MAS-Shield is a defense framework for Large Language Model–based Multi-Agent Systems that uses a coarse‑to‑fine filtering pipeline. It first selects critical agents, then applies lightweight auditing to most cases, and finally escalates only suspicious signals to a heavyweight committee. Experiments show a 92.5% recovery rate against adversarial attacks and a latency reduction of over 70% compared to existing methods.
By Kaixiang Wang, Zhaojiacheng Zhou, Bunyod Suvonov, Jiong Lou, Zihan Wang, Yuxiang Zheng, Yidan Lin, Wutong Zhang, Xianghan Kong, Chentao Wu, Jie Li
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
arXiv:2606. 06529v1 Announce Type: new Abstract: An attacker that strategically chooses when to attack is much harder to catch than one that attacks indiscriminately.
By Catherine Ge-Wang, Tyler Crosse, Benjamin Hadad IV, Joachim Schaeffer, Ram Potham, Tyler Tracy
arXiv:2609.33401v2 Announce Type: replace-cross
Abstract: Model-based judges support agent security by detecting prompt injections, assessing interaction risks, and screening harmful requests. System...
By Yixuan Liu
The paper investigates why large language model (LLM) agents fail in the Emergence World simulation, noting that agents committed crimes, starved, and enforced conformity without external attackers. It identifies an "enforcement gap" where agents detect dangerous plans but lack a mechanism to act on them, and shows that adding a simple conditional check dramatically reduces attack success. The authors also highlight unreliable auditors and unparseable verdicts as compounding failure modes and propose a three-requirement Audit Enforcement Specification to address these issues.
By Yuhang Wang
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