Diversity Helps Jailbreak Large Language Models

Authors: Weiliang Zhao, Daniel Ben-Levi, Junfeng Yang, Chengzhi Mao
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: Attack, Large Language Model, Safety

Abstract:
We have uncovered a powerful jailbreak technique that leverages large language models' ability to diverge from prior context, enabling them to bypass safety constraints and generate harmful outputs. By simply instructing the LLM to deviate and obfuscate previous attacks, our method dramatically outperforms existing approaches, achieving up to a 62\% higher success rate in compromising nine leading chatbots, including GPT-4, Gemini, and Llama, while using only 12\% of the queries. This revelation exposes a critical flaw in current LLM safety training, suggesting that existing methods may merely mask vulnerabilities rather than eliminate them. Our findings sound an urgent alarm for the need to revolutionize testing methodologies to ensure robust and reliable LLM security.

Decision: reject

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