swiki.jp Domain Breach Exposure Report
Risk Score
Massive event volume or critical assets compromised.
AI Findings Summary
The domain swiki.jp has experienced a significant number of data breach events, totaling 360 over the observed period, with an additional 97 active infostealer logs. The majority of these events are linked to historical data breaches, specifically "Combolist sources," suggesting compromised credential reuse. The timeline indicates a surge in employee and client-related events starting in March 2026, with a peak in April 2026. Malware families like Redline and LummaC2 were detected, often associated with infostealer activity. Targeted services include the main domain and its login/logon pages, indicating potential credential harvesting attempts. The exposure primarily affects clients (349 events) and employees (108 events), with a notable concentration of events originating from the United States.
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
108 compromised employee accounts pose an infrastructure risk, while 349 leaked client credentials create regulatory liability.
Antivirus Distribution
Security Tools on Infected Endpoints
No Data Available
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.