{"ID":5936954,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-09T16:45:10.440590912Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.05318","arxiv_id":"2607.05318","title":"PiSAs: Benchmarking Contextual Integrity in Multi-User Agentic Systems","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 agents. These data spillage risks are not captured by existing privacy benchmarks grounded in contextual integrity (CI) as they focus primarily on either single-user settings or interactions between independently owned agents. We introducePiSAs (Privacy in Shared Agentic systems), a benchmark for assessing unintentional leaks with dual CI annotations: whether an information is appropriate for the task, and which users may legitimately access it. This enables direct measurement of cross-user spillage across agentic system components and interfaces, such as outputs, inter-agent communication, and memory. PiSAsis system-agnostic and supports evaluation across different agent topologies and memory regimes. We find that, although system design improves CI compliance, results are bottlenecked by incorrect LLM judgment calls: even state-of-the-art models fail to reliably filter inappropriate content or restrict transmission to authorized users. Our findings underscore the need for privacy-preserving strategies, beyond those studied in this work.","short_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 agents. These data spillage ri...","url_abs":"https://arxiv.org/abs/2607.05318","url_pdf":"https://arxiv.org/pdf/2607.05318v1","authors":"[\"Shubham Gupta\",\"Nazanin Mohammadi Sepahvand\",\"Abhinav Kumar\",\"Cem Subakan\",\"Spandana Gella\",\"Pierre-André Noël\",\"Perouz Taslakian\",\"Eugene Bagdasarian\",\"Valentina Zantedeschi\"]","published":"2026-07-06T16:57:24Z","proceeding":"cs.MA","tasks":"[\"cs.MA\",\"cs.CR\"]","methods":"[\"Large Language Model\",\"Generative Adversarial Network\"]","has_code":false}