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DatAFLow

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Date

2023-07-21

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Abstract

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.

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Keywords

Fuzzing, coverage, data flow

Citation

URI

https://dspace-test.anu.edu.au/handle/1885/733719501

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DevOps Pure Testing

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ACM Transactions on Software Engineering and Methodology

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Article

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DOI

10.1145/3587159

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