This research reveals an alarming vulnerability in LLMs through a systematic iterative prompting technique that progressively refines attacks to bypass ethical constraints.

• Tests across multiple models including GPT-3.5/4, LLaMa2, Vicuna, and ChatGLM
• Leverages persuasion skills to gradually overcome safety mechanisms
• Demonstrates how attackers can methodically analyze response patterns to optimize harmful prompts
• Highlights critical security gaps in current AI safety implementations

This work is crucial for security teams developing more robust defenses against sophisticated jailbreaking attempts, as it exposes how determined attackers can systematically work around existing protections in commercial AI systems.

Original Paper: Iterative Prompting with Persuasion Skills in Jailbreaking Large Language Models