AutoRedTeamer delivers fully automated, end-to-end red teaming for LLMs through an innovative multi-agent system that identifies vulnerabilities without human intervention.

• Employs a memory-guided attack selection mechanism that learns and adapts over time
• Creates a comprehensive attack database that evolves with emerging threats
• Achieves superior coverage and efficiency compared to traditional red teaming approaches
• Provides a scalable solution for ongoing LLM security evaluation

This research addresses the critical security challenge of protecting AI systems as they become more powerful and widely deployed in business applications, enabling proactive vulnerability detection and mitigation.

AutoRedTeamer: Autonomous Red Teaming with Lifelong Attack Integration