The Silent Spill: Measuring Sensitive Data Leaks Across Public URL Repositories

2026-02-25

AI Summary

This study addresses the lack of systematic quantification of unintentional sensitive information leaks in publicly accessible URL repositories. It presents the first large-scale measurement across multiple open URL platforms, introducing an end-to-end detection framework that integrates multimodal analysis techniques—including lexical filtering, dynamic page rendering, OCR-based text extraction, and sensitive content classification. The analysis of over six million URLs uncovered 12,331 distinct leakage incidents involving authentication credentials, financial data, personally identifiable information, and sensitive documents. These findings reveal pervasive privacy risks in today’s open web ecosystem and provide an empirical foundation for developing effective mitigation and protection mechanisms.

Technology Category

• Natural Language Processing: Information Extraction
• Data Mining & Knowledge Management: Web
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Responsible Web: Measurement, analysis, and circumvention of Web censorship

Abstract

A large number of URLs are made public by various platforms for security analysis, archiving, and paste sharing -- such as VirusTotal, URLScan.io, Hybrid Analysis, the Wayback Machine, and RedHunt. These services may unintentionally expose links containing sensitive information, as reported in some news articles and blog posts. However, no large-scale measurement has quantified the extent of such exposures. We present an automated system that detects and analyzes potential sensitive information leaked through publicly accessible URLs. The system combines lexical URL filtering, dynamic rendering, OCR-based extraction, and content classification to identify potential leaks. We apply it to 6,094,475 URLs collected from public scanning platforms, paste sites, and web archives, identifying 12,331 potential exposures across authentication, financial, personal, and document-related domains. These findings show that sensitive information remains exposed, underscoring the importance of automated detection to identify accidental leaks.

Problem

• sensitive data leaks
• public URL repositories
• data exposure
• accidental leaks
• security analysis

Innovation

• sensitive data leakage
• automated detection
• URL analysis
• OCR-based extraction
• dynamic rendering