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New framework TG-CI enhances privacy in multi-agent LLM systems

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Task-Graph Contextual Integrity (TG-CI), a novel framework designed to enhance privacy in multi-agent Large Language Model (LLM) systems. Unlike existing methods that assess disclosures in isolation, TG-CI formalizes privacy by considering the entire workflow represented as a Multi-Agent Task Graph (MATG). This approach evaluates information flow across the planned sequence of operations, ensuring that disclosures remain appropriate not just at the point of transfer but also considering how information propagates to downstream agents. Experiments using the new MAPLE-Bench benchmark demonstrated that TG-CI significantly reduces unauthorized disclosures while maintaining high task accuracy, offering complementary trade-offs between privacy, cost, and utility. AI

IMPACT Enhances privacy guarantees for complex, multi-agent AI systems by considering information propagation.

RANK_REASON Academic paper introducing a new framework for multi-agent LLM privacy. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework TG-CI enhances privacy in multi-agent LLM systems

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Academic paper introducing a new framework for multi-agent LLM privacy. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Hengyu Wu, Blaise Delattre, Yang Cao ·

    Judging Before Propagation: Task-Graph Contextual Integrity for Multi-Agent Privacy

    arXiv:2610.39532v1 Announce Type: cross 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 inappr…

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