Incident Score: Analysis & Impact (OPEOPEBITGOO1782318372)
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 OpenAI's Vulnerability 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 OpenAI 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 OpenAI breach identified under incident ID OPEOPEBITGOO1782318372.
The analysis begins with a detailed overview of OpenAI's information like the linkedin page: https://www.linkedin.com/company/openai, the number of followers: 11173860, the industry type: Research Services and the number of employees: 9859 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 671 and after the incident was 618 with a difference of -53 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 OpenAI and their customers.
A newly reported cybersecurity incident, "Ransomware Attacks Surge in 2025, Driven by Russian-Linked Groups and AI Exploitation", has drawn attention.
In 2025, ransomware attacks claimed on dark-web leak sites jumped nearly 20%, reaching 6,883 incidents, while the number of leak sites grew by a third to 115.
The disruption is felt across the environment, and exposing PII, operational data and supply chain assets.
Formal response steps have not been shared publicly yet.
The case underscores how teams are taking away lessons such as The shrinking window between vulnerability discovery and exploitation demands faster response times. Traditional patching schedules are no longer sufficient in this accelerated threat landscape.
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 to high confidence (80%), with evidence including poorly secured AI tools, and n8n and Open WebUI flaws and Supply Chain Compromise (T1195) with moderate confidence (60%), with evidence including supply chain assets compromised, and domino-effect targets. Under the Execution tactic, the analysis identified Exploitation for Client Execution (T1203) with moderate to high confidence (70%), with evidence including aI exploitation by hackers, and vulnerable AI platforms. Under the Credential Access tactic, the analysis identified Steal Application Access Token (T1528) with moderate confidence (50%), with evidence including poorly secured AI platforms, and aI tools exploited. Under the Collection tactic, the analysis identified Data from Local System (T1005) with moderate to high confidence (80%), supported by evidence indicating pII, operational data, supply chain assets compromised. Under the Exfiltration tactic, the analysis identified Exfiltration Over C2 Channel (T1041) with high confidence (90%), supported by evidence indicating 6,883 ransomware incidents on dark-web leak sites and Exfiltration Over Web Service (T1567) with moderate to high confidence (70%), supported by evidence indicating dark-web leak sites grew by a third to 115. Under the Impact tactic, the analysis identified Data Encrypted for Impact (T1486) with high confidence (90%), supported by evidence indicating ransomware attacks claimed on dark-web leak sites, Defacement (T1491) with moderate confidence (60%), supported by evidence indicating dark-web leak sites used for ransomware claims, and Resource Hijacking (T1496) with moderate confidence (50%), supported by evidence indicating aI tools exploited by hackers for advantage. Under the Defense Evasion tactic, the analysis identified Valid Accounts (T1078) with moderate confidence (60%), supported by evidence indicating poorly secured AI platforms creating new vulnerabilities and Impair Defenses: Disable or Modify Tools (T1562.001) with moderate confidence (50%), supported by evidence indicating aIs dual role in cybersecurity (defenders vs. hackers). These correlations help security teams understand the attack chain and develop appropriate defensive measures based on the observed tactics and techniques.
Sources & References
- OpenAI Rankiteo Cyber Incident Details: https://www.rankiteo.com/company/openai/incident/OPEOPEBITGOO1782318372
- OpenAI CyberSecurity Rating page: https://www.rankiteo.com/company/openai
- OpenAI Rankiteo Cyber Incident Blog Article: https://blog.rankiteo.com/opeopebitgoo1782318372-openai-bitsight-google-open-webui-vulnerability-january-2025/
- OpenAI CyberSecurity Score History: https://www.rankiteo.com/company/openai/history
- OpenAI CyberSecurity Incident Source: https://www.cybersecuritydive.com/news/ransomware-data-breaches-ai-bitsight/823649/
- 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