arXiv AI July 7, 2026 By Ronny Ko, Jiseong Jeong, Shuyuan Zheng, Chuan Xiao, Tae-Wan Kim, Makoto Onizuka, Won-Yong Shin

Seven Security Challenges in Cross-domain Multi-agent LLM Systems

arXiv:2505. 23847v5 Announce Type: replace-cross 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.

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arXiv AI

Jun 29

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

arXiv:2505. 23847v4 Announce Type: replace-cross 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.

By Ronny Ko, Jiseong Jeong, Shuyuan Zheng, Chuan Xiao, Tae-Wan Kim, Makoto Onizuka, Won-Yong Shin

llms agents safety

arXiv AI

Jun 10

Toward Secure LLM Agents: Threat Surfaces, Attacks, Defenses, and Evaluation

arXiv:2606. 10749v1 Announce Type: cross Abstract: Large language model (LLM) agents are rapidly moving from conversational interfaces to software components that plan, invoke tools, maintain memory, and act on external environments.

By Yuchen Ling, Shengcheng Yu, Zhenyu Chen, Chunrong Fang

llms agents benchmarks

arXiv AI

Sep 2

SoK: When Safe Agents Fail Together: The Security of Multi Agent LLM Systems

arXiv:2609.00595v1 Announce Type: cross Abstract: Safe agents can fail together. Multi-agent LLM systems (MAS) move information, state, decisions, and authority across principal boundaries, creating...

By Rui Yang, Junjie Xu, Zhengyu Liu, Neil Fendley, Yang Hong, Ziyang Li, Yinzhi Cao

llms agents benchmarks

arXiv AI

Jun 30

LLM agents security duality: a comprehensive survey of self-security and empowered cybersecurity

arXiv:2606. 28450v1 Announce Type: cross Abstract: Large language model (LLM) agents are rapidly being integrated into real-world systems.

By Yiwei Xu, Yong Zhuang, Xuanming Liu, Tian Zhang, Bowen Xiao, Xiaoyang Xu, Delong Jiang, Juan Wang, Hongxin Hu

llms agents

arXiv AI

Aug 10

Trustworthy Agent Network: Trust in Agent Networks Must Be Baked In, Not Bolted On

arXiv:2605. 19035v2 Announce Type: replace Abstract: The rapid advancement of Large Language Models has given rise to autonomous LLM-based agents capable of complex reasoning and execution.

By Yixiang Yao, Yuhang Yao, Xinyi Fan, Jiechao Gao, Jie Wang, Minjia Zhang, Srivatsan Ravi, Carlee Joe-Wong

llms agents safety

arXiv AI

Jul 15

Isolation as a First-Class Principle for LLM-Agent System Safety: Concepts, Taxonomy, Challenges and Future Directions

arXiv:2607. 12406v1 Announce Type: new Abstract: The capability of LLM agents to function as the ``brain'' of a system fundamentally expands the scope of analysis beyond a standalone model.

By Huihao Jing, Wenbin Hu, Shaojin Chen, Haochen Shi, Sirui Zhang, Hanyu Yang, Changxuan Fan, Zhongwei Xie, Hongyu Luo, Wun Yu Chan, Wei Fan, Haoran Li, Yangqiu Song

llms agents benchmarks safety

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

Toward Secure LLM Agents: Threat Surfaces, Attacks, Defenses, and Evaluation

SoK: When Safe Agents Fail Together: The Security of Multi Agent LLM Systems

LLM agents security duality: a comprehensive survey of self-security and empowered cybersecurity

Trustworthy Agent Network: Trust in Agent Networks Must Be Baked In, Not Bolted On

Isolation as a First-Class Principle for LLM-Agent System Safety: Concepts, Taxonomy, Challenges and Future Directions