Side-channel power analysis of a GPU AES implementation

Graphics Processing Units (GPUs) have been used to run a range of cryptographic algorithms. The main reason to choose a GPU is to accelerate the encryption/decryption speed. Since GPUs are mainly used for graphics rendering, and only recently have they become a fully-programmable parallel computing device, there has been little attention paid to their vulnerability to side-channel attacks. In this paper we present a study of side-channel vulnerability on a state-of-the-art graphics processor. To the best of our knowledge, this is the first work that attempts to extract the secret key of a block cipher implemented to run on a GPU. We present a side-channel power analysis methodology to extract all of the last round key bytes of a CUDA AES (Advanced Encryption Standard) implementation run on an NVIDIA TESLA GPU. We describe how we capture power traces and evaluate the power consumption of a GPU. We then construct an appropriate power model for the GPU. We propose effective methods to sample and process the GPU power traces so that we can recover the secret key of AES. Our results show that parallel computing hardware systems such as a GPU are highly vulnerable targets to power-based side-channel attacks, and need to be hardened against side-channel threats.