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DatAFLow: Toward a Data-flow-guided Fuzzer

delete2023-07-21
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OA
AI
A
Adrian Herrera *
M
Mathias Payer
A
Antony L. Hosking
DOI:10.1145/3587159delete
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Abstract

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
ACM Transactions on Software Engineering and Methodology
IF:
6.2
Papers:
1.2K
Citations:
3.4K

Organization

A
Australian National University
Scholars:
2.1W
Papers: 2.3W
Citations: 3.9W
S
swiss federal institutes of technology domain
Scholars:
9.0W
Papers: 8.0W
Citations: 163