Jailbreaking with Universal Multi-Prompts
Journal
2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Proceedings of the Conference Findings, NAACL 2025
Start Page
4870
End Page
4891
Date Issued
2025-04
Author(s)
Abstract
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.
Event(s)
2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics, NAACL 2025
Publisher
Association for Computational Linguistics
Type
conference paper