Incident Score: Analysis & Impact (UNI1773102677)
The details regarding individual company incidents & reports gives you full view from every side.
Rankiteo Score Impact Analysis
Key Highlights From The Incident Analysis
- Timeline of Universal Lenders LLC's Breach and lateral movement inside company's environment.
- Overview of affected data sets, including SSNs and PHI, and why they materially increase incident severity.
- How Rankiteo’s incident engine converts technical details into a normalized incident score.
- How this cyber incident impacts Universal Lenders LLC Rankiteo cyber scoring and cyber rating.
- Rankiteo’s MITRE ATT&CK correlation analysis for this incident, with associated confidence level.
Full Incident Analysis Transcript
In this Rankiteo incident briefing, we review the Universal Lenders LLC breach identified under incident ID UNI1773102677.
The analysis begins with a detailed overview of Universal Lenders LLC's information like the linkedin page: https://www.linkedin.com/company/universallenders, the number of followers: 14, the industry type: Consumer Services and the number of employees: 1 employees
After the initial compromise, the video explains how Rankiteo's incident engine converts technical details into a normalized incident score. The incident score before the incident was 774 and after the incident was 643 with a difference of -131 which is could be a good indicator of the severity and impact of the incident.
In the next step of the video, we will analyze in more details the incident and the impact it had on Universal Lenders LLC and their customers.
On 01 November 2024, Universal Lenders LLC disclosed Data Breach issues under the banner "Universal Lenders Data Breach Settlement".
Universal Lenders LLC reached a class action settlement following a November 2024 cyberattack that exposed sensitive personal data of approximately 19,575 current and former customers.
The disruption is felt across the environment, and exposing Names, Social Security numbers, financial account details, driver’s license numbers, with nearly 19,575 records at risk, plus an estimated financial loss of $200,000 (attorneys' fees) + $1,500 (service awards) + settlement payouts.
In response, and stakeholders are being briefed through Mailed notices to affected customers, settlement website for claims.
The case underscores how Settled (class action), with advisories going out to stakeholders covering Mailed notices to affected customers with settlement details and claim instructions.
Finally, we try to match the incident with the MITRE ATT&CK framework to see if there is any correlation between the incident and the MITRE ATT&CK framework.
The MITRE ATT&CK framework is a knowledge base of techniques and sub-techniques that are used to describe the tactics and procedures of cyber adversaries. It is a powerful tool for understanding the threat landscape and for developing effective defense strategies.
MITRE ATT&CK® Correlation Analysis
Rankiteo's analysis has identified several MITRE ATT&CK tactics and techniques associated with this incident, each with varying levels of confidence based on available evidence. Under the Initial Access tactic, the analysis identified Exploit Public-Facing Application (T1190) with moderate confidence (50%), supported by evidence indicating cyberattack that exposed sensitive personal data of ~19,575 customers and Valid Accounts (T1078) with moderate confidence (60%), supported by evidence indicating alleged failure to adequately safeguard customer data (implies weak access controls). Under the Credential Access tactic, the analysis identified Unsecured Credentials: Credentials In Files (T1552.001) with moderate to high confidence (70%), supported by evidence indicating financial account details and SSNs compromised (suggests stored credentials exposed) and Credentials from Password Stores (T1555) with moderate confidence (60%), supported by evidence indicating driver’s license numbers and financial data accessed (implies credential stores breached). Under the Collection tactic, the analysis identified Data from Local System (T1005) with moderate to high confidence (80%), supported by evidence indicating names, SSNs, financial account details, driver’s license numbers compromised and Data from Information Repositories (T1213) with moderate to high confidence (70%), supported by evidence indicating 19,575 customer records exposed (suggests database or file system access). Under the Exfiltration tactic, the analysis identified Exfiltration Over C2 Channel (T1041) with moderate to high confidence (70%), supported by evidence indicating sensitive personal data of ~19,575 customers exposed (implies data exfiltrated) and Transfer Data to Cloud Account (T1537) with lower confidence (40%), supported by evidence indicating no details on exfiltration method, but cloud storage possible. Under the Impact tactic, the analysis identified Data Destruction (T1485) with lower confidence (30%), supported by evidence indicating no evidence of destruction, but high-impact breach implies potential disruption and Data Manipulation: Transmitted Data Manipulation (T1565.002) with lower confidence (40%), supported by evidence indicating high identity theft risk (SSNs/financial data exposed). These correlations help security teams understand the attack chain and develop appropriate defensive measures based on the observed tactics and techniques.
Sources & References
- Universal Lenders LLC Rankiteo Cyber Incident Details: https://www.rankiteo.com/company/universallenders/incident/UNI1773102677
- Universal Lenders LLC CyberSecurity Rating page: https://www.rankiteo.com/company/universallenders
- Universal Lenders LLC Rankiteo Cyber Incident Blog Article: https://blog.rankiteo.com/uni1773102677-universal-lenders-llc-breach-march-2026/
- Universal Lenders LLC CyberSecurity Score History: https://www.rankiteo.com/company/universallenders/history
- Universal Lenders LLC CyberSecurity Incident Source: https://www.claimdepot.com/settlements/ul-data-settlement
- Rankiteo A.I CyberSecurity Rating methodology: https://www.rankiteo.com/Images/rankiteo_algo.pdf
- Rankiteo TPRM Scoring methodology: https://static.rankiteo.com/model/rankiteo_tprm_methodology.pdf