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This paper studies membership inference attacks (MIA) on fine-tuned masked diffusion language models (MDLMs). It proposes a white-box attack using a 46-dimensional feature vector from the model's reconstruction loss at varying masking ratios, achieving high AUC scores and showing MDLMs are more vulnerable than previously thought.