Researchers introduce In-Context Adversarial Game (ICAG), a dynamic defense mechanism against jailbreak attacks on Large Language Models that requires no fine-tuning.

• ICAG leverages agent learning to conduct adversarial games that strengthen model defenses
• The approach dynamically adapts to new attack patterns without modifying model parameters
• Demonstrates effective protection against various jailbreak attempts while maintaining model performance
• Offers a practical security solution for deployed LLMs where retraining is costly or impractical

This research provides critical security enhancements for organizations deploying LLM technologies, addressing a key vulnerability while offering a resource-efficient implementation path.

Defending Jailbreak Prompts via In-Context Adversarial Game