  Fuzzing is a popular vulnerability automated testing method utilized by
professionals and broader community alike. However, despite its abilities,
fuzzing is a time-consuming, computationally expensive process. This is
problematic for the open source community and smaller developers, as most
people will not have dedicated security professionals and/or knowledge to
perform extensive testing on their own. The goal of this project is to increase
the efficiency of existing fuzzers by allowing fuzzers to explore more paths
and find more bugs in shorter amounts of time, while still remaining operable
on a personal device. To accomplish this, adversarial methods are built on top
of current evolutionary algorithms to generate test cases for further and more
efficient fuzzing. The results of this show that adversarial attacks do in fact
increase outpaces existing fuzzers significantly and, consequently, crashes
found.
