Context-Aware Membership Inference Attacks against Pre-trained Large Language Models
<|reference_start|>Context-Aware Membership Inference Attacks against Pre-trained Large Language Models: Prior Membership Inference Attacks (MIAs) on pre-trained Large Language Models (LLMs), adapted from classification model attacks, fail due to ignoring the generative process of LLMs across token sequences. In this paper, we present a novel attack that adapts MIA statistical tests to the perplexity dynamics of subsequences within a data point. Our method significantly outperforms prior loss-based approaches, revealing context-dependent memorization patterns in pre-trained LLMs.<|reference_end|>
@article{chang2024context-aware,
    title={Context-Aware Membership Inference Attacks against Pre-trained Large
  Language Models},
    author={Hongyan Chang, Ali Shahin Shamsabadi, Kleomenis Katevas, Hamed
  Haddadi, Reza Shokri},
    journal={arXiv preprint arXiv:2409.13745},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.13745},
    primaryClass={cs.CL cs.AI cs.CR cs.LG stat.ML}
}