@inproceedings{chang-etal-2025-context,
    title = "Context-Aware Membership Inference Attacks against Pre-trained Large Language Models",
    author = "Chang, Hongyan  and
      Shahin Shamsabadi, Ali  and
      Katevas, Kleomenis  and
      Haddadi, Hamed  and
      Shokri, Reza",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.370/",
    pages = "7299--7321",
    ISBN = "979-8-89176-332-6",
    abstract = "Membership Inference Attacks (MIAs) on pre-trained Large Language Models (LLMs) aim at determining if a data point was part of the model{'}s training set. Prior MIAs that are built for classification models fail at LLMs, due to ignoring the generative nature of LLMs across token sequences. In this paper, we present a novel attack on pre-trained LLMs that adapts MIA statistical tests to the perplexity dynamics of subsequences within a data point. Our method significantly outperforms prior approaches, revealing context-dependent memorization patterns in pre-trained LLMs."
}