Propagating Unsafe Actions in LLM Controlled Multi-Robot Collaboration via Single Robot Compromise

Computer Science > Robotics [Submitted on 15 May 2026] Propagating Unsafe Actions in LLM Controlled Multi-Robot Collaboration via Single Robot Compromise Zhen Huang, Zhihuang Liu, Weishang Wu, Zhiping Cai Large language models (LLMs) are increasingly used as general planners in embodied intelligence, enabling high level coordination and low level task planning for both single robot and multi-robot collaboration. This increasing reliance on embodied LLM planners also raises critical security concerns, since misaligned or manipulated instructions can be translated into physical actions. Prior work has studied such threats in single robot settings, while security risks in LLM controlled multi-robot collaboration, especially those propagated through inter robot communication, remain largely unexplored. To bridge this gap, we propose a novel attack paradigm for multi-robot system in which the adversary interacts with only a single entry robot. The compromised robot then propagates malicious intent through peer communication, leading to coordinated unsafe actions across the system. Our evaluation, covering high risk dimensions of dereliction of duty, privacy compromise, and public safety hazards, reveals a persistent safety alignment gap in multi-robot planners. We quantify this process with three metrics, obedience, infectiousness, and stealthiness. Experiments demonstrate both persistent attacker control and rapid propagation: obedience reaches 1.00 in the strongest cases, and infectiousness rises to 0.90. Notably, the attack is highly efficient, requiring as few as 3.0 rounds to compromise all the robots while maintaining a stealthiness score of 0.81. Such risks are amplified when robots must resolve trade offs in critical situations, such as emergencies or conflicts of rights, because the coordination mechanism can unintentionally allow adversarial instructions to override safety requirements. The code is available at this https URL. Comments: Accepted by the 35th International Joint Conference on Artificial Intelligence (IJCAI 2026). 9 pages, 4 figures, 3 tables Subjects: Robotics (cs.RO); Cryptography and Security (cs.CR) Cite as: arXiv:2605.15641 [cs.RO]   (or arXiv:2605.15641v1 [cs.RO] for this version)   https://doi.org/10.48550/arXiv.2605.15641 Focus to learn more Submission history From: Zhen Huang [view email] [v1] Fri, 15 May 2026 05:45:52 UTC (7,205 KB) Access Paper: HTML (experimental) view license Current browse context: cs.RO < prev   |   next > new | recent | 2026-05 Change to browse by: cs cs.CR References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)