Black-Box Membership Inference via Word-Level Probability Estimation
Membership inference attacks (MIAs) have emerged as critical tools for auditing privacy risks in large language models (LLMs), aiming to determine whether a given text was included in a model's training corpus. However, most existing MIAs require access to per-token logits or probabilities, making them inapplicable in practice to proprietary LLMs that expose only textual continuations. To address this underexplored setting, we propose Word-level Probability MIA (WPMIA), a statistically principled MIA for strict black-box privacy auditing. WPMIA estimates word-level generation probabilities via Monte Carlo sampling with local kernel smoothing, then aggregates these estimates into a sequenc...
| Authors | Shengjie Niu, Yeheng Ge, Jian Huang |
|---|---|
| Primary category | cs.CR |
| Published | 8 September 2026 |
| Last updated at source | 8 September 2026 |
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