摘要： 导向式灰盒模糊测试是一种能够快速生成测试用例，达到给定程序目标区域并且发现漏洞的模糊测试技术。针对当前导向式模糊测试难以通过魔术字节等检查语句，且对目标区域路径覆盖率较低的问题，提出结合混合符号执行的导向式灰盒模糊测试方法。通过跟踪种子的执行路径，使用约束求解器对种子的遗传变异加以辅助，生成能够通过检查语句的测试用例，从而对目标区域进行有效测试。实验结果表明，该测试方法能够提高导向式模糊测试对目标区域的覆盖率。

关键词: 导向式灰盒模糊测试, 动态能量调控, 混合符号执行, 遗传变异, 约束求解器

Abstract: Directed Gray-box Fuzzing(DGF) test is a kind of fuzzing test technique which can quickly generate test cases to reach a given target area of the program and find vulnerabilities,but the existing DGF technique often fail to pass the checking statements such as magic bytes,and their path coverage of the target area is not high.To address the problems,this paper proposes a DGF technique combining mixed symbolic execution.By tracking the execution path of seeds,the genetic variation of seeds is assisted by the constraint solver to generate test cases that can pass checking statements,so as to test the target area more deeply and effectively.Experimental results show that the proposed test technique can improve the coverage of the target area,and it is of high application value in patch testing and high-risk code area detection.

Key words: Directed Gray-box Fuzzing(DGF) test, dynamic energy regulation, mixed symbolic execution, genetic variation, constraint solver

中图分类号: 

• TP391