The Devil behind the mask: An emergent safety vulnerability of Diffusion
  LLMs
Authors: name: Zichen Wen; name: Jiashu Qu; name: Dongrui Liu; name: Zhiyuan Liu; name: Ruixi Wu; name: Yicun Yang; name: Xiangqi Jin; name: Haoyun Xu; name: Xuyang Liu; name: Weijia Li; name: Chaochao Lu; name: Jing Shao; name: Conghui He; name: Linfeng Zhang
Published: 2025-07-15 08:44:46

Abstract: Diffusion-based large language models (dLLMs) have recently emerged as a
powerful alternative to autoregressive LLMs, offering faster inference and
greater interactivity via parallel decoding and bidirectional modeling.
However, despite strong performance in code generation and text infilling, we
identify a fundamental safety concern: existing alignment mechanisms fail to
safeguard dLLMs against context-aware, masked-input adversarial prompts,
exposing novel vulnerabilities. To this end, we present DIJA, the first
systematic study and jailbreak attack framework that exploits unique safety
weaknesses of dLLMs. Specifically, our proposed DIJA constructs adversarial
interleaved mask-text prompts that exploit the text generation mechanisms of
dLLMs, i.e., bidirectional modeling and parallel decoding. Bidirectional
modeling drives the model to produce contextually consistent outputs for masked
spans, even when harmful, while parallel decoding limits model dynamic
filtering and rejection sampling of unsafe content. This causes standard
alignment mechanisms to fail, enabling harmful completions in alignment-tuned
dLLMs, even when harmful behaviors or unsafe instructions are directly exposed
in the prompt. Through comprehensive experiments, we demonstrate that DIJA
significantly outperforms existing jailbreak methods, exposing a previously
overlooked threat surface in dLLM architectures. Notably, our method achieves
up to 100% keyword-based ASR on Dream-Instruct, surpassing the strongest prior
baseline, ReNeLLM, by up to 78.5% in evaluator-based ASR on JailbreakBench and
by 37.7 points in StrongREJECT score, while requiring no rewriting or hiding of
harmful content in the jailbreak prompt. Our findings underscore the urgent
need for rethinking safety alignment in this emerging class of language models.
Code is available at https://github.com/ZichenWen1/DIJA.