PiSAs: Benchmarking Contextual Integrity in Multi-User Agentic Systems
As LLM agents evolve into shared organisational infrastructure, cross-user privacy leakage becomes a systemic risk — this benchmark quantifies contextual integrity failures in multi-user deployments.
Summary written by editorial AI · Source link below
arXiv:2607.05318v2 Announce Type: replace-cross Abstract: As LLM agents evolve from single-user assistants into shared organizational infrastructure, new privacy risks emerge: inappropriate information may not only be exposed through outputs for external recipients, but also internally across users through inter-agent messages, shared memory and user-agent interactions. These data spillage risks are not captured by existing privacy benchmarks grounded in contextual integrity (CI) as they focus
Editorial Analysis
Enterprises scaling LLM agents across teams face a new category of internal privacy risk: information flowing between users in ways that violate contextual norms and potentially GDPR.
Conduct a cross-user information flow assessment for any multi-user LLM agent system using contextual integrity principles before broader organisational rollout.
Shared LLM agents in enterprise settings can leak information between users — a privacy risk that grows as organisations scale agentic AI.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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