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