xFUZZ: A Flexible Framework for Fine-Grained, Runtime-Adaptive Fuzzing Strategy Composition

Fuzzing is one of the most efficient techniques for detecting vulnerabilities in software. Existing approaches struggle with performance inconsistencies across different targets and rely on rigid, coarse-grained fuzzing strategy composition, limiting the flexibility to adaptively combine the strengths of different fuzzing strategies at runtime. To address these challenges, we present xFUZZ, a flexible and extensible fuzzing framework supporting fine-grained, runtime-adaptive strategy composition. xFUZZ integrates popular input scheduling and mutation scheduling strategies as fine-grained, independently switchable plugins, allowing users to adaptively replace any plugins throughout the fuzzing campaign. Furthermore, we introduce an adaptive algorithm based on Sliding-Window Thompson Sampling, which dynamically selects the optimal composition of the fuzzing strategy during the fuzzing campaign. Experimental results show that xFUZZ outperforms stateof-the-art fuzzers by achieving a 10.07% increase in unique vulnerability discovery and a 4.94% improvement in code coverage. Notably, xFUZZ is the first to detect 21 out of 37 vulnerabilities in the test suite, establishing its effectiveness across varied targets.