Membership Inference in Fine-tuned Diffusion Language Models via Token-level Memorization Asymmetry
cs.CL, cs.CR
Submitted: 2026-09-01
Updated: 2026-09-01
Code: https://github.com/tatsu-lab/stanford_alpaca
Terminology
Sources
- LLaDA2.0: Scaling Up Diffusion Language Models to 100B
- Is My Data in Your AI? Membership Inference Test (MINT) applied to Face Biometrics
- Do Membership Inference Attacks Work on Large Language Models?
- A Probabilistic Fluctuation based Membership Inference Attack for Diffusion Models
- Tulu 3: Pushing Frontiers in Open Language Model Post-Training
- Enhancing Membership Inference Attacks on Diffusion Models from a Frequency-Domain Perspective
- Diffusion Language Models are Super Data Learners
- Large Language Diffusion Models
- Detecting Pretraining Data from Large Language Models
- Seed Diffusion: A Large-Scale Diffusion Language Model with High-Speed Inference
- On the Importance of Difficulty Calibration in Membership Inference Attacks
- Dream 7B: Diffusion Large Language Models
- BadDLM: Backdooring Diffusion Language Models with Diverse Targets
- Discovering Universal Semantic Triggers for Text-to-Image Synthesis
- Min-K%++: Improved Baseline for Detecting Pre-Training Data from Large Language Models
Related papers
- Exploring Solution Divergence and Its Effect on Large Language Model Problem Solving
- Ishigaki-IDS-Bench: A Benchmark for Generating Information Delivery Specification from BIM Information Requirements
- Subliminal Steering: Stronger Encoding of Hidden Signals
- MedStruct-S: A Benchmark for Key Discovery, Key-Conditioned QA and Semi-Structured Extraction from OCR Clinical Reports
- The End of Transformers? On Challenging Attention and the Rise of Sub-Quadratic Architectures
- Untangling the Mechanisms of Misleading Context in Medical Question Answering