Found in Translation: A Generative Language Modeling Approach to Memory Access Pattern Attacks
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Presented at USENIX Security 2025 by
Confidential computing environments (CCEs) provide a secure way for privacy-sensitive applications to ensure the confidentiality and integrity of data and computations offloaded to the cloud, relying on a hardware root of trust. However, the cloud provider-controlled Operating System (OS) stack still manages key memory management system services such as paging. Several recent works have demonstrated that these services can leverage side channels, specifically page access patterns, to reconstruct private application data. However, related attacks have primarily targeted applications with simple one-to-one mappings between application-level objects and OS-level pages, which is seldom true for most real-world cloud applications. Moreover, these attacks tend to overlook correlations in access patterns—a common occurrence in most real-world applications—leaving untapped critical side-channel information for improving attack accuracy.