.png){kind=icon}
Return
DatAFLow: Toward a Data-flow-guided Fuzzer
.png){kind=icon}
OA
AI
DOI：10.1145/3587159.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This Replicating Computational Report (RCR) describes (a) our datAFLow fuzzer and (b) how to replicate the results in datAFLow: Toward a Data-Flow-Guided Fuzzer. Our primary artifact is the datAFLow fuzzer. Unlike traditional coverage-guided greybox fuzzers-which use control-flow coverage to drive program exploration-datAFLow uses data-flow coverage to drive exploration. This is achieved through a set of LLVM-based analyses and transformations. In addition to datAFLow, we also provide a set of tools, scripts, and patches for (a) statically analyzing data flows in a target program, (b) compiling a target program with the datAFLow instrumentation, (c) evaluating datAFLow on the Magma benchmark suite, and (d) evaluating datAFLow on the DDFuzz dataset. datAFLow is available at https://github.com/HexHive/datAFLow.
Keywords:
Fuzzing
data flow
coverage
Journal
A
IF:
6.2
Papers:
1.2K
Citations:
3.4K
Organization
Cited Papers
ShareSaveShareSaveDawn-dusk difference of periodic oxygen EUV dayglow variations at Venus observed by Hisaki.png)
Icarus
IcarusIF0
PREAI
ShareSaveShareSaveShareSave
View more