RiverFuzzRL - an open-source tool to experiment with reinforcement learning for fuzzing
Ciprian Păduraru, Miruna Gabriela Paduraru, Alin Ştefănescu · 2021
Combining fuzzing techniques and reinforcement learning could be an important direction in software testing. However, there is a gap in support for experimentation in this field, as there are no open-source tools to let academia and industry to perform experiments easily. The purpose of this paper is to fill this gap by introducing a new framework, named RiverFuzzRL, on top of our already mature frame-work for AI-guided fuzzing, River. We provide out-of-the-box implementations for users to choose from or customize for their test target. The work presented here is performed on testing binaries and does not require access to the source code, but it can be easily adapted to other types of software testing as well. We also discuss the challenges faced, opportunities, and factors that are important for performance, as seen in the evaluation.