Diffusion Attacker: Diffusion-Driven Prompt Manipulation for LLM Jailbreak
Keywords: LLM safety; LLM jailbreak; Diffusion Language Model; Gumbel Softmax;
Abstract: Large Language Models can generate harmful content when prompted with carefully crafted inputs, a vulnerability known as LLM jailbreaking. As LLMs become more powerful, studying jailbreaking becomes a critical aspect of enhancing security and human value alignment. Currently, jailbreak is usually implemented by adding suffixes or using prompt templates, which suffers from low attack diversity. Inspired by diffusion models, this paper introduces the DiffusionAttacker, an end-to-end generative method for jailbreak rewriting. Our approach employs a seq2seq text diffusion model as a generator, conditioning on the original prompt and guiding the denoising process with a novel attack loss. This method preserves the semantic content of the original prompt while producing harmful content. Additionally, we leverage the Gumbel-Softmax technique to make the sampling process from the output distribution of the diffusion model differentiable, thereby eliminating the need for an iterative token search. Through extensive experiments on the Advbench and Harmbench, we show that DiffusionAttacker outperforms previous methods in various evaluation indicators including attack success rate (ASR), fluency, and diversity.
Primary Area: alignment, fairness, safety, privacy, and societal considerations
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.
Reciprocal Reviewing: I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6.
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Submission Number: 3818
Loading