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Among the many software vulnerability discovery techniques available today,fuzzing has remained highly popular due to its conceptual simplicity, its lowbarrier to deployment, and its vast amount of empirical evidence in discoveringreal-world software vulnerabilities. At a high level, fuzzing refers to aprocess of repeatedly running a program with generated inputs that may besyntactically or semantically malformed. While researchers and practitionersalike have invested a large and diverse effort towards improving fuzzing inrecent years, this surge of work has also made it difficult to gain acomprehensive and coherent view of fuzzing. To help preserve and bringcoherence to the vast literature of fuzzing, this paper presents a unified,general-purpose model of fuzzing together with a taxonomy of the currentfuzzing literature. We methodically explore the design decisions at every stageof our model fuzzer by surveying the related literature and innovations in theart, science, and engineering that make modern-day fuzzers effective.

The Art, Science, and Engineering of Fuzzing: A Survey