Udemy Faces Data Breach Claim by ShinyHunters, 1.4 Million Records Allegedly Compromised
The cybercriminal group ShinyHunters posted a 'Pay or Leak' warning on their data leak site on April 24, 2026, claiming to have compromised over 1.4 million records from Udemy.
Key points
- ShinyHunters claimed compromise of over 1.4 million records.
- PII and internal corporate data allegedly stolen.
- 'Pay or Leak' warning posted on April 24, 2026, with a deadline of April 27, 2026.
Connected intelligence
Signal brief
Follow the organization, likely method and potential impact to explore connected Shadow Tier intelligence.
Organization
- Published
- Apr 24, 2026
- Updated
- Jul 3, 2026
- Confidence
- High
- Evidence
- 4 sources
Structured assessment
Signal analysis
This analysis groups the signal by industry, likely incident action and impacted security area. It helps compare this signal with other published signals without treating the labels as final determinations.
Threat source
Watch process controls, misconfiguration and accidental disclosure paths.
Business impact
- Impact area
- Confidentiality
- Likely asset
- User or customer data
Mentioned entities
Quick context
Questions about this signal
What happened in this signal?
The cybercriminal group ShinyHunters posted a 'Pay or Leak' warning on their data leak site on April 24, 2026, claiming to have compromised over 1.4 million records from Udemy. The alleged stolen data includes Personally Identifiable Information (PII) and internal corporate data. As of April 24, 2026, Udemy had not issued an official statement confirming or denying the breach.
When was this signal reported?
Shadow Tier lists Apr 24, 2026 as the signal date.
Which organization is connected to this signal?
Udemy is the organization connected to this public signal.
Explore UdemyWhich impact area is relevant?
This signal is connected to data exposure and breach intelligence based on its reported consequences.
Explore data exposure and breach intelligenceRelated signals
Compare shared topics, actors and incident patterns before opening the full signal.
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