Re-Mask and Redirect: Exploiting Denoising Irreversibility in Diffusion Language Models
arXiv:2604.08557v2 Announce Type: replace
Abstract: Safety alignment in diffusion language models (dLLMs) relies on a single load-bearing assumption: that committed tokens are permanent. We show that violating this assumption, by re-masking committed …