Comprehensive Vulnerability Analysis is Necessary for Trustworthy LLM-MAS
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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Citing papers per year: 2025: 4; 2026: 4.
8 papers.
- 2026Security Threat Modeling for Emerging AI-Agent Protocols: A Comparative Analysis of MCP, A2A, Agora, and ANPcited by 15
- 2026AgentArk: Distilling Multi-Agent Intelligence into a Single LLM Agentcited by 13
- 2025Beyond Data Privacy: New Privacy Risks for Large Language Modelscited by 6
- 2025A Survey on AgentOps: Categorization, Challenges, and Future Directionscited by 4
- 2025Before Humans Join the Team: Diagnosing Coordination Failures in Healthcare Robot Team Simulationcited by 1
- 2026The Containment Gap: How Deployed Agentic AI Frameworks Fail Public-Facing Safety Requirementscited by 1
- 2025Who Gets the Reward & Who Gets the Blame? Evaluation-Aligned Training Signals for Multi-LLM Agentscited by 0
- 2026On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Modelscited by 0
- 2026On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models11 Aug 2026
- 2026The Containment Gap: How Deployed Agentic AI Frameworks Fail Public-Facing Safety Requirements11 Jun 2026
- 2026Security Threat Modeling for Emerging AI-Agent Protocols: A Comparative Analysis of MCP, A2A, Agora, and ANP11 Feb 2026
- 2026AgentArk: Distilling Multi-Agent Intelligence into a Single LLM Agent3 Feb 2026
- 2025Who Gets the Reward & Who Gets the Blame? Evaluation-Aligned Training Signals for Multi-LLM Agents11 Nov 2025
- 2025Beyond Data Privacy: New Privacy Risks for Large Language Models16 Sep 2025
- 2025Before Humans Join the Team: Diagnosing Coordination Failures in Healthcare Robot Team Simulation6 Aug 2025
- 2025A Survey on AgentOps: Categorization, Challenges, and Future Directions4 Aug 2025
Similar, not cited
Close in wording, no citation either way
- 2026SoK: When Safe Agents Fail Together: The Security of Multi Agent LLM Systems
- 2025Seven Security Challenges in Cross-domain Multi-agent LLM Systems
- 2026Toward Secure LLM Agents: Threat Surfaces, Attacks, Defenses, and Evaluation
- 2025A Survey on Trustworthy LLM Agents: Threats and Countermeasures
- 2025The Dark Side of LLMs: Agent-based Attack Vectors for System-level Compromise
- 2025Bridging AI and Software Security: A Comparative Vulnerability Assessment of LLM Agent Deployment Paradigms
- 2025The Trust Paradox in LLM-Based Multi-Agent Systems: When Collaboration Becomes a Security Vulnerability
- 2026LLM agents security duality: a comprehensive survey of self-security and empowered cybersecurity