JUMP: Efficient Membership Inference on Fine-Tuned Diffusion Language Models
Membership inference attacks (MIAs) test whether a candidate example was used to train a language model. Existing attacks on fine-tuned discrete diffusion language models (dLLMs) often aggregate reconstruction signals across many mask configurations, requiring repeated model evaluations. We propose Joint Uncertainty Guided Mask Probing (JUMP), an efficient MIA that exploits the ability of dLLMs to predict masked tokens in parallel. Using the pre-fine-tuning checkpoint as a reference, JUMP selects low-confidence positions, masks them jointly, and aggregates clipped target-reference reconstruction gaps. This focuses the attack on positions that reveal stronger membership signals from fine-t...
| Authors | Yeachan Jun, Albert No |
|---|---|
| Primary category | cs.AI |
| Published | 9 May 2026 |
| Last updated at source | 28 September 2026 |
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