Improving Search Space Analysis of Fuzzing Mutators Using Cryptographic Structures

This paper introduces a novel approach to enhance the performance of software fuzzing mutator tools, by leveraging cryptographic structures known as substitution-permutation networks and Feistel networks. By integrating these structures into the existing HonggFuzz fuzzing library, we propose HonggFuzz+ and demonstrate its effectiveness over other leading fuzzers, such as how the method can uncover bugs and edges earlier due to enhanced search space optimisation. By introducing these two structures, we can diversify memory region relationships that can ultimately improve the performance of HonggFuzz. We demonstrate our approach on a range of common software examples from previous software fuzzing literature. Our results show better or as good performance across a range of software targets when compared to other leading fuzzing techniques. We discuss the relevance of the findings and consider future directions for improving software fuzzing search space analysis.