Graphics Processing Units (GPUs) have evolved from very specialized, idiosyncratic hardware intended to execute specialized graphics workloads to semi-autonomous "supercomputers" that can be programmed easily using common programming languages and powerful, portable APIs. GPUs also form the basis for an emerging threat, GPU malware, which offloads important aspects of malicious computations onto the GPU. The benefits of executing malicious computations on the GPU include abundant compute power, a large amount of semi-non-volatile memory, and perhaps most importantly, isolation from host-based security measures. While memory analysis offers powerful tools to detect and analyze traditional host-based malware, there are essentially no equivalent tools for analyzing GPU malware. Furthermore, existing general-purpose tools for debugging GPU applications are completely ineffective if a large number of conditions are not established before a GPU application is executed, all of which will certainly be violated by weaponized GPU malware. This talk explores GPU malware in detail, identifies why it's hard to analyze, and also discusses measures that can easily employed to make analysis even more difficult. A primary motivation for this research is the 2015 DFRWS Digital Forensics Challenge, under development by Dr. Richard, the aim of which is to increase interest in GPU malware analysis and foster the development of powerful tools to analyze and combat this threat.