AgentBreeder introduces a novel framework for multi-objective evolutionary search that improves LLM scaffolds while addressing critical safety concerns.

• Achieves 79.4% improvement on safety benchmarks in 'blue' safety-enhancing mode
• Demonstrates how multi-agent systems can be optimized for both performance and safety
• Reveals potential vulnerabilities when running in 'red' adversarial mode
• Provides a methodology to evaluate and mitigate safety risks in multi-agent LLM systems

This research is crucial for security professionals as it highlights how multi-agent systems can be made more robust against potential misuse while maintaining high performance on complex tasks.

AgentBreeder: Mitigating the AI Safety Impact of Multi-Agent Scaffolds via Self-Improvement