Not yet reviewed by Pith; the record is open.

Effective communication in Multi-Agent Reinforcement Learning (MARL) can significantly enhance coordination and collaborative performance in complex and partially observable environments. However, reliance on communication can also introduce vulnerabilities when agents are misaligned, potentially leading to adversarial interactions that exploit implicit assumptions of cooperative intent. Prior work has addressed adversarial behavior through power regularization through controlling the influence one agent exerts over another, but has largely overlooked the role of communication in these dynamics. This paper introduces Communicative Power Regularization (CPR), extending power regularization specifically to communication channels. By explicitly quantifying and constraining agents' communicative influence during training, CPR actively mitigates vulnerabilities arising from misaligned or adversarial communications. Evaluations across benchmark environments Red-Door-Blue-Door, Predator-Prey, and Grid Coverage demonstrate that our approach significantly enhances robustness to adversarial communication while preserving cooperative performance, offering a practical framework for secure and resilient cooperative MARL systems.