research-panel.jp Domain Breach Exposure Report
Risk Score
Massive event volume or critical assets compromised.
AI Findings Summary
The domain research-panel.jp has experienced a significant number of events, with 7473 total occurrences recorded between July 2025 and June 2026. The majority of these events are classified as historical data breaches (5810) and active infostealer logs (1663). Notably, there were 83 employee-related events and 7390 client-related events. The infostealer activity primarily involved malware families such as Vidar, Rhadamanthys, and Redline, targeting services like the domain's login and registration pages. The data breaches appear to originate from combolist sources, suggesting compromised credentials. The timeline shows a surge in both employee and client events in March and April 2026, indicating a period of heightened compromise. The geographical breakdown shows a strong concentration of events in Japan.
Total Events
Employee Affected Events
Client Affected Events
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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
83 compromised employee accounts pose an infrastructure risk, while 7,390 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.