A Targeted Assessment of Cross-Site Scripting Detection Tools

Bruno Pala;Lorenzo Pisu;Silvia Lucia Sanna;Davide Maiorca;Giorgio Giacinto

2023-01-01

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Abstract

Cross-Site Scripting (XSS) attacks are among the most exploited vulnerabilities in web applications. As a countermeasure, various open-source XSS detectors have been released over the years, but none of such tools has been significantly tested to verify their effectiveness. In this paper, we propose an assessment of five of the most employed XSS detectors in the wild. The purpose of this analysis is two-folded: (i) to understand their efficacy in well-known and customized vulnerable environments; (ii) to provide a better comprehension of their detection mechanisms. We performed our evaluation by testing the detectors against one publicly available test bench. Additionally, we created two customized test benches that contain less trivial XSS vulnerabilities. The attained results show how, while most detectors show good accuracy at detecting trivial XSS vulnerabilities, they could fail as the XSS complexity increases.