Snappy: Efficient Fuzzing with Adaptive and Mutable Snapshots
Elia Geretto, Cristiano Giuffrida, Herbert Bos, Erik van der Kouwe · 2022
Modern coverage-oriented fuzzers play a crucial role in vulnerability finding. While much research focuses on improving the core fuzzing techniques, some fundamental speed bottlenecks, such as the redundant computations incurred by re-executing the target for every input, remain. Prior solutions mitigate the impact of redundant computations by instead fuzzing a program snapshot, such as the one placed by a fork server at the program entry point or generalizations for annotated APIs, drivers, networked servers, etc. Such snapshots are static and, as such, cannot adapt to the characteristics of the target and the input, missing opportunities to further reduce redundancy and improve fuzzing speed.