@inproceedings{takahashi-ishihara-2026-fast,
title = "Fast-{MIA}: Efficient and Scalable Membership Inference for {LLM}s",
author = "Takahashi, Hiromu and
Ishihara, Shotaro",
editor = "Durrett, Greg and
Jian, Ping",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-demo.9/",
pages = "89--98",
ISBN = "979-8-89176-392-0",
abstract = "We propose Fast-MIA (https://github.com/Nikkei/fast-mia), a Python library for efficiently evaluating membership inference attacks (MIA) against large language models (LLMs).MIA has emerged as a crucial technique for auditing privacy risks and copyright infringement in LLMs. However, computational demands have grown substantially: recent methods rely on repeated inference, while practical auditing requires large-scale evaluation.Progress is further hindered by existing implementations that execute methods independently, redundantly computing shared intermediate results such as log-probabilities.To address these challenges, Fast-MIA combines two strategies: (1) high-throughput batch inference via vLLM, achieving approximately 5$\times$ speedup, and (2) a cross-method caching architecture that computes intermediate results once and shares them across methods.The library includes representative MIA methods under a unified framework, integrates with established benchmarks, and supports flexible YAML configuration.We release Fast-MIA under the Apache License 2.0 to support scalable and reproducible MIA research."
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%0 Conference Proceedings
%T Fast-MIA: Efficient and Scalable Membership Inference for LLMs
%A Takahashi, Hiromu
%A Ishihara, Shotaro
%Y Durrett, Greg
%Y Jian, Ping
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-392-0
%F takahashi-ishihara-2026-fast
%X We propose Fast-MIA (https://github.com/Nikkei/fast-mia), a Python library for efficiently evaluating membership inference attacks (MIA) against large language models (LLMs).MIA has emerged as a crucial technique for auditing privacy risks and copyright infringement in LLMs. However, computational demands have grown substantially: recent methods rely on repeated inference, while practical auditing requires large-scale evaluation.Progress is further hindered by existing implementations that execute methods independently, redundantly computing shared intermediate results such as log-probabilities.To address these challenges, Fast-MIA combines two strategies: (1) high-throughput batch inference via vLLM, achieving approximately 5\times speedup, and (2) a cross-method caching architecture that computes intermediate results once and shares them across methods.The library includes representative MIA methods under a unified framework, integrates with established benchmarks, and supports flexible YAML configuration.We release Fast-MIA under the Apache License 2.0 to support scalable and reproducible MIA research.
%U https://aclanthology.org/2026.acl-demo.9/
%P 89-98
Markdown (Informal)
[Fast-MIA: Efficient and Scalable Membership Inference for LLMs](https://aclanthology.org/2026.acl-demo.9/) (Takahashi & Ishihara, ACL 2026)
ACL
- Hiromu Takahashi and Shotaro Ishihara. 2026. Fast-MIA: Efficient and Scalable Membership Inference for LLMs. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 89–98, San Diego, California, United States. Association for Computational Linguistics.