A Taint Based Approach for Smart Fuzzing

Abstract

Fuzzing is one of the most popular test-based software vulnerability detection techniques. It consists in running the target application with dedicated inputs in order to exhibit potential failures that could be exploited by a malicious user. In this paper we propose a global approach for fuzzing, addressing the main challenges to be faced in an industrial context: large-size applications, without source code access, and with a partial knowledge of the input specifications. This approach integrates several successive steps, and we mostly focus here on an important one which relies on binary-level dynamic taint analysis. We summarize the main problems to be addressed in this step, and we detail the solution we implemented to solve them.