Open AccessEnhanced Grey Box Fuzzing for Intel Media Driver
Author(s) -
Linlin Zhang,
Na Luo
Publication year - 2022
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5121/csit.2022.120609
Subject(s) - fuzz testing , computer science , vulnerability (computing) , schedule , overhead (engineering) , operating system , computer security , software
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.