CRMay 16

Comprehensive Vulnerability Analysis is Necessary for Trustworthy LLM-MAS

arXiv:2506.0124596.56 citationsh-index: 16
Predicted impact top 1% in CR · last 90 daysOriginality Synthesis-oriented
AI Analysis

For researchers and practitioners building LLM-MAS, this work provides foundational groundwork for security analysis, but it is primarily a position paper without empirical results.

This paper argues that comprehensive vulnerability analysis is necessary for trustworthy LLM-based multi-agent systems (LLM-MAS), proposing a systematic framework to unify research on unique attack surfaces like inter-agent communication and trust relationships. It identifies open challenges such as developing tailored benchmarks and trust management systems.

TThis paper argues that \textbf{a comprehensive vulnerability analysis is essential for building trustworthy Large Language Model-based Multi-Agent Systems (LLM-MAS)}. These systems, which consist of multiple LLM-powered agents working collaboratively, are increasingly deployed in high-stakes applications but face novel security threats due to their complex structures. While single-agent vulnerabilities are well-studied, LLM-MAS introduces unique attack surfaces through inter-agent communication, trust relationships, and tool integration that remain significantly underexplored. We present a systematic framework for vulnerability analysis of LLM-MAS that unifies diverse research. For each type of vulnerability, we define formal threat models grounded in practical attacker capabilities and illustrate them using real-world LLM-MAS applications. This formulation enables rigorous quantification of vulnerability across different architectures and provides a foundation for designing meaningful evaluation benchmarks. We also identify critical open challenges: (1) developing benchmarks specifically tailored to LLM-MAS vulnerability assessment, (2) considering new potential attacks specific to multi-agent architectures, and (3) implementing trust management systems that can enforce security in LLM-MAS. This research provides essential groundwork for future efforts to enhance LLM-MAS trustworthiness.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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