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    <abstract>Large Language Models (LLMs)-based Multi-Agent Systems (MAS) exhibit remarkable problem-solving and task planning capabilities across diverse domains due to their specialized agentic roles and collaborative interactions. However, this also amplifies the severity of security risks under MAS attacks. To address this, we introduce MASTER, a novel security research framework for MAS, focusing on diverse Role configurations and Topological structures across various scenarios. MASTER offers an automated construction process for different MAS setups and an information-flow-based interaction paradigm. To tackle MAS security challenges in varied scenarios, we design a scenario-adaptive, extensible attack strategy utilizing role and topological information, which dynamically allocates targeted, domain-specific attack tasks for collaborative agent execution. Our experiments demonstrate that such an attack, leveraging role and topological information, exhibits significant destructive potential across most models. Additionally, we propose corresponding defense strategies, substantially enhancing MAS resilience across diverse scenarios. We anticipate that our framework and findings will provide valuable insights for future research into MAS security challenges.</abstract>
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%0 Conference Proceedings
%T MASTER: Multi-Agent Security Through Exploration of Roles and Topological Structures - A Comprehensive Framework
%A Zhu, Yifan
%A Zhang, Chao
%A Shi, Xin
%A Zhang, Xueqiao
%A Yang, Yi
%A Luo, Yawei
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F zhu-etal-2025-master
%X Large Language Models (LLMs)-based Multi-Agent Systems (MAS) exhibit remarkable problem-solving and task planning capabilities across diverse domains due to their specialized agentic roles and collaborative interactions. However, this also amplifies the severity of security risks under MAS attacks. To address this, we introduce MASTER, a novel security research framework for MAS, focusing on diverse Role configurations and Topological structures across various scenarios. MASTER offers an automated construction process for different MAS setups and an information-flow-based interaction paradigm. To tackle MAS security challenges in varied scenarios, we design a scenario-adaptive, extensible attack strategy utilizing role and topological information, which dynamically allocates targeted, domain-specific attack tasks for collaborative agent execution. Our experiments demonstrate that such an attack, leveraging role and topological information, exhibits significant destructive potential across most models. Additionally, we propose corresponding defense strategies, substantially enhancing MAS resilience across diverse scenarios. We anticipate that our framework and findings will provide valuable insights for future research into MAS security challenges.
%U https://aclanthology.org/2025.findings-emnlp.917/
%P 16895-16921

Markdown (Informal)

[MASTER: Multi-Agent Security Through Exploration of Roles and Topological Structures - A Comprehensive Framework](https://aclanthology.org/2025.findings-emnlp.917/) (Zhu et al., Findings 2025)

• MASTER: Multi-Agent Security Through Exploration of Roles and Topological Structures - A Comprehensive Framework (Zhu et al., Findings 2025)

ACL

• Yifan Zhu, Chao Zhang, Xin Shi, Xueqiao Zhang, Yi Yang, and Yawei Luo. 2025. MASTER: Multi-Agent Security Through Exploration of Roles and Topological Structures - A Comprehensive Framework. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 16895–16921, Suzhou, China. Association for Computational Linguistics.