A Defensive Framework for Reflected XSS in Client-Side Applications


  • Khulud Fisal Alenzi Department of Information Technology, University of Tabuk, Kingdom of Saudi Arabia
  • Onytra Abbas Bashir Abbas Department of Computer Science, University of Tabuk, Kingdom of Saudi Arabia https://orcid.org/0000-0001-9272-6041




Cross-site scripting, XSS, XSS filters, filtering rules, XSSFilter


Cross-site scripting attack (XSS) is a common vulnerability that is exploited in modern web applications by entering advanced HTML tags and Java Script functions. An attacker could potentially use this vulnerability to steal users’ sensitive information, hijack user sessions or rewrite whole website contents displaying fake login forms. This class of attacks affects the client-side of a web application and is a critical vulnerability that is difficult to both detect and remediate for websites, often leading to insufficient server-side protection, which is why the end-users need an extra layer of protection at the client-side. In this paper, we analyze the best-known client-side XSS filters, study their mechanisms, structures and mentioned the advantages and disadvantages of each filter. This paper presents a novel XSS filtering model based on filtering rules, XSSFilter, uses Regular Expression in Xpath to detect reflected content, which makes it more robust for web sites that employ custom input sanitizations. We provide a detailed experimental evaluation to compare the four filters with respect to their usability and protection.


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Author Biographies

Khulud Fisal Alenzi, Department of Information Technology, University of Tabuk, Kingdom of Saudi Arabia

Khulud Fisal Alenzi has received her BS and MSc degrees from University of Tabuk in 2015, 2020 respectively, College of Computers and Information Technology. Her major research interests include Cyber Security and Web Applications.

Onytra Abbas Bashir Abbas, Department of Computer Science, University of Tabuk, Kingdom of Saudi Arabia

Onytra Abbas Bashir Abbas received her PhD in AI (text summarization and caching in mobile web application) from Sudan University of Science and Technology, Sudan (SUST) 2012. She is currently Assistance Professor in the College of Computers and Information Technology, University of Tabuk. Her major research interests include Cyber Security, Machine Learning and Web applications.


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How to Cite

Alenzi, K. F. ., & Abbas, O. A. B. . (2022). A Defensive Framework for Reflected XSS in Client-Side Applications. Journal of Web Engineering, 21(07), 2209–2230. https://doi.org/10.13052/jwe1540-9589.2179



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