Collected research

Towards a Lightweight, Hybrid Approach for Detecting DOM XSS Vulnerabilities with Machine Learning

William Melicher, Clement Fung, Lujo Bauer and Limin Jia · ACM

Trains a neural classifier on JavaScript function tokens to select code for DOM XSS taint tracking. Its hybrid design retains 94.5% of unique confirmed vulnerabilities while modeling a 3.43-fold reduction in taint-tracking cost; the classifier alone has inadequate precision.

Record

Researcher

William Melicher, Clement Fung, Lujo Bauer and Limin Jia

Published by

ACM

Format

Whitepaper

Topic

XSS

In the archive

• Collected in the 2021 research round

Tags

• xss
• dom
• javascript
• dynamic-analysis
• measurement-study
• owasp-a03-2021

This page is the archive's own catalogue record. The research is the work of William Melicher, Clement Fung, Lujo Bauer and Limin Jia, first published at the original source. Preserved copies are kept so the citation survives its host; this one was last captured on 10 September 2026.