title: Jailbreaking with Universal Multi-Prompts
summary: 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_authors._id: 67a4609af2e553c1d0da914d
paper_authors.name: Yu-Ling Hsu
paper_authors._id: 67a4609af2e553c1d0da914e
paper_authors.name: Hsuan Su
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-06T14:14:29.721Z
paper_authors._id: 608abf1272b50b02c4b02865
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1619708309549-608abf1272b50b02c4b02865.jpeg
paper_authors.fullname: Hsuan Su
paper_authors.type: user
paper_authors.user: jacksukk
paper_authors._id: 67a4609af2e553c1d0da914f
paper_authors.name: Shang-Tse Chen
paper_title: Jailbreaking with Universal Multi-Prompts
paper_summary: 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_ai_summary: JUMP and DUMP are prompt-based methods for jailbreaking and defending large language models using universal multi-prompts, outperforming existing techniques.
paper_ai_keywords: prompt-based methods jailbreaking large language models universal multi-prompts defense