MASTER: Multi-Agent Security Through Exploration of Roles and Topological Structures

Challenge:	Large Language Models (LLMs)-based Multi-Agent Systems (MAS) exhibit remarkable problem-solving and task planning capabilities across diverse domains .
Approach:	They propose a security research framework for LLM-based multi-agent systems . they propose corresponding defense strategies to address MAS security risks .
Outcome:	The proposed framework amplifies the severity of security risks under MAS attacks . it offers an automated construction process for different MAS setups and an interaction paradigm .

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Overview

Challenge

Approach

Outcome

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Miao Yu, Shilong Wang, Guibin Zhang, Junyuan Mao, Chenlong Yin, Qijiong Liu, Kun Wang, Qingsong Wen, Yang Wang

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G-Safeguard: A Topology-Guided Security Lens and Treatment on LLM-based Multi-agent Systems (2025.acl-long)

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Challenge:	Large Language Model (LLM)-based Multi-agent Systems (MAS) have demonstrated remarkable capabilities in various complex tasks, but their vulnerability to adversarial attacks, misinformation propagation, and unintended behaviors have raised significant concerns.
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TAMAS: Benchmarking Adversarial Risks in Multi-Agent LLM Systems (2026.acl-long)

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Robustness in NLP

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BlindGuard: Safeguarding LLM-based Multi-Agent Systems under Unknown Attacks (2026.acl-long)

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Rui Miao, Yixin Liu, Yili Wang, Xu Shen, Yue Tan, Yiwei Dai, Shirui Pan, Xin Wang

Robustness in NLP

Challenge:	Existing supervised defense methods rely on labeled malicious agents to train a supervised model of malicious behavior.
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AMAS: Adaptively Determining Communication Topology for LLM-based Multi-agent System (2025.emnlp-industry)

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Hui Yi Leong, Yuheng Li, Yuqing Wu, Wenwen Ouyang, Wei Zhu, Jiechao Gao, Wei Han

Structured Data in NLP

Challenge:	Large language models (LLMs) have revolutionized natural language processing, but their practical implementation as autonomous multi-agent systems remains fraught with unresolved challenges.
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Red-Teaming LLM Multi-Agent Systems via Communication Attacks (2025.findings-acl)

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Pengfei He, Yuping Lin, Shen Dong, Han Xu, Yue Xing, Hui Liu

Robustness in NLP

Challenge:	Large Language Model-based Multi-Agent Systems (LLM-MAS) have revolutionized complex problem-solving capability by enabling agent collaboration through message-based communications.
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AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments (2025.acl-long)

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Zhiheng Xi, Yiwen Ding, Wenxiang Chen, Boyang Hong, Honglin Guo, Junzhe Wang, Xin Guo, Dingwen Yang, Chenyang Liao, Wei He, Songyang Gao, Lu Chen, Rui Zheng, Yicheng Zou, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, Yu-Gang Jiang

Language Models

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Towards Effective and Efficient Multi-Agent Language Model Systems: Foundations, Prospects, and Applications (2026.acl-tutorials)

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Xuan Wang, Shuxiang Cao, Yuchen Zhuang, Wenqi Shi

Language Models

Challenge:	Multi-agent systems powered by large language models still face challenges . tutorial focuses on three core components to build effective and efficient systems .
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CIA: Inferring the Communication Topology from LLM-based Multi-Agent Systems (2026.acl-long)

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Yongxuan Wu, Xixun Lin, He Zhang, Nan Sun, Kun Wang, Chuan Zhou, Shirui Pan, Yanan Cao

Responsible & Trustworthy NLP

Challenge:	LLM-based multi-agent systems (MAS) have demonstrated remarkable capabilities in solving complex tasks.
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Vulnerabilities of Large Language Models to Adversarial Attacks (2024.acl-tutorials)

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Yu Fu, Erfan Shayegan, Md. Mamun Al Abdullah, Pedram Zaree, Nael Abu-Ghazaleh, Yue Dong

Robustness in NLP

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