标题： BandFuzz：一种基于机器学习的协作模糊测试框架 显示英文标题

标题： BandFuzz: An ML-powered Collaborative Fuzzing Framework

摘要： 协作模糊测试结合多个单独的模糊测试工具，并动态选择适用于不同程序的适当组合。 与依赖特定假设的单个模糊测试工具不同，协作模糊测试放松了对目标程序的假设，在各种程序上提供了稳健的性能。 然而，现有的协作模糊测试框架面临包括额外计算资源需求和模糊测试工具之间低效资源分配在内的挑战。 为解决这些挑战，我们提出了 BANDFUZZ，一个由机器学习驱动的协作模糊测试框架，在不需要额外计算资源的情况下优于单个模糊测试工具。 BANDFUZZ 的关键贡献在于其由我们提出的多臂老虎机模型驱动的创新资源分配算法。 与现有框架中的贪心方法不同，BANDFUZZ 模型化了单个模糊测试工具的长期影响，从而能够发现全局最优的协作策略。 我们提出了一种新颖的模糊测试工具评估方法，不仅评估代码覆盖率，还评估模糊测试工具解决困难分支的能力。 最后，我们集成了实时种子同步机制和实现优化，以提高模糊测试的效率和稳定性。 通过在 Fuzzbench 和 Fuzzer Test Suite 上的大量实验，我们表明 BANDFUZZ 超过了最先进的协作模糊测试框架 autofz 和广泛使用的单个模糊测试工具。 我们通过全面的消融研究验证了 BANDFUZZ 的关键设计。 值得注意的是，我们通过分析全球模糊测试竞赛的结果，展示了 BANDFUZZ 在现实世界漏洞检测中的有效性，其中 BANDFUZZ 获得了第一名。 显示英文摘要

摘要： Collaborative fuzzing combines multiple individual fuzzers and dynamically chooses appropriate combinations for different programs. Unlike individual fuzzers that rely on specific assumptions, collaborative fuzzing relaxes assumptions on target programs, providing robust performance across various programs. However, existing collaborative fuzzing frameworks face challenges including additional computational resource requirements and inefficient resource allocation among fuzzers. To tackle these challenges, we present BANDFUZZ, an ML-powered collaborative fuzzing framework that outperforms individual fuzzers without requiring additional computational resources. The key contribution of BANDFUZZ lies in its novel resource allocation algorithm driven by our proposed multi-armed bandits model. Different from greedy methods in existing frameworks, BANDFUZZ models the long-term impact of individual fuzzers, enabling discovery of globally optimal collaborative strategies. We propose a novel fuzzer evaluation method that assesses not only code coverage but also the fuzzer's capability of solving difficult branches. Finally, we integrate a real-time seed synchronization mechanism and implementation-wise optimizations to improve fuzzing efficiency and stability. Through extensive experiments on Fuzzbench and Fuzzer Test Suite, we show that BANDFUZZ outperforms state-of-the-art collaborative fuzzing framework autofz and widely used individual fuzzers. We verify BANDFUZZ's key designs through comprehensive ablation study. Notably, we demonstrate BANDFUZZ's effectiveness in real-world bug detection by analyzing results of a worldwide fuzzing competition, where BANDFUZZ won first place.