Incident Score: Analysis & Impact (OPE1774910406)
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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 OPE1774910406.
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: 9569287, the industry type: Research Services and the number of employees: 6888 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 643 and after the incident was 640 with a difference of -3 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.
OpenAI recently reported "OpenAI Patches ChatGPT Data Leak via DNS Side Channel", a noteworthy cybersecurity incident.
OpenAI addressed a critical vulnerability in ChatGPT that allowed attackers to exfiltrate sensitive data through a DNS side channel.
The disruption is felt across the environment, affecting ChatGPT's code execution environment, and exposing Sensitive data, including personal health information.
In response, moved swiftly to contain the threat with measures like Patch to monitor and block DNS-based exfiltration, and began remediation that includes Strengthened defenses against DNS side-channel attacks, and stakeholders are being briefed through No immediate public response from OpenAI.
The case underscores how Resolved, teams are taking away lessons such as DNS queries must be monitored as a potential exfiltration vector in AI environments; safeguards should account for indirect data transmission methods, and recommending next steps like Implement comprehensive monitoring of all outbound network activity, including DNS queries; enforce stricter validation of third-party GPT apps; enhance user awareness of data exfiltration risks.
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 Supply Chain Compromise: Compromise Software Dependencies and Development Tools (T1195.002) with moderate confidence (50%), supported by evidence indicating single malicious prompt could bypass OpenAI’s safeguards. Under the Exfiltration tactic, the analysis identified Exfiltration Over Alternative Protocol: Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol (T1048.003) with high confidence (90%), supported by evidence indicating exfiltrate sensitive data through a DNS side channel and Exfiltration Over C2 Channel (T1041) with moderate to high confidence (80%), supported by evidence indicating unauthorized data transmission from ChatGPT’s code execution environment. Under the Defense Evasion tactic, the analysis identified Impair Defenses: Disable or Modify Tools (T1562.001) with moderate to high confidence (70%), supported by evidence indicating failed to monitor DNS queries...did not trigger protective measures and Masquerading: Match Legitimate Name or Location (T1036.005) with moderate confidence (60%), supported by evidence indicating third-party GPT app acting as a personal health analyst. Under the Collection tactic, the analysis identified Data from Local System (T1005) with moderate to high confidence (80%), supported by evidence indicating pDF containing lab results and personal data...processed the file. Under the Command and Control tactic, the analysis identified Application Layer Protocol: DNS (T1071.004) with high confidence (90%), supported by evidence indicating dNS side channel...transmitted to an attacker-controlled server. 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/OPE1774910406
- OpenAI CyberSecurity Rating page: https://www.rankiteo.com/company/openai
- OpenAI Rankiteo Cyber Incident Blog Article: https://blog.rankiteo.com/ope1774910406-openai-vulnerability-february-2026/
- OpenAI CyberSecurity Score History: https://www.rankiteo.com/company/openai/history
- OpenAI CyberSecurity Incident Source: https://www.theregister.com/2026/03/30/openai_chatgpt_dns_data_snuggling_flaw/
- 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