Ptfuzz: Guided fuzzing with processor trace feedback

G Zhang, X Zhou, Y Luo, X Wu, E Min - IEEE Access, 2018 - ieeexplore.ieee.org
G Zhang, X Zhou, Y Luo, X Wu, E Min
IEEE Access, 2018ieeexplore.ieee.org
Greybox fuzzing, such as american fuzzy lop (AFL), is very efficient in finding software
vulnerability, which makes it the state-of-the-art fuzzing technology. Greybox fuzzing
leverages the branch information collected during program running as feedback to guide
choosing seeds. Current greybox fuzzing generally uses two kinds of methods to collect
branch information: compile-time instrumentation (AFL) and emulation [AFL extended with
QEMU emulation (QAFL)]. Compile-time instrumentation is efficient, but it does not support …
Greybox fuzzing, such as american fuzzy lop (AFL), is very efficient in finding software vulnerability, which makes it the state-of-the-art fuzzing technology. Greybox fuzzing leverages the branch information collected during program running as feedback to guide choosing seeds. Current greybox fuzzing generally uses two kinds of methods to collect branch information: compile-time instrumentation (AFL) and emulation [AFL extended with QEMU emulation (QAFL)]. Compile-time instrumentation is efficient, but it does not support binary programs. Meanwhile, emulation supports binary programs, but its efficiency is very low. In this paper, we propose a greybox fuzzing approach named PTfuzz, which leverages hardware mechanism (Intel Processor Trace) to collect branch information. Our approach supports binary programs, just like the emulation method, while it gains a comparable performance with the compile-time instrumentation method. Our experiments show that PTfuzz can fuzz the original binary programs without any modification, and we gain a 3× performance improvement compared to QAFL.
ieeexplore.ieee.org
以上显示的是最相近的搜索结果。 查看全部搜索结果

Google学术搜索按钮

example.edu/paper.pdf
搜索
获取 PDF 文件
引用
References