Diversity Helps Jailbreak Large Language Models (2025)

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 62.83% higher success rate in compromising ten leading chatbots, including GPT-4, Gemini, and Llama, while using only 12.9% 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.

## introduction exposing the vulnerabilities and weaknesses of large language models (llms) under jailbreak attack is important, especially before their deployment in critical applications @xcite . despite research in jailbreaking llms that aims to discover alignment weakness @xcite by circumventing safety measures to elicit harmful content @xcite @xcite , recent efforts to increase llm alignment with safety guidelines has made many prior attack strategies no longer effective @xcite @xcite @xcite . existing jailbreaking techniques often rely on laborious human engineering @xcite @xcite , whitebox access to model internals @xcite @xcite @xcite @xcite ## conclusion we introduce a novel approach to jailbreaking llms by encouraging higher levels of diversification and adjacent obfuscated prompting. our results demonstrate increased performance on several metrics, including attack success rate, queries per objective, and run time. our findings suggest that differentiation of adversarial prompting, along with sentence-level obfuscation for thorough subspace searching, is key to effectively and efficiently jailbreaking llms. we hope that our findings will be able to inform further research in ai alignment by revealing where current safety mechanisms fail.