Incident Score: Analysis & Impact (OPE1775256197)
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Rankiteo Score Impact Analysis
Key Highlights From The Incident Analysis
- Timeline of OpenAI's Cyber Attack 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 OPE1775256197.
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 594 and after the incident was 570 with a difference of -24 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.
On 31 March 2024, Mercor disclosed Data Breach issues under the banner "Meta and AI Labs Pause Work with Mercor Following Major Security Breach".
Meta has indefinitely suspended all projects with data contracting firm Mercor after a significant security breach exposed sensitive systems.
The disruption is felt across the environment, affecting AI training data systems, contractor networks, and exposing Proprietary training datasets, 200+ GB database, 1 TB of source code, 3 TB of video files.
In response, moved swiftly to contain the threat with measures like Projects paused, contractors reassigned, and stakeholders are being briefed through Internal email to contractors, public statements from OpenAI.
The case underscores how Ongoing, teams are taking away lessons such as The incident highlights supply chain risks in AI development, where third-party vendors handle highly sensitive data. It underscores the need for stricter security standards in the AI industry, and recommending next steps like Enhance vendor security assessments, Implement stricter access controls for proprietary data and Improve supply chain security for AI tools and dependencies, with advisories going out to stakeholders covering AI labs reassessing partnerships with Mercor.
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 (T1195) with high confidence (90%), with evidence including supply Chain Attack (Tainted AI API tool updates), and compromised LiteLLM AI API tool versions and Compromise Software Supply Chain (T1195.002) with high confidence (90%), supported by evidence indicating tainted updates that exposed numerous organizations via LiteLLM. Under the Execution tactic, the analysis identified Exploitation for Client Execution (T1203) with moderate to high confidence (70%), supported by evidence indicating compromised LiteLLM AI API tool versions exploited. Under the Persistence tactic, the analysis identified Browser Extensions (T1176) with moderate confidence (50%), supported by evidence indicating liteLLM AI API tool updates may have established persistence. Under the Privilege Escalation tactic, the analysis identified Exploitation for Privilege Escalation (T1068) with moderate confidence (60%), supported by evidence indicating compromised AI training data systems via tainted updates. Under the Credential Access tactic, the analysis identified Steal Application Access Token (T1528) with moderate to high confidence (70%), supported by evidence indicating aI training datasets and proprietary data accessed. Under the Discovery tactic, the analysis identified Account Discovery (T1087) with moderate confidence (60%), supported by evidence indicating contractor networks and AI training systems targeted. Under the Collection tactic, the analysis identified Data from Information Repositories (T1213) with high confidence (90%), supported by evidence indicating proprietary training datasets, 200+ GB database, 1 TB source code and Data from Local System (T1005) with moderate to high confidence (80%), supported by evidence indicating 3 TB of video files collected from contractor networks. Under the Exfiltration tactic, the analysis identified Exfiltration Over C2 Channel (T1041) with high confidence (90%), supported by evidence indicating data advertised on Telegram and BreachForums clone and Exfiltration Over Web Service (T1567) with moderate to high confidence (80%), supported by evidence indicating stolen data advertised on Telegram/BreachForums. Under the Impact tactic, the analysis identified Data Encrypted for Impact (T1486) with moderate confidence (50%), supported by evidence indicating teamPCP linked to ransomware operations and Data Destruction (T1485) with moderate confidence (60%), supported by evidence indicating canisterWorm data-wiping tool linked to TeamPCP. Under the Defense Evasion tactic, the analysis identified Masquerading (T1036) with moderate to high confidence (70%), supported by evidence indicating lapsus$ impersonation by TeamPCP for data advertisement and Deobfuscate/Decode Files or Information (T1140) with moderate confidence (50%), supported by evidence indicating tainted LiteLLM updates may have hidden malicious payloads. 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/OPE1775256197
- OpenAI CyberSecurity Rating page: https://www.rankiteo.com/company/openai
- OpenAI Rankiteo Cyber Incident Blog Article: https://blog.rankiteo.com/ope1775256197-openai-cyber-attack-march-2026/
- OpenAI CyberSecurity Score History: https://www.rankiteo.com/company/openai/history
- OpenAI CyberSecurity Incident Source: https://www.wired.com/story/meta-pauses-work-with-mercor-after-data-breach-puts-ai-industry-secrets-at-risk/
- 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