Incident Score: Analysis & Impact (WEGTAR1778027645)
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Rankiteo Score Impact Analysis
Key Highlights From The Incident Analysis
- Timeline of Target'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 Target 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 Target breach identified under incident ID WEGTAR1778027645.
The analysis begins with a detailed overview of Target's information like the linkedin page: https://www.linkedin.com/company/target, the number of followers: 2409265, the industry type: Retail and the number of employees: 173307 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 735 and after the incident was 697 with a difference of -38 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 Target and their customers.
Australian bars and clubs (2024 breach) recently reported "Facial Recognition Data Breaches Pose Permanent Identity Risks", a noteworthy cybersecurity incident.
Facial recognition technology is increasingly embedded in daily life, scanning individuals in public spaces without their knowledge.
The disruption is felt across the environment, affecting Facial recognition databases, Cloud-based biometric systems, Third-party vendor systems, and exposing Facial recognition templates, Personally identifiable information (PII), Surveillance footage matches.
Formal response steps have not been shared publicly yet.
The case underscores how teams are taking away lessons such as Biometric data, particularly facial recognition templates, poses permanent risks if compromised due to its non-resettable nature. Organizations must implement robust safeguards, including encryption, decentralized storage, and strict third-party vendor oversight. Legal protections for biometric data are inconsistent, and individuals have limited recourse once their data is exposed, and recommending next steps like Implement decentralized or device-level biometric storage to reduce centralized breach risks, Enforce strict encryption standards for biometric data at rest and in transit and Conduct regular security audits of third-party vendors handling biometric data.
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 Trusted Relationship (T1199) with high confidence (90%), supported by evidence indicating u.S. Customs and Border Protection’s biometric data was exposed via a subcontractor breach and Exploit Public-Facing Application (T1190) with moderate to high confidence (70%), supported by evidence indicating cloud-based biometric systems remain vulnerable to large-scale attacks. Under the Credential Access tactic, the analysis identified Unsecured Credentials: Credentials In Files (T1552.001) with moderate to high confidence (80%), supported by evidence indicating centralized biometric databases, Lack of robust safeguards, Third-party vendor vulnerabilities and Adversary-in-the-Middle (T1557) with moderate confidence (60%), supported by evidence indicating facial recognition can capture individuals from a distance in public spaces. Under the Collection tactic, the analysis identified Data from Local System (T1005) with high confidence (90%), supported by evidence indicating facial recognition templates, Personally identifiable information (PII) compromised and Archive Collected Data: Archive via Utility (T1560.001) with moderate to high confidence (70%), supported by evidence indicating stolen templates can be matched against surveillance footage or online photos. Under the Exfiltration tactic, the analysis identified Exfiltration Over C2 Channel (T1041) with moderate to high confidence (80%), supported by evidence indicating facial recognition database used by Australian bars and clubs was hacked and Exfiltration Over Web Service: Exfiltration to Cloud Storage (T1567.002) with moderate to high confidence (70%), supported by evidence indicating cloud-based biometric systems remain vulnerable to large-scale attacks. Under the Impact tactic, the analysis identified Data Destruction (T1485) with moderate confidence (50%), supported by evidence indicating biometric data cannot be reset if compromised, creating lifelong vulnerability and Data Manipulation: Stored Data Manipulation (T1565.001) with moderate confidence (60%), supported by evidence indicating stolen templates can unlock access to bank accounts, secure facilities. Under the Defense Evasion tactic, the analysis identified Hide Artifacts: Email Hiding Rules (T1564.008) with lower confidence (40%), supported by evidence indicating facial recognition scanning often occurs without individuals knowledge and Valid Accounts (T1078) with moderate to high confidence (70%), supported by evidence indicating third-party vendor vulnerabilities exploited for access to biometric data. These correlations help security teams understand the attack chain and develop appropriate defensive measures based on the observed tactics and techniques.
Sources & References
- Target Rankiteo Cyber Incident Details: https://www.rankiteo.com/company/target/incident/WEGTAR1778027645
- Target CyberSecurity Rating page: https://www.rankiteo.com/company/target
- Target Rankiteo Cyber Incident Blog Article: https://blog.rankiteo.com/wegtar1778027645-wegmans-target-breach-january-2019/
- Target CyberSecurity Score History: https://www.rankiteo.com/company/target/history
- Target CyberSecurity Incident Source: https://japantoday.com/category/tech/facial-recognition-data-is-a-key-to-your-identity-%E2%80%93-if-stolen-you-can%E2%80%99t-just-change-the-locks
- 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