KameleonFuzz: evolutionary fuzzing for black-box XSS detection

Abstract

Fuzz testing consists in automatically generating and sending malicious inputs to an application in order to hopefully trigger a vulnerability. Fuzzing entails such questions as: Where to fuzz? Which parameter to fuzz? Where to observe its effects? In this paper, we specifically address the questions: How to fuzz a parameter? How to observe its effects? To address these questions, we propose KameleonFuzz, a black-box Cross Site Scripting (XSS) fuzzer for web applications. KameleonFuzz can not only generate malicious inputs to exploit XSS, but also detect how close it is revealing a vulnerability. The malicious inputs generation and evolution is achieved with a genetic algorithm, guided by an attack grammar. A double taint inference, up to the browser parse tree, permits to detect precisely whether an exploitation attempt succeeded. Our evaluation demonstrates no false positives and high XSS revealing capabilities: KameleonFuzz detects several vulnerabilities missed by other black-box scanners.