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An empirical study of security warnings from static application security testing tools

机译:来自静态应用程序安全性测试工具的安全性警告的实证研究

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摘要

The Open Web Application Security Project (OWASP) defines Static Application Security Testing (SAST) tools as those that can help find security vulnerabilities in the source code or compiled code of software. Such tools detect and classify the vulnerability warnings into one of many types (e.g., input validation and representation). It is well known that these tools produce high numbers of false positive warnings. However, what is not known is if specific types of warnings have a higher predisposition to be false positives or not. Therefore, our goal is to investigate the different types of SAST-produced warnings and their evolution over time to determine if one type of warning is more likely to have false positives than others. To achieve our goal, we carry out a large empirical study where we examine 116 large and popular C++ projects using six different state-of-the-art open source and commercial SAST tools that detect security vulnerabilities. In order to track a piece of code that has been tagged with a warning, we use a new state of the art framework called cregit(+) that traces source code lines across different commits. The results demonstrate the potential of using SAST tools as an assessment tool to measure the quality of a product and the possible risks without manually reviewing the warnings. In addition, this work shows that pattern-matching static analysis technique is a very powerful method when combined with other advanced analysis methods. (C) 2019 Elsevier Inc. All rights reserved.
机译:开放Web应用程序安全项目(OWASP)将静态应用程序安全测试(SAST)工具定义为可以帮助在软件的源代码或编译代码中查找安全漏洞的工具。此类工具将漏洞警告检测并分类为多种类型之一(例如,输入验证和表示)。众所周知,这些工具会产生大量的误报。但是,尚不清楚的是,特定类型的警告是否更倾向于误报。因此,我们的目标是调查SAST产生的警告的不同类型及其随时间的演变,以确定一种警告是否比其他警告更容易出现误报。为了实现我们的目标,我们进行了一项大型的实证研究,使用六种不同的可检测安全漏洞的最新开源软件和商业SAST工具检查了116个大型且流行的C ++项目。为了跟踪带有警告标记的一段代码,我们使用了一个称为cregit(+)的最新框架,该框架可跟踪不同提交之间的源代码行。结果证明了使用SAST工具作为评估工具来测量产品质量和可能的风险而无需手动查看警告的潜力。此外,这项工作表明,与其他高级分析方法结合使用时,模式匹配静态分析技术是一种非常强大的方法。 (C)2019 Elsevier Inc.保留所有权利。

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