Trident: A Hybrid Correlation-Collision GPU Cache Timing Attack for AES Key Recovery

Abstract

Given the parallel processing capabilities of Graphics Processing Units (GPUs), many applications are exploiting GPUs and cryptographic systems have also begun to leverage GPUs to accelerate encryption/decryption. Recent work has identified how microarchitectural side-channel attacks can be carried out on AES (Advanced Encryption Standard) by exploiting the SIMT characteristics and memory coalescing of GPUs. In this work, we first show that previously proposed correlation-based side-channel attacks are not feasible on modern GPUs that support narrower data-cache accesses via a sectored-cache microarchitecture-resulting in memory accesses from different levels of the memory hierarchy. In comparison, we identify how negative timing correlation can occur in modern GPUs when data is fetched from different levels of the cache hierarchy. We then propose Trident - a hybrid cache-collision timing attack on GPUs that can fully recover all AES key bytes on modern GPUs. Cache collisions in GPUs present challenges due to the large number of threads and the number of samples required. To address these challenges, Trident consists of three different components - negative timing correlation, cache-collision attack, and chosen plaintext attack. We leverage the negative timing correlation to recover earlier key bytes of AES while exploiting cache-collision attacks for the latter AES key bytes. To enable GPU cache collision attacks, we exploit memory coalescing to control the number of memory accesses through chosen-plaintext attacks to significantly reduce the number of timing samples needed. Our proposed Trident attack results in over 10× reduction in the number of samples needed to recover the key bytes compared with prior work, while still being successful in full AES key recovery in modern GPUs. We also propose TridentShield - a latency-based countermeasure to the Trident attack that minimizes throughput degradation in GPUs.