Automatic In-Memory Fuzzing with the Assistance of Taint Flow Analysis

In-memory fuzzing is a research hotspot in the field of vulnerability mining in recent years, due to the high efficiency and lightweight. However its incompleteness, poor robustness, and low automation, make in-memory fuzzing difficult to be applied in the actual vulnerability discovering. In this paper, we combine the taint analysis with in-memory fuzzing, to solve the above problems. And the experiments show that our method can improve the level of automation and robustness, reduce incompleteness effectively.