hsbc.co.uk Domain Breach Exposure Report
Risk Score
Massive event volume or critical assets compromised.
AI Findings Summary
The telemetry indicates a significant exposure of client and employee data, with 34,905 client records and 455 employee records compromised over the reporting period. This is primarily driven by 31,222 historical data breaches and 4,138 active infostealer logs, suggesting a high volume of credential stuffing and data exfiltration activities. The most prevalent malware families are Redline, LummaC2, and Rhadamanthys, commonly associated with credential theft. The targeted services include various HSBC security portals and the mobile banking application, indicating a direct assault on customer-facing and internal security infrastructure. The majority of the compromised data originates from combolist sources, highlighting the impact of large-scale credential stuffing attacks against the organization's user base.
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
455 compromised employee accounts pose an infrastructure risk, while 34,905 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.