DatAFLow: Toward a Data-flow-guided Fuzzer: Replicating Computational Report (RCR)

Adrian Herrera; Mathias Payer; Antony L. Hosking

doi:10.1145/3587159

DatAFLow: Toward a Data-flow-guided Fuzzer: Replicating Computational Report (RCR)

Adrian Herrera, Mathias Payer, Antony L. Hosking

School of Computing

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Article number	133
Pages (from-to)	1-7
Journal	ACM Transactions on Software Engineering and Methodology
Volume	32
Issue number	5
DOIs	https://doi.org/10.1145/3587159
Publication status	Published - 21 Jul 2023

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DatAFLow: Toward a Data-flow-guided Fuzzer: Replicating Computational Report (RCR)

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