19T19:50:55.000Z",
  "fetched_at": "2026-07-14T14:14:46.035Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/754",
  "original_url": "https://arxiv.org/abs/2606.21710v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}