DFL: A DOM sample generation oriented fuzzing framework for browser rendering engines

Abstract

The security of web browsers, being fundamental to Internet access infrastructure, has garnered significant attention. Current approaches to identify browser vulnerabilities predominantly rely on code auditing and componentized unit testing. Fuzzing has emerged as an efficient technique for vulnerability discovery. However, adapting this method to browser security testing poses considerable challenges. Recent endeavors in browser vulnerability discovery primarily concentrate on the parsing engine, with limited solutions addressing the rendering engine. Moreover, coverage-guided mutation, a critical aspect, is not prevalent in existing fuzzing frameworks. In this paper, we present a coverage-guided fuzzing framework of DFL, which builds on Freedom and AFL to re-engineer various text generators based on DOM syntax and optimize the efficiency of sample generation. Additionally, serialization and deserialisation methods are developed for the implementation of generator text mutations and the seamless conversion between binary samples and the source DOM tree. When compared with three established DOM fuzzing frameworks in the latest Chromium kernel, DFL has demonstrated an ability to uncover 1.5–3 times more vulnerabilities within a short timeframe. Our research identifies potential avenues for further exploration in browser rendering engine security, specifically focusing on sample generation and path direction.