Security risks are a significant concern for multi-agent reinforcement learning (MARL) systems, which involve multiple agents interacting and making decisions. These systems can be vulnerable to various types of attacks, including policy poisoning, reward hacking, environment manipulation, communication channel exploits, and model extraction and stealing. Policy poisoning involves corrupting the learning process of reinforcement learning agents by injecting malicious perturbations during training. Reward hacking occurs when agents exploit imperfections in reward functions to achieve high rewards without fulfilling the intended objectives. Environment manipulation attacks target the learning process by altering the environment in which MARL agents operate, while communication channel exploits compromise coordination and collaborative learning. Model extraction and stealing attacks attempt to reverse-engineer trained RL policies by observing agent behaviors and interactions. To mitigate these risks, it is essential to implement robust reward functions, apply adversarial training techniques, secure inter-agent communication, develop environment integrity verification, and explore specialized tools like Galileo, which provides end-to-end monitoring capabilities for MARL systems. By adopting these strategies, developers can enhance the security of their multi-agent reinforcement learning systems and prevent catastrophic failures impacting critical infrastructure, financial systems, and public safety.