Introduction && Credit to Gamozolabs For a long time I’ve wanted to develop a fuzzer on the blog during my weekends and freetime, but for one reason or another, I could never really conceptualize a project that would be not only worthwhile as an educational tool, but also offer some utility to the fuzzing community in general. Recently, for Linux Kernel exploitation reasons, I’ve been very interested in Nyx. Nyx is a KVM-based hypervisor fuzzer that you can use to snapshot fuzz traditionally hard to fuzz targets. A lot of the time (most of the time?), we want to fuzz things that don’t naturally lend themselves well to traditional fuzzing approaches. When faced with target complexity in fuzzing (leaving input generation and nuance aside for now), there have generally been two approaches.

Introduction && Credit to Gamozolabs For a long time I’ve wanted to develop a fuzzer on the blog during my weekends and freetime, but for one reason or another, I could never really conceptualize a project that would be not only worthwhile as an educational tool, but also offer some utility to the fuzzing community in general. Recently, for Linux Kernel exploitation reasons, I’ve been very interested in Nyx. Nyx is a KVM-based hypervisor fuzzer that you can use to snapshot fuzz traditionally hard to fuzz targets. A lot of the time (most of the time?), we want to fuzz things that don’t naturally lend themselves well to traditional fuzzing approaches. When faced with target complexity in fuzzing (leaving input generation and nuance aside for now), there have generally been two approaches.