700Credit Data Breach Exposes Consumer SSNs and Personal Information
700Credit, a provider of credit and identity verification services for automotive dealerships, experienced a data breach that was reported on December 6, 2025.
Key points
- 700Credit, a credit and identity verification service, had a data breach reported on December 6, 2025.
- The breach, identified in late October 2025, exposed consumer names, addresses, and Social Security numbers.
- Data was linked to auto financing applications submitted between May and October 2025.
Connected intelligence
Signal brief
Follow the organization, likely method and potential impact to explore connected Shadow Tier intelligence.
Organization
- Published
- Dec 6, 2025
- Updated
- Jul 2, 2026
- Confidence
- High
- Evidence
- 2 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 internet-facing systems, credential abuse and exploit activity.
Business impact
- Impact area
- Confidentiality
- Likely asset
- User or customer data
Mentioned entities
Quick context
Questions about this signal
What happened in this signal?
700Credit, a provider of credit and identity verification services for automotive dealerships, experienced a data breach that was reported on December 6, 2025. Internal documents confirmed that the breach, identified in late October 2025, exposed consumer names, addresses, and Social Security numbers tied to auto financing applications submitted between May and October 2025. The incident was caused by unauthorized access to a third-party API, allowing attackers to copy records from the 700Dealer.com application layer.
When was this signal reported?
Shadow Tier lists Dec 6, 2025 as the signal date.
Which organization is connected to this signal?
700credit is the organization connected to this public signal.
Explore 700creditRelated signals
Compare shared topics, actors and incident patterns before opening the full signal.
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