mlsanityData to 14 Oct 2026

Comprehensive Vulnerability Analysis is Necessary for Trustworthy LLM-MAS

arXiv 2506.01245 ↗First posted 2 Jun 2025cs.CRcited by 8

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

TThis paper argues that 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.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. First posted on arXiv 2 Jun 2025.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 4.

8 papers.

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  1. 2026SoK: When Safe Agents Fail Together: The Security of Multi Agent LLM Systems
  2. 2025Seven Security Challenges in Cross-domain Multi-agent LLM Systems
  3. 2026Toward Secure LLM Agents: Threat Surfaces, Attacks, Defenses, and Evaluation
  4. 2025A Survey on Trustworthy LLM Agents: Threats and Countermeasures
  5. 2025The Dark Side of LLMs: Agent-based Attack Vectors for System-level Compromise
  6. 2025Bridging AI and Software Security: A Comparative Vulnerability Assessment of LLM Agent Deployment Paradigms
  7. 2025The Trust Paradox in LLM-Based Multi-Agent Systems: When Collaboration Becomes a Security Vulnerability
  8. 2026LLM agents security duality: a comprehensive survey of self-security and empowered cybersecurity