To trust or not to trust: Attention-based Trust Management for LLM Multi-Agent Systems

[2506.02546] To trust or not to trust: Attention-based Trust Management for LLM Multi-Agent Systems Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Cryptography and Security arXiv:2506.02546 (cs) [Submitted on 3 Jun 2025 ( v1 ), last revised 14 Apr 2026 (this version, v2)] Title: To trust or not to trust: Attention-based Trust Management for LLM Multi-Agent Systems Authors: Pengfei He , Zhenwei Dai , Xianfeng Tang , Yue Xing , Hui Liu , Jingying Zeng , Qiankun Peng , Shrivats Agrawal , Samarth Varshney , Suhang Wang , Jiliang Tang , Qi He View a PDF of the paper titled To trust or not to trust: Attention-based Trust Management for LLM Multi-Agent Systems, by Pengfei He and 11 other authors View PDF HTML (experimental) Abstract: Large Language Model-based Multi-Agent Systems (LLM-MAS) have demonstrated strong capabilities in solving complex tasks but remain vulnerable when agents receive unreliable messages. This vulnerability stems from a fundamental gap: LLM agents treat all incoming messages equally without evaluating their trustworthiness. While some existing studies approach trustworthiness, they focus on a single type of harmfulness rather than analyze it in a holistic approach from multiple trustworthiness perspectives. We address this gap by proposing a comprehensive definition of trustworthiness inspired by human communication theory (Grice, 1975). Our definition identifies six orthogonal trust dimensions that provide interpretable measures of trustworthiness. Building on this definition, we introduce the Attention Trust Score (A -Trust), a lightweight, attention-based method for evaluating the trustworthiness of messages. We then develop a principled trust management system (TMS) for LLM -MAS that supports both message-level and agent-level trust assessments. Experiments across diverse multi-agent settings and tasks demonstrate that our TMS significantly improves robustness against malicious inputs. Comments: Accepted to ACL 2026 main Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2506.02546 [cs.CR] (or arXiv:2506.02546v2 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2506.02546 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Pengfei He [ view email ] [v1] Tue, 3 Jun 2025 07:32:57 UTC (1,796 KB) [v2] Tue, 14 Apr 2026 05:32:17 UTC (1,796 KB) Full-text links: Access Paper: View a PDF of the paper titled To trust or not to trust: Attention-based Trust Management for LLM Multi-Agent Systems, by Pengfei He and 11 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CR < prev | next > new | recent | 2025-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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