Found in Translation: A Generative Language Modeling Approach to Memory Access Pattern Attacks

Abstract

Confidential-computing environments protect data and computation but leave paging under a cloud-controlled operating system, creating a page-access side channel. Earlier attacks largely assume simple mappings between application objects and pages and overlook correlations in access sequences. Found in Translation identifies parallels between page-access patterns and natural-language structure and uses a recurrent encoder-decoder to infer application-level object accesses from page-level sequences. Across AI model serving and semantic-search applications, FIT reconstructs object sequences with 71.7–99.9 percent average accuracy, substantially outperforming prior approaches.

BibTeX

@inproceedings{fit,
  author = {Jia, Grace and Wong, Alex and Khandelwal, Anurag},
  title = {{Found in Translation: A Generative Language Modeling Approach to Memory Access Pattern Attacks}},
  booktitle = {USENIX Security},
  month = aug,
  year = {2025}
}