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

TL;DR

An automated system is presented that detects and analyzes potential sensitive information leaked through publicly accessible URLs, identifying 12,331 potential exposures across authentication, financial, personal, and document-related domains.

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.

Figures (2)

• Figure 1: Leak Detection Workflow Overview
• Figure 2: Distribution of Flagged Leak Categories.