This paper investigates the practicality of memory attacks on commercial Graphics Processing Units (GPUs). With recent advances in the performance and viability of using GPUs for various highly-parallelised data processing tasks, a number of security challenges are raised. Unscrupulous software running subsequently on the same GPU, either by the same user, or another user, in a multi-user system, may be able to gain access to the contents of the GPU memory. This contains data from previous program executions.

In certain use-cases, where the GPU is used to offload intensive parallel processing such as pattern matching for an intrusion detection system, financial systems, or cryptographic algorithms, it may be possible for the GPU memory to contain privileged data, which would ordinarily be inaccessible to an unprivileged application running on the host computer. With GPUs potentially yielding access to confidential information, existing research in the field is built upon, to investigate the practicality of extracting data from global, shared and texture memory, and retrieving this data for further analysis. These techniques are also implemented on various GPUs using three different Nvidia CUDA versions. A novel methodology for digital forensic examination of GPU memory for remanent data is then proposed, along with some suggestions and considerations towards countermeasures and anti-forensic techniques.