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Grammar-Based Fuzzing

机译:基于语法的模糊测试

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摘要

This article presents new method for fuzzing programs accepting complex structured data based on BNF grammars. The majority of existing fuzzing methods do not take into account the structure of inputs for target program. Existing BNF structured data generating tools have various restrictions: BNF rules must be specified for target program, they are not scalable, generated data is not fully compatible with BNF rules, etc. We propose new algorithm for BNF structured data generation which uses ANTLR platform's descriptions of BNF rules for more than 120 languages and data formats. Every rule of grammar designed as universal pushdown automata, which allows as automatically generate BNF compatible data. Then we embed it as mutation plugin in fuzzing tool. According to experimental results in some cases we were able significantly increased code coverage.
机译:本文提出了一种新的方法,用于基于BNF语法对程序接受复杂的结构化数据进行模糊处理。现有的大多数模糊测试方法都没有考虑目标程序的输入结构。现有的BNF结构化数据生成工具有多种限制:必须为目标程序指定BNF规则,它们不可扩展,生成的数据与BNF规则不完全兼容,等等。我们提出了使用ANTLR平台描述的BNF结构化数据生成新算法。适用于120多种语言和数据格式的BNF规则。每个语法规则都设计为通用下推式自动机,可自动生成BNF兼容数据。然后我们将其作为突变插件嵌入到模糊测试工具中。根据实验结果,在某些情况下,我们可以大大提高代码覆盖率。

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