This research introduces a representation space guided reinforcement learning framework that systematically identifies vulnerabilities in LLM safety mechanisms.

• Develops xJailbreak, a more effective and interpretable method for analyzing potential jailbreak attacks
• Uses guided exploration rather than random search to identify security weaknesses
• Provides deeper interpretability of how and why jailbreak attempts succeed
• Offers insights for developing more robust safety alignment mechanisms

This work is critical for security professionals as it illuminates how safety measures can be circumvented, enabling more comprehensive defenses against harmful content generation in deployed LLM systems.

xJailbreak: Representation Space Guided Reinforcement Learning for Interpretable LLM Jailbreaking