Cooperative multi-agent reinforcement learning (c-MARL) has been widely deployed in real-world applications, including social robots, embodied intelligence, and UAV swarms. However, various adversarial attacks continue to threaten c-MARL systems. Existing studies primarily focus on single-adversary perturbation attacks and white-box adversarial attacks that manipulate agents' internal observations or actions. This paper proposes a novel study of collusive adversarial attacks by strategically organizing malicious agents into three collusive attack modes: Collective Malicious Agents, Disguised Malicious Agents, and Spied Malicious Agents. The proposed unified framework CAMA enables policy-level collusive attacks, with attack effectiveness theoretically analyzed from perspectives of disruptiveness and stealthiness.