@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://preview.aclanthology.org/ingest-acl/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."
}