DynamiQ: Unlocking the Potential of Dynamic Task Allocation in Parallel Fuzzing

Abstract

We present DynamiQ, a full-fledged and optimized successor to AFLTeam that supports dynamic and adaptive parallel fuzzing. Unlike most existing approaches that treat individual seeds as tasks, DynamiQ leverages structural information from the program's call graph to define tasks and continuously refines task allocation using runtime feedback. This design significantly reduces redundant exploration and enhances fuzzing efficiency at scale. Built on top of the state-of-the-art LibAFL framework, DynamiQ incorporates several practical optimizations in both task allocation and task-aware fuzzing. We evaluate DynamiQ on eighteen targets from Magma and FuzzBench with a total budget of over 40 CPU years. It achieves the highest mean branch coverage on fourteen targets, with average relative gains ranging from 8.37% to 19.53% over five baseline fuzzers. Across our experiments, DynamiQ identifies nine previously unknown bugs.