arXiv cs.AI· Hengyu Wu, Blaise Delattre, Yang Cao·· 14 小时前AI 评分31
多智能体隐私新框架 TG-CI：在信息传播前完成判断
Judging Before Propagation: Task-Graph Contextual Integrity for Multi-Agent Privacy
AI 导读
研究者提出 Task-Graph Contextual Integrity（TG-CI），将多智能体工作流建模为 Multi-Agent Task Graph，把上下文完整性从单次信息传递扩展到有状态的图级信息流，并给出 Full-Scope 与 Auto-Region 两种传播范围授权策略。
正文 · 原文
Abstract:Multi-agent LLM systems increasingly rely on persistent memory and inter-agent communication. These capabilities create privacy risks that extend beyond individual interactions: a release appropriate in isolation may become inappropriate when combined with information previously disclosed to a recipient or propagated to downstream agents along the planned workflow. Existing Contextual Integrity (CI) mechanisms for agentic systems typically rely on local judgments that assess each disclosure in isolation, with or without regard to the recipient's history, but do not account for how information propagates downstream. However, compliance at the current boundary does not guarantee appropriateness across the workflow. We introduce Task-Graph Contextual Integrity (TG-CI), to our knowledge the first task-graph-native formalization of Contextual Integrity for multi-agent systems. TG-CI represents a workflow as a Multi-Agent Task Graph (MATG) and extends CI from atomic transfers to stateful, graph-level information flows by jointly considering an item's intended propagation scope and recipients' evolving disclosure states. We operationalize TG-CI through propagation-scoped authorization under two complementary policies: Full-Scope, which requires admissibility across the complete downstream scope, and Auto-Region, which confines each item to its maximal admissible region using lightweight frontier gates. We further introduce MAPLE-Bench, a benchmark of 144 cases across four scenarios that jointly vary MATG topology, disclosure history, compromised-agent placement, and item-level privacy policies. In our primary experimental configuration, TG-CI reduces the mean unauthorized disclosure count by 85.8-94.1% relative to the Local-History CI baseline, while retaining 93.6-96.1% of the baseline's task accuracy, with the two enforcement policies providing complementary privacy--cost--utility trade-offs.
| Subjects: | Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.39532 [cs.CR] |
| (or arXiv:2610.39532v1 [cs.CR] for this version) | |
| https://doi.org/10.48550/arXiv.2610.39532 arXiv-issued DOI via DataCite |
Submission history
From: Hengyu Wu [view email]
[v1]
Thu, 29 Oct 2026 07:31:50 UTC (351 KB)
来源：arXiv cs.AI · arxiv.org