arXiv AI September 28, 2026 By Thong Bach, Dung Nguyen, Thao Minh Le, Truyen Tran

Why Jailbreaks Succeed in Diffusion Language Models: An Energy Landscape Analysis

The paper proposes a framework that explains why jailbreak attacks succeed against diffusion-based large language models (dLLMs) by viewing safety alignment as shaping a denoising energy landscape. It identifies two attack strategies—obscuring the query’s safety disposition at initialization or forcing the denoising path across an energy barrier mid‑trajectory—and introduces three training‑free detection signals that monitor initial safety disposition and kinetic energy in complementary subspaces. Experiments on several dense and sparse dLLMs show that these signals complement each other, and any attack that evades detection also fails to produce harmful content.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Sep 2

Beyond Token Positions: Safety Alignment Across Denoising Steps in Diffusion Language Models

This paper investigates safety alignment in diffusion large language models (dLLMs), which generate text via iterative denoising instead of left‑to‑right decoding. By tracking token distributions and commitment decisions across denoising steps, the authors find that refusal signals are concentrated early in the denoising process and at leading response positions, and that early committed tokens strongly influence the final safety outcome. They introduce Refusal‑Aware Early Commitment (RAEC), a training‑free decoding method that preserves early refusal signals, and demonstrate that RAEC reduces attack success rates on LLaDA and Dream while largely maintaining utility.

By Guoli Wang, Haonan Shi, Tu Ouyang, An Wang

llms diffusion safety

arXiv AI

Aug 10

Diffusion LLMs as Targets and Adversaries: Mechanistic Safety Exploits

arXiv:2608. 07430v1 Announce Type: cross Abstract: Diffusion Large Language Models (DLLMs) replace autoregressive next-token prediction with iterative parallel denoising, yet their internal safety mechanisms remain poorly understood.

By Elena Dumitrescu, Gert Lek, Lydia Y. Chen, J\'er\'emie Decouchant

llms diffusion efficiency multimodal safety

arXiv AI

Jul 20

Latent Fusion Jailbreak: Blending Harmful and Harmless Representations to Elicit Unsafe LLM Outputs

arXiv:2508. 10029v3 Announce Type: replace-cross Abstract: Safety-aligned large language models can still be manipulated through white-box interventions that modify their internal representations.

By Wenpeng Xing, Bohan Yang, Mohan Li, Chunqiang Hu, Haitao Xu, Ningyu Zhang, Bo Lin, Meng Han

llms multimodal benchmarks safety

arXiv AI

Jun 4

MaskForge: Structure-Aware Adaptive Attacks for Jailbreaking Diffusion Large Language Models

arXiv:2606. 04027v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) generate text by iteratively denoising partially masked sequences under bidirectional context, exposing a safety surface distinct from autoregressive LLMs.

By Yingzi Ma, Zhengyue Zhao, Xiaogeng Liu, Minhui Xue, Yue Zhao, Chaowei Xiao

llms diffusion reinforcement-learning benchmarks safety

arXiv Machine Learning

Jun 25

What Intermediate Layers Know: Detecting Jailbreaks from Entropy Dynamics

arXiv:2606. 25182v1 Announce Type: cross Abstract: Jailbreak attacks reveal a persistent weakness in aligned Large Language Models: carefully crafted prompts can elicit policy-violating responses despite safety training.

By Sofiia Nikolenko, Michele Papucci, Mina Rezaei, Shireen Kudukkil Manchingal

llms benchmarks safety

arXiv AI

Jun 8

Step-Wise Refusal Dynamics in Autoregressive and Diffusion Language Models

arXiv:2602. 02600v3 Announce Type: replace-cross Abstract: Diffusion language models (DLMs) have recently emerged as a competitive alternative to autoregressive (AR) models, offering parallel decoding, competitive generation quality, and initial evidence of improved jailbreak robustness.

By Eliron Rahimi, Elad Hirshel, Rom Himelstein, Amit LeVi, Avi Mendelson, Chaim Baskin

llms diffusion safety

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Related stories

Beyond Token Positions: Safety Alignment Across Denoising Steps in Diffusion Language Models

Diffusion LLMs as Targets and Adversaries: Mechanistic Safety Exploits

Latent Fusion Jailbreak: Blending Harmful and Harmless Representations to Elicit Unsafe LLM Outputs

MaskForge: Structure-Aware Adaptive Attacks for Jailbreaking Diffusion Large Language Models

What Intermediate Layers Know: Detecting Jailbreaks from Entropy Dynamics

Step-Wise Refusal Dynamics in Autoregressive and Diffusion Language Models