nydailynews.com Domain Breach Exposure Report
Risk Score
Massive event volume or critical assets compromised.
AI Findings Summary
The telemetry indicates a significant exposure event impacting nydailynews.com, with 4196 total events observed between July 2025 and June 2026. The majority of these events, 3849, are classified as historical data breaches, with an additional 347 events identified as active infostealer logs. The timeline shows a notable surge in employee and client-related events in January 2026, with 1134 employee and 141 client events, and a subsequent increase in client events in March 2026 (536) and April 2026 (441). Malware families associated with these events include Redline, Acreed, and LummaC2, commonly used for credential theft. The primary sources of leaked data appear to be combolist sources (2165 events) and database dumps (1684 events), predominantly originating from the United States.
Total Events
Employee Affected Events
Client Affected Events
Free Community Account
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12-Month Events Timeline
Event volume by breach date, employee VS client
Infostealers VS Data Breaches
Live stealer logs VS data breaches
2,032 compromised employee accounts pose an infrastructure risk, while 2,164 leaked client credentials create regulatory liability.
Antivirus Distribution
Security Tools on Infected Endpoints
Malware Families Distribution
Distribution of Active Stealer Strains
Top Login URLs
Top exposed services found in the event results
Infostealer By Geography
Shows the distribution of Infostealer-related credential exposure events across different geographic regions. The location is determined by analyzing the metadata of the infected machines associated with each event.
Country Breakdown
Services Classification Distribution
Blast radius - closer to core = more critical infrastructure, size = credential volume
Operating System Distribution
Distribution of compromised endpoint builds
Leak Repository Classification
Where the exposed records currently reside
Disclaimer: This report includes AI-generated content. AI can make mistakes, so verify important findings independently before taking action.