MASTER: Multi-Agent Security Through Exploration of Roles and Topological Structures - A Comprehensive Framework

ACL ARR 2025 May Submission2780 Authors

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 \textbf{MASTER}, a novel security research framework for MAS, focusing on diverse \textbf{R}ole configurations and \textbf{T}opological 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.

Paper Type: Long

Research Area: Ethics, Bias, and Fairness

Research Area Keywords: ethical considerations in NLP applications, model bias/fairness evaluation, model bias/unfairness mitigation

Contribution Types: NLP engineering experiment, Data resources, Data analysis

Languages Studied: English

Submission Number: 2780