bioyun.com Domain Breach Exposure Report
Risk Score
Minimal or historical exposure.
AI Findings Summary
The domain bioyun.com is associated with one historical data breach event, which involved one employee. The breach data originated from "Combolist sources," indicating a potential credential stuffing or account takeover risk. The exposure occurred in September 2025, with no other employee or client-related events detected within the observed period. The risk score of 34 suggests a moderate level of concern. The primary concern is the historical data breach originating from combolists, which could expose employee credentials. This exposure may have occurred in September 2025. Remediation efforts should focus on identifying the compromised employee accounts, enforcing password resets, and implementing multi-factor authentication to mitigate the risk of further account compromise.
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
1 compromised employee accounts pose an infrastructure risk, while 0 leaked client credentials create regulatory liability.
Antivirus Distribution
Security Tools on Infected Endpoints
No Data Available
Malware Families Distribution
Distribution of Active Stealer Strains
No Data Available
Top Login URLs
Top exposed services found in the event results
No Data Available
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
No Data Available
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.