Effective Fuzzing Framework for the Sleuthkit Tools

The fields of digital forensics and incident response have seen significant growth over the last decade due to the increasing threats faced by organizations and the continued reliance on digital platforms and devices by criminals. In the past, digital investigations were performed manually by expert investigators, but this approach has become no longer viable given the amount of data that must be processed compared to the relatively small number of trained investigators. These resource constraints have led to the development and reliance on automated processing and analysis systems for digital evidence. In this paper, we present our effort to develop a stress testing platform specifically tailored to assess the robustness and reliability of digital forensics tools. For our initial testing, we chose to target The Sleuthkit framework, given its prominence both as a standalone tool as well as a programming library that is utilized by many open source and commercial file system analysis systems. The results of our efforts were the automated discovery of many critical programming errors in the Sleuthkit framework