TT-XSS: A novel taint tracking based dynamic detection framework for DOM Cross-Site Scripting

• Published in: Journal of parallel and distributed computing Vol. 118; pp. 100 - 106
• Main Authors: Wang, Ran, Xu, Guangquan, Zeng, Xianjiao, Li, Xiaohong, Feng, Zhiyong
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.08.2018
• Subjects: DOM Cross-Site Scripting; Dynamic analysis; Static analysis; Vulnerabilities; DOM Cross-Site Scripting; Vulnerabilities; Dynamic analysis; Static analysis
• ISSN: 0743-7315; 1096-0848
• DOI: 10.1016/j.jpdc.2017.07.006

Most work on DOM Cross-Site Scripting (DOM-XSS) detection methods can be divided into three kinds: black-box fuzzing, static analysis, and dynamic analysis. However, black-box fuzzing and static analysis suffer much from high false negative rates and high false positive rates respectively. Current dynamic analysis is complex and expensive, though it can obtain more efficient results. In this paper, we propose a dynamic detection framework (TT-XSS) for DOM-XSS by means of taint tracking at client side. We rewrite all JavaScript features and DOM APIs to taint the rendering process of browsers. To this end, new data types and methods are presented to extend the semantic description ability of the original data structure, based on which we can analyze the taint traces through tainting all sources, sinks and transfer processes during pages parsing. In this way, attack vectors are derived to verify the vulnerabilities automatically. Compared to AWVS 10.0, our framework detects more 1.8% vulnerabilities, and it can generate the corresponding attack vectors to verify 9.1% vulnerabilities automatically. •A dynamic detection framework for DOM-XSS by taint tracking is proposed.•New data types and methods used in tainting process are advanced.•Attack vectors are derived to verify the vulnerability automatically.