A benchmark of 85 manually curated workplace scenarios reveals that multi-user AI agent systems suffer high rates of contextual integrity violations across outputs, inter-agent communication, and shared memory.
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=
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PiSAs: Benchmarking Contextual Integrity in Multi-User Agentic Systems
A benchmark of 85 manually curated workplace scenarios reveals that multi-user AI agent systems suffer high rates of contextual integrity violations across outputs, inter-agent communication, and shared memory.