TOFU：面向目标的模糊控制器
TOFU: Target-Oriented FUzzer
摘要：
程序模糊-向计算机程序提供随机构造的输入-已被证明是发现错误、发现安全漏洞和生成增加代码覆盖率的测试输入的强大方法。然而，在许多应用程序中，人们对面向目标的方法感兴趣-人们希望找到一个输入，使程序到达程序中的特定目标点。我们创建了TOFU（面向目标的模糊）来解决定向模糊问题。TOFU的搜索根据距离度量进行偏置，该距离度量根据输入的执行轨迹与目标位置的接近程度对每个输入进行评分。TOFU也是输入结构感知的（即，该搜索利用程序的允许输入的超集的规范）。我们在xmlint上的实验表明，TOFU比AFLGo快28%，同时达到45%以上的目标。此外，距离引导搜索和利用知识的输入结构的贡献显着TOFU的性能。
英文摘要：
Program fuzzing---providing randomly constructed inputs to a computer program---has proved to be a powerful way to uncover bugs, find security vulnerabilities, and generate test inputs that increase code coverage. In many applications, however, one is interested in a target-oriented approach-one wants to find an input that causes the program to reach a specific target point in the program. We have created TOFU (for Target-Oriented FUzzer) to address the directed fuzzing problem. TOFU's search is biased according to a distance metric that scores each input according to how close the input's execution trace gets to the target locations. TOFU is also input-structure aware (i.e., the search makes use of a specification of a superset of the program's allowed inputs). Our experiments on xmllint show that TOFU is 28% faster than AFLGo, while reaching 45% more targets. Moreover, both distance-guided search and exploitation of knowledge of the input structure contribute significantly to TOFU's performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
