Diversity Helps Jailbreak Large Language Models
Keywords: Attack, Large Language Model, Safety
TL;DR: We present a novel jailbreaking strategy that employs an attacker LLM to generate diversified and obfuscated adversarial prompts, demonstrating significant improvement over past approaches.
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.
Primary Area: alignment, fairness, safety, privacy, and societal considerations
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Submission Number: 2433
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