Multi-agent AI systems face security challenges due to their decentralized nature, creating blind spots for hackers. Detection and prevention of malicious behaviors are essential in high-stakes environments like financial trading or healthcare. Behavioral monitoring is crucial, using baseline profiles created with relevant AI safety metrics to spot problems fast. Statistical anomaly detection provides a solid foundation, while machine learning boosts capabilities for complex environments. Trust and reputation systems offer structured ways to evaluate agent reliability, and secure communication channels form the first defense against malicious behaviors. Implementing comprehensive mitigation strategies, including zero-trust principles, role-based access control, verification mechanisms, code verification measures, and agent input/output validation, is essential to prevent malicious agent behaviors in multi-agent systems.