This research introduces a novel red teaming agent that mimics real-world adversaries by using a strategic global approach while adapting locally to target responses.

• Develops a dual-level learning framework that maintains overall attack strategies while adjusting tactics based on model responses
• Simulates sophisticated human attackers who iteratively probe for vulnerabilities in multi-turn conversations
• Outperforms existing red teaming methods by discovering more diverse and effective attack approaches
• Provides critical insights for strengthening LLM defenses against increasingly sophisticated security threats

As LLMs become more powerful and widespread, this research addresses the urgent security need for more realistic adversarial testing that mirrors how actual malicious actors operate in the real world.

Strategize Globally, Adapt Locally: A Multi-Turn Red Teaming Agent with Dual-Level Learning