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        <title>Black-Box Membership Inference via Word-Level Probability Estimation</title>
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        <namePart type="given">Shengjie</namePart>
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    <abstract>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 sequence-level likelihood estimator. Furthermore, WPMIA constructs the likelihood conditioned on different prefixes, thereby amplifying the distributional differences between members and non-members. We evaluate WPMIA across various open-source LLMs and find that it consistently outperforms existing black-box baselines. Importantly, we also evaluate WPMIA on modern proprietary LLMs, including GPT-5-Chat, Gemini-2.5-Flash, and Claude-4.5-Haiku, achieving an average TPR@5%FPR of 42.0 across these models. These results offer a sound foundation for future research on strict black-box membership inference. Code is available at https://github.com/niusj03/WPMIA.</abstract>
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%0 Conference Proceedings
%T Black-Box Membership Inference via Word-Level Probability Estimation
%A Niu, Shengjie
%A Ge, Yeheng
%A Huang, Jian
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F niu-etal-2026-black
%X 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 sequence-level likelihood estimator. Furthermore, WPMIA constructs the likelihood conditioned on different prefixes, thereby amplifying the distributional differences between members and non-members. We evaluate WPMIA across various open-source LLMs and find that it consistently outperforms existing black-box baselines. Importantly, we also evaluate WPMIA on modern proprietary LLMs, including GPT-5-Chat, Gemini-2.5-Flash, and Claude-4.5-Haiku, achieving an average TPR@5%FPR of 42.0 across these models. These results offer a sound foundation for future research on strict black-box membership inference. Code is available at https://github.com/niusj03/WPMIA.
%U https://aclanthology.org/2026.emnlp-main.446/
%P 10271-10287

Markdown (Informal)

[Black-Box Membership Inference via Word-Level Probability Estimation](https://aclanthology.org/2026.emnlp-main.446/) (Niu et al., EMNLP 2026)

• Black-Box Membership Inference via Word-Level Probability Estimation (Niu et al., EMNLP 2026)

ACL

• Shengjie Niu, Yeheng Ge, and Jian Huang. 2026. Black-Box Membership Inference via Word-Level Probability Estimation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 10271–10287, Budapest, Hungary. Association for Computational Linguistics.