Jailbreak Instruction-Tuned LLMs via end-of-sentence MLP Re-weighting

• URL: http://arxiv.org/abs/2410.10150v1
• Date: Mon, 14 Oct 2024 04:32:22 GMT
• Title: Jailbreak Instruction-Tuned LLMs via end-of-sentence MLP Re-weighting
• Authors: Yifan Luo, Zhennan Zhou, Meitan Wang, Bin Dong,
• Abstract summary: We investigate the safety mechanisms of instruction fine-tuned language models (LLMs) We develop novel white-box jailbreak methods: a prompt-specific method and a prompt-general method. Our methods demonstrate robust performance across 7 popular open-source LLMs, size ranging from 2B to 72B.
• Score: 6.263011023287022
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we investigate the safety mechanisms of instruction fine-tuned large language models (LLMs). We discover that re-weighting MLP neurons can significantly compromise a model's safety, especially for MLPs in end-of-sentence inferences. We hypothesize that LLMs evaluate the harmfulness of prompts during end-of-sentence inferences, and MLP layers plays a critical role in this process. Based on this hypothesis, we develop 2 novel white-box jailbreak methods: a prompt-specific method and a prompt-general method. The prompt-specific method targets individual prompts and optimizes the attack on the fly, while the prompt-general method is pre-trained offline and can generalize to unseen harmful prompts. Our methods demonstrate robust performance across 7 popular open-source LLMs, size ranging from 2B to 72B. Furthermore, our study provides insights into vulnerabilities of instruction-tuned LLM's safety and deepens the understanding of the internal mechanisms of LLMs.

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