Beyond the Prompt: Leveraging Pre-Decoding States for Jailbreak Detection in dLLMs

Diffusion language models (dLLMs) generate text by iteratively denoising masked response positions, exposing hidden states over future response slots before any token is finalized. This creates a detection surface that is absent from standard autoregressive decoding: even when a jailbreak is difficult to identify from the prompt alone, the initial masked response states may already reflect the model's emerging completion. We test this hypothesis on LLaDA-8B-Instruct by training lightweight linear classifiers on two frozen representations: prompt hidden states and pre-decoding masked-response hidden states. Empirically, the two views are complementary: neither classifier strictly dominates the other, and each recovers attacks missed by the other view. We then introduce $\text{ReFuse}$ (Representation Fusion), an inference-time detector that fuses prompt and pre-decoding response classifier scores without modifying model weights or the decoding procedure. Across transferred and dLLM-targeted jailbreaks, $\text{ReFuse}$ reduces average ASR from 63.29% for the undefended model to 3.31%, while keeping average benign refusal on standard utility benchmarks below 1%. These results suggest that pre-decoding response states provide a complementary safety signal for detecting jailbreaks in dLLMs.