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Medium

Reynella East College Data Dumped Online by Interlock Ransomware Group

Reynella East College, an Australian school, had over 600 gigabytes of its data dumped online by the Interlock ransomware group on June 23, 2026.

Key points

  • Interlock ransomware group dumped over 600 gigabytes of school data online.
  • Over 473,000 files across more than 68,000 folders were extracted.
  • The school had notified parents of a breach on June 9, 2026.

Connected intelligence

Signal brief

Follow the organization, likely method and potential impact to explore connected Shadow Tier intelligence.

02

Likely method

Ransomware

Threat source not confirmed

03

Potential impact

Potential operational disruption

Availability

Published
Jun 23, 2026
Updated
Jun 26, 2026
Confidence
Medium
Evidence
1 source

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

Threat source not confirmed

Watch ransomware, endpoint compromise and business interruption exposure.

Business impact

Potential operational disruption
Impact area
Availability

Mentioned entities

ReynellaecOnlineInterlock Ransomware Group Reynella EastCollegeAustralianInterlockOver

Quick context

Questions about this signal

What happened in this signal?

Reynella East College, an Australian school, had over 600 gigabytes of its data dumped online by the Interlock ransomware group on June 23, 2026. The threat actor claimed to have extracted over 473,000 files across more than 68,000 folders. The school had initially notified parents of a system-wide breach two weeks prior.

When was this signal reported?

Shadow Tier lists Jun 23, 2026 as the signal date.

Which organization is connected to this signal?

Reynellaec is the organization connected to this public signal.

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Which attack pattern is relevant?

This signal is connected to current ransomware incidents based on its reported incident context.

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