论文标题
增强的灰色盒子为英特尔媒体驱动程序模糊
Enhanced Grey Box Fuzzing For Intel Media Driver
论文作者
论文摘要
灰色盒子模糊是自动漏洞检测最成功的方法之一。但是，像AFL这样的常规灰色盒构函可以打开对整个输入的构图，并在执行时间较低的较小的种子上花费更多的时间，这对复杂的输入类型产生了严重影响。在这项工作中，我们为英特尔媒体驱动程序介绍了一个智能的灰色盒子模糊盒，Mediafuzzer可以根据复杂输入的选择性领域进行有效的模糊性。此外，由于一个基于呼叫深度的新型功率计划偏向种子语料库，这可能会导致更深的呼叫链，因此，它极大地改善了脆弱性暴露（暴露的问题多6.6倍），并且对基线媒体驱动程序的基线AFL效率（效率高2.7倍）几乎可忽略不计。
Grey box fuzzing is one of the most successful methods for automatic vulnerability detection. However,conventional Grey box Fuzzers like AFL can open perform fuzzing against the whole input and spend more time on smaller seeds with lower execution time, which significantly impact fuzzing efficiency for complicated input types. In this work, we introduce one intelligent grey box fuzzing for Intel Media driver, MediaFuzzer, which can perform effective fuzzing based on selective fields of complicated input. Also, with one novel calling depth-based power schedule biased toward seed corpus which can lead to deeper calling chain, it dramatically improves the vulnerability exposures (~6.6 times more issues exposed) and fuzzing efficiency (~2.7 times more efficient) against the baseline AFL for Intel media driver with almost negligible overhead.
