Software fuzzing mutates bytes in the test seeds to explore different behaviors of the program under test. Initial seeds can have great impact on the performance of a fuzzing campaign. Mutating a lot of uninteresting bytes in a large seed wastes the fuzzing resources. In this paper, we present the preliminary results of our approach that aims to improve the performance of fuzzers through identifying and removing uninteresting bytes in the seeds. In particular, we present DIAR, a technique that reduces the size of the seeds based on their coverage. Our preliminary results suggest fuzzing campaigns that start with reduced seeds, find new paths faster, and can produce higher coverage overall.
翻译:测试种子中模糊软件变异字节以探索测试中程序的不同行为。 初始种子可对模糊运动的绩效产生巨大影响。 在大型种子中变异许多不感兴趣的字节会浪费模糊资源。 在本文中, 我们介绍了我们旨在通过识别和清除种子中不感兴趣的字节来改善模糊器性能的方法的初步结果。 特别是, 我们介绍了DIAR, 这是一种根据种子的覆盖面来降低种子大小的技术。 我们的初步结果显示, 以种子减少、寻找新路径更快、可以产生更高覆盖率的模糊运动。