[2603.20390] CAMA: Exploring Collusive Adversarial Attacks in c-MARL

Computer Science > Machine Learning arXiv:2603.20390 (cs) [Submitted on 20 Mar 2026] Title:CAMA: Exploring Collusive Adversarial Attacks in c-MARL Authors:Men Niu, Xinxin Fan, Quanliang Jing, Shaoye Luo, Yunfeng Lu View a PDF of the paper titled CAMA: Exploring Collusive Adversarial Attacks in c-MARL, by Men Niu and 4 other authors View PDF HTML (experimental) Abstract:Cooperative multi-agent reinforcement learning (c-MARL) has been widely deployed in real-world applications, such as social robots, embodied intelligence, UAV swarms, etc. Nevertheless, many adversarial attacks still exist to threaten various c-MARL systems. At present, the studies mainly focus on single-adversary perturbation attacks and white-box adversarial attacks that manipulate agents' internal observations or actions. To address these limitations, we in this paper attempt to study collusive adversarial attacks through strategically organizing a set of malicious agents into three collusive attack modes: Collective Malicious Agents, Disguised Malicious Agents, and Spied Malicious Agents. Three novelties are involved: i) three collusive adversarial attacks are creatively proposed for the first time, and a unified framework CAMA for policy-level collusive attacks is designed; ii) the attack effectiveness is theoretically analyzed from the perspectives of disruptiveness, stealthiness, and attack cost; and iii) the three collusive adversarial attacks are technically realized through agent's observation info...