Jailbreaking with Universal Multi-Prompts

Yu-Ling Hsu, Hsuan Su, Shang-Tse Chen

Jailbreaking with Universal Multi-Prompts: 9 upvotes on Hugging Face Daily Papers, #11 of 14 papers on 2025-02-06. Day-by-day upvote history.

Large language models (LLMs) have seen rapid development in recent years, revolutionizing various applications and significantly enhancing convenience and productivity. However, alongside their impressive capabilities, ethical concerns and new types of attacks, such as jailbreaking, have emerged. While most prompting techniques focus on optimizing adversarial inputs for individual cases, resulting in higher computational costs when dealing with large datasets. Less research has addressed the more general setting of training a universal attacker that can transfer to unseen tasks. In this paper, we introduce JUMP, a prompt-based method designed to jailbreak LLMs using universal multi-prompts. We also adapt our approach for defense, which we term DUMP. Experimental results demonstrate that our method for optimizing universal multi-prompts outperforms existing techniques.

Paper page on Hugging Face · arXiv

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