Seven Security Challenges That Must be Solved in Cross-domain Multi-agent LLM Systems

• URL: http://arxiv.org/abs/2505.23847v3
• Date: Tue, 15 Jul 2025 16:18:29 GMT
• Title: Seven Security Challenges That Must be Solved in Cross-domain Multi-agent LLM Systems
• Authors: Ronny Ko, Jiseong Jeong, Shuyuan Zheng, Chuan Xiao, Tae-Wan Kim, Makoto Onizuka, Won-Yong Shin,
• Abstract summary: Large language models (LLMs) are rapidly evolving into autonomous agents that cooperate across organizational boundaries.<n>This position paper maps the security agenda for cross-domain multi-agent LLM systems.
• Score: 16.838103835766066
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) are rapidly evolving into autonomous agents that cooperate across organizational boundaries, enabling joint disaster response, supply-chain optimization, and other tasks that demand decentralized expertise without surrendering data ownership. Yet, cross-domain collaboration shatters the unified trust assumptions behind current alignment and containment techniques. An agent benign in isolation may, when receiving messages from an untrusted peer, leak secrets or violate policy, producing risks driven by emergent multi-agent dynamics rather than classical software bugs. This position paper maps the security agenda for cross-domain multi-agent LLM systems. We introduce seven categories of novel security challenges, for each of which we also present plausible attacks, security evaluation metrics, and future research guidelines.

Related papers

• BlockA2A: Towards Secure and Verifiable Agent-to-Agent Interoperability [5.483452240835409]
  BlockA2A is a unified multi-agent trust framework for agent-to-agent interoperability.<n>It eliminates centralized trust bottlenecks, ensures message authenticity and execution integrity, and guarantees accountability across agent interactions.<n>It neutralizes attacks through real-time mechanisms, including Byzantine agent flagging, reactive execution halting, and instant permission revocation.
  arXiv  Detail & Related papers  (2025-08-02T11:59:21Z)
• Towards Unifying Quantitative Security Benchmarking for Multi Agent Systems [0.0]
  Evolving AI systems increasingly deploy multi-agent architectures where autonomous agents collaborate, share information, and delegate tasks through developing protocols.<n>One such risk is a cascading risk: a breach in one agent can cascade through the system, compromising others by exploiting inter-agent trust.<n>In an ACI attack, a malicious input or tool exploit injected at one agent leads to cascading compromises and amplified downstream effects across agents that trust its outputs.
  arXiv  Detail & Related papers  (2025-07-23T13:51:28Z)
• OpenAgentSafety: A Comprehensive Framework for Evaluating Real-World AI Agent Safety [58.201189860217724]
  We introduce OpenAgentSafety, a comprehensive framework for evaluating agent behavior across eight critical risk categories.<n>Unlike prior work, our framework evaluates agents that interact with real tools, including web browsers, code execution environments, file systems, bash shells, and messaging platforms.<n>It combines rule-based analysis with LLM-as-judge assessments to detect both overt and subtle unsafe behaviors.
  arXiv  Detail & Related papers  (2025-07-08T16:18:54Z)
• SafeMobile: Chain-level Jailbreak Detection and Automated Evaluation for Multimodal Mobile Agents [58.21223208538351]
  This work explores the security issues surrounding mobile multimodal agents.<n>It attempts to construct a risk discrimination mechanism by incorporating behavioral sequence information.<n>It also designs an automated assisted assessment scheme based on a large language model.
  arXiv  Detail & Related papers  (2025-07-01T15:10:00Z)
• Kaleidoscopic Teaming in Multi Agent Simulations [75.47388708240042]
  We argue that existing red teaming or safety evaluation frameworks fall short in evaluating safety risks in complex behaviors, thought processes and actions taken by agents.<n>We introduce new in-context optimization techniques that can be used in our kaleidoscopic teaming framework to generate better scenarios for safety analysis.<n>We present appropriate metrics that can be used along with our framework to measure safety of agents.
  arXiv  Detail & Related papers  (2025-06-20T23:37:17Z)
• LLM Agents Should Employ Security Principles [60.03651084139836]
  This paper argues that the well-established design principles in information security should be employed when deploying Large Language Model (LLM) agents at scale.<n>We introduce AgentSandbox, a conceptual framework embedding these security principles to provide safeguards throughout an agent's life-cycle.
  arXiv  Detail & Related papers  (2025-05-29T21:39:08Z)
• CoTGuard: Using Chain-of-Thought Triggering for Copyright Protection in Multi-Agent LLM Systems [55.57181090183713]
  We introduce CoTGuard, a novel framework for copyright protection that leverages trigger-based detection within Chain-of-Thought reasoning.<n>Specifically, we can activate specific CoT segments and monitor intermediate reasoning steps for unauthorized content reproduction by embedding specific trigger queries into agent prompts.<n>This approach enables fine-grained, interpretable detection of copyright violations in collaborative agent scenarios.
  arXiv  Detail & Related papers  (2025-05-26T01:42:37Z)
• AGENTFUZZER: Generic Black-Box Fuzzing for Indirect Prompt Injection against LLM Agents [54.29555239363013]
  We propose a generic black-box fuzzing framework, AgentXploit, to automatically discover and exploit indirect prompt injection vulnerabilities.<n>We evaluate AgentXploit on two public benchmarks, AgentDojo and VWA-adv, where it achieves 71% and 70% success rates against agents based on o3-mini and GPT-4o.<n>We apply our attacks in real-world environments, successfully misleading agents to navigate to arbitrary URLs, including malicious sites.
  arXiv  Detail & Related papers  (2025-05-09T07:40:17Z)
• A Trustworthy Multi-LLM Network: Challenges,Solutions, and A Use Case [59.58213261128626]
  We propose a blockchain-enabled collaborative framework that connects multiple Large Language Models (LLMs) into a Trustworthy Multi-LLM Network (MultiLLMN)<n>This architecture enables the cooperative evaluation and selection of the most reliable and high-quality responses to complex network optimization problems.
  arXiv  Detail & Related papers  (2025-05-06T05:32:46Z)
• Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents [0.0]
  Decentralized AI agents will soon interact across internet platforms, creating security challenges beyond traditional cybersecurity and AI safety frameworks.<n>We introduce textbfmulti-agent security, a new field dedicated to securing networks of decentralized AI agents against threats that emerge or amplify through their interactions.
  arXiv  Detail & Related papers  (2025-05-04T12:03:29Z)
• Securing Agentic AI: A Comprehensive Threat Model and Mitigation Framework for Generative AI Agents [0.0]
  This paper introduces a comprehensive threat model tailored specifically for GenAI agents.<n>Research work identifies 9 primary threats and organizes them across five key domains.
  arXiv  Detail & Related papers  (2025-04-28T16:29:24Z)
• Les Dissonances: Cross-Tool Harvesting and Polluting in Multi-Tool Empowered LLM Agents [15.15485816037418]
  This paper presents the first systematic security analysis of task control flows in multi-tool-enabled LLM agents.<n>We identify a novel threat, Cross-Tool Harvesting and Polluting (XTHP), which includes multiple attack vectors.<n>To understand the impact of this threat, we developed Chord, a dynamic scanning tool designed to automatically detect real-world agent tools susceptible to XTHP attacks.
  arXiv  Detail & Related papers  (2025-04-04T01:41:06Z)
• Red-Teaming LLM Multi-Agent Systems via Communication Attacks [10.872328358364776]
  Large Language Model-based Multi-Agent Systems (LLM-MAS) have revolutionized complex problem-solving capability by enabling sophisticated agent collaboration through message-based communications.<n>We introduce Agent-in-the-Middle (AiTM), a novel attack that exploits the fundamental communication mechanisms in LLM-MAS by intercepting and manipulating inter-agent messages.
  arXiv  Detail & Related papers  (2025-02-20T18:55:39Z)
• Multi-Agent Risks from Advanced AI [90.74347101431474]
  Multi-agent systems of advanced AI pose novel and under-explored risks.<n>We identify three key failure modes based on agents' incentives, as well as seven key risk factors.<n>We highlight several important instances of each risk, as well as promising directions to help mitigate them.
  arXiv  Detail & Related papers  (2025-02-19T23:03:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.