BandFuzz: An ML-powered Collaborative Fuzzing Framework

Abstract: Collaborative fuzzing has recently emerged as a technique that combines multiple individual fuzzers and dynamically chooses the appropriate combinations suited for different programs. Unlike individual fuzzers, which rely on specific assumptions to maintain their effectiveness, collaborative fuzzing relaxes the assumptions on target programs, providing constant and robust performance across various programs. Ideally, collaborative fuzzing should be a more promising direction toward generic fuzzing solutions, as it mitigates the need for manual cherry-picking of individual fuzzers. However, the effectiveness of existing collaborative fuzzing frameworks is limited by major challenges, such as the need for additional computational resources compared to individual fuzzers and the inefficient allocation of resources among the various fuzzers.