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Selection-Invariant Communication Compilers for Privacy-Aware Multi-Agent LLM Workflows
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Jinghan Xu, Longze Fan, Zeyuan Wang, Xinjin Li, Hankai Liu

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ResearcharXiv cs.AI

Selection-Invariant Communication Compilers for Privacy-Aware Multi-Agent LLM Workflows

arXiv:2609.26076v1 Announce Type: new Abstract: Structured multi-agent workflows exchange intermediate messages whose content and form can reveal private state even when the final output is safe. We identify selection-channel leakage: after authorization fixes what may be released, a private-state-aware choice among semantically valid realizations creates an additional inference channel. We introduce the selection-invariant communication compiler(SICC), which constrains this post-authorization representation kernel rather than prescribing templates. Any deterministic or independently public-randomized generator satisfying the invariant is valid; requirement-indexed canonical forms are one auditable implementation. We prove a compositional communication-layer guarantee: authorization, public-only form generation, and a dependency-safe utility gate make the emitted transcript reveal no information beyond the complete authorized view. Private-state-aware selection remains vulnerable after surface-disjoint and length-matched controls. Across 132 AgentLeak communication replays and 100 executable LangGraph tasks, deterministic SICC retains complete protocol utility without a positive excess-gain signal; independent public randomization preserves the same result in AgentLeak and 480 controlled cases.

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This story was published by arXiv cs.AI and written by Jinghan Xu, Longze Fan, Zeyuan Wang, Xinjin Li, Hankai Liu. SyncAI.news shows a preview; the complete article is on the publisher's site.

Read the full story on arxiv.org

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