Reinforcement Learning-Based Collaborative Fuzzing

Collaborative fuzzing is an approach that addresses the limitations of a single fuzzer by combining the complementary strengths of different fuzzers. However, existing collaborative fuzzing has primarily focused on improving general exploration performance, such as increasing code coverage and maximizing overall bug detection. As a result, it does not sufficiently capture target-oriented requirements, such as rapidly reaching specific target sites or reproducing vulnerabilities. When extending collaborative fuzzing to directed fuzzing scenarios, additional challenges arise. Different fuzzers employ different distance metrics and seed prioritization criteria, making it difficult to compare their performance under a unified standard. Moreover, the effectiveness of each fuzzer may vary depending on the current exploration state, making it challenging to determine which fuzzer should be prioritized during execution. To address these limitations, this dissertation proposes a framework that preserves the strengths of collaborative fuzzing while supporting target-oriented objectives. Specifically, it introduces a common evaluation criterion to enable consistent comparison of fuzzer performance, along with an adaptive collaboration mechanism that dynamically selects the most suitable fuzzer based on the current exploration state. The ultimate goal is to improve both exploration efficiency and vulnerability reproduction performance.