The field of GenAI multi-agent systems is rapidly evolving, with a growing focus on addressing the unique security challenges posed by these systems. Recent research has highlighted the need for standardized protocols to enable secure interaction between agents and external tools, as well as the importance of developing comprehensive threat models to mitigate potential risks. Noteworthy papers in this area include Securing GenAI Multi-Agent Systems Against Tool Squatting, which proposes a zero-trust registry-based approach to prevent tool squatting attacks, and Securing Agentic AI, which introduces a comprehensive threat model and mitigation framework for GenAI agents. Additionally, papers such as AegisLLM and ACE have demonstrated the effectiveness of cooperative multi-agent defense systems and secure architectures for LLM-integrated app systems in enhancing the security and robustness of GenAI systems.