Year: 2024
Type: conference-paper
Abstract: Fuzzing large and complex programs remains challenging due to difficulties in uncovering deeply hidden vulnerabilities. This paper addresses the limitations of existing coverage-guided fuzzers, focusing on the scheduler and mutator components. We propose an end-to-end online stochastic control formulation for coverage-guided fuzzing. Our approach incorporates a custom scheduler and mutator that can adapt to branch logic, maximizing edge coverage. The scheduler utilizes fine-grained branch distance measures to identify frontier branches, where new coverage is likely to be achieved. The mutator leverages branch distance information to perform efficient and targeted seed mutations, leading to robust progress with minimal overhead. We present FOX, a proof-of-concept implementation of our control-theoretic approach.
Authors: Dongdong She, Adam Storek, Yuchong Xie, Seoyoung Kweon, Prashast Srivastava, Suman Jana
Language: English
Subfield: Software
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: National Science Foundation
Award: G6671297155
OpenAlex ID: W4402391527