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Rare Path Guided Fuzzing.

ISSTA 2023(2023)

University of California at Santa Barbara

Cited 2|Views32
Abstract
Starting with a random initial seed, fuzzers search for inputs that trigger bugs or vulnerabilities. However, fuzzers often fail to generate inputs for program paths guarded by restrictive branch conditions. In this paper, we show that by first identifying rare-paths in programs (i.e., program paths with path constraints that are unlikely to be satisfied by random input generation), and then, generating inputs/seeds that trigger rare-paths, one can improve the coverage of fuzzing tools. In particular, we present techniques 1) that identify rare paths using quantitative symbolic analysis, and 2) generate inputs that can explore these rare paths using path-guided concolic execution. We provide these inputs as initial seed sets to three state of the art fuzzers. Our experimental evaluation on a set of programs shows that the fuzzers achieve better coverage with the rare-path based seed set compared to a random initial seed.
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Key words
Fuzz testing,Control flow analysis,Model counting,Probabilistic analysis,Concolic execution
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Chat Paper

要点】:本文通过识别程序中的稀有路径,并生成能够触发这些稀有路径的输入/种子,来改善模糊测试工具的覆盖率。

方法】:使用定量符号分析识别稀有路径,使用路径引导的共同执行生成能够探索这些稀有路径的输入。

实验】:我们对一组程序进行实验评估,结果表明与随机初始种子相比,使用基于稀有路径的种子集,模糊测试工具实现了更好的覆盖率。