女武神:通过确定性技术提高模糊测试性能

Yuyang Rong, Chibin Zhang, Jianzhong Liu, Hao Chen
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引用次数: 0

摘要

灰盒模糊测试因其在许多程序中发现错误的成功而受到了开发人员和研究人员的广泛关注。然而,随机算法限制了模糊器的有效性。首先,基于随机边缘ID的分支覆盖反馈可能导致分支碰撞。此外,最先进的模糊探测器严重依赖随机方法来达到新的覆盖范围。即使是带有解算器的模糊玩家也依赖于不正确的假设,这限制了他们解决分支的能力,并迫使他们将随机性作为最后的手段。我们相信确定性技术提供一致的、可预测的、可重复的结果。我们提出了Valkyrie,一个灰盒模糊器,它的性能主要是通过确定性技术提高的。Valkyrie将无冲突分支覆盖与上下文敏感性相结合,以保持准确性,同时引入工具删除算法以减少开销。它还开创了一种新的突变方法,补偿步进,允许使用求解器的模糊器适应现实世界的模糊场景,而不使用随机性。我们在标准基准Magma和各种现实世界的程序上实施和评估Valkyrie的有效性。瓦尔基里触发了21个唯一整数和内存错误,分别比afl++和安哥拉多10.5%和50%。Valkyrie在10次试验中几乎没有变化,并且最快触发一半的bug。与afl++和安哥拉相比,Valkyrie在实际程序中的分支分别增加了8.2%和12.4%。我们还验证了我们的分支计数和突变方法比最先进的方法更好,这表明确定性技术在一致性、可预测性、可重复性和性能方面优于随机技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Valkyrie: Improving Fuzzing Performance Through Deterministic Techniques
Greybox fuzzing has received much attention from developers and researchers due to its success in discovering bugs within many programs. However, randomized algorithms have limited fuzzers’ effectiveness. First, branch coverage feedback that is based on random edge ID can lead to branch collision. Besides, state-of-the-art fuzzers heavily rely on randomized methods to reach new coverage. Even fuzzers with a solver rely on incorrect assumptions, limiting their ability to solve branches and forcing them to turn to randomness as a last resort.We believe deterministic techniques deliver consistent, predictable, reproducible results. We propose Valkyrie, a greybox fuzzer whose performance is boosted primarily by deterministic techniques. Valkyrie combines collision-free branch coverage with context sensitivity to maintain accuracy while introducing an instrumentation removal algorithm to reduce overhead. It also pioneers a new mutation method, compensated step, allowing fuzzers that use solvers to adapt to real-world fuzzing scenarios without using randomness. We implement and evaluate Valkyrie’s effectiveness on the standard benchmark Magma, and a wide variety of real-world programs. Valkyrie triggered 21 unique integer and memory errors, 10.5% and 50% more than AFL++ and Angora, respectively. Valkyrie shows little to no variance across ten trials and is the fastest to trigger half of the bugs. Valkyrie reached 8.2% and 12.4% more branches in real-world programs, compared with AFL++ and Angora, respectively. We also verify that our branch counting and mutation method is better than the state-of-the-art, which shows that deterministic techniques trump random techniques in consistency, predictability, reproducibility, and performance.
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