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Analyze » OpenAI » OPEGIT1774889403

Incident Score: Analysis & Impact (OPEGIT1774889403)

The details regarding individual company incidents & reports gives you full view from every side.

Rankiteo Score Impact Analysis

Rankiteo Incident Impact-3
Company Score Before Incident599 / 1000
Company Score After Incident596 / 1000
INCIDENT NUMBEROPEGIT1774889403
Type of Cyber IncidentVulnerability
ATTACK VECTORMalicious branch names in GitHub repositories
DATA EXPOSEDGitHub OAuth tokens
INCIDENT DATE29/03/2026
STATUSpublished

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 OPEGIT1774889403.

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 599 and after the incident was 596 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 Codex Vulnerability Exposed GitHub Tokens via Command Injection", a noteworthy cybersecurity incident.

A critical security flaw in OpenAI’s Codex, an AI-powered coding assistant integrated with GitHub, could have allowed attackers to steal GitHub OAuth tokens through a command injection vulnerability.

The disruption is felt across the environment, affecting CLI tools, SDKs and IDE integrations, and exposing GitHub OAuth tokens.

In response, moved swiftly to contain the threat with measures like Stricter input validation, shell escaping protections, tighter token controls, and began remediation that includes Reduced token scope and lifetime during task execution.

The case underscores how teams are taking away lessons such as The incident underscores the growing security challenges of AI-driven development tools, which operate as live execution environments with access to sensitive credentials. Securing their containerized environments and input processing requires the same rigor as traditional application security boundaries.

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%), supported by evidence indicating critical security flaw in OpenAI’s Codex...integrated with GitHub and Drive-by Compromise (T1189) with moderate confidence (60%), supported by evidence indicating vulnerability affected CLI tools, SDKs, and IDE integrations. Under the Execution tactic, the analysis identified Command and Scripting Interpreter (T1059) with high confidence (90%), supported by evidence indicating command injection vulnerability...inject arbitrary shell commands and Exploitation for Client Execution (T1203) with moderate to high confidence (70%), supported by evidence indicating malicious branch names in GitHub repositories. Under the Credential Access tactic, the analysis identified Steal Application Access Token (T1528) with high confidence (95%), supported by evidence indicating steal GitHub OAuth tokens...exposed via task outputs or external network requests and Unsecured Credentials: Credentials In Files (T1552.001) with moderate to high confidence (80%), supported by evidence indicating locally stored credentials could be leveraged to reproduce the attack. Under the Lateral Movement tactic, the analysis identified Use Alternate Authentication Material: Application Access Token (T1550.001) with moderate to high confidence (85%), supported by evidence indicating gitHub tokens...granting attackers potential access to sensitive resources and Account Discovery: Cloud Account (T1087.004) with moderate to high confidence (70%), supported by evidence indicating lateral movement within GitHub...broad permissions across multiple repositories. Under the Exfiltration tactic, the analysis identified Exfiltration Over C2 Channel (T1041) with moderate to high confidence (80%), supported by evidence indicating tokens could be exposed via external network requests and Exfiltration Over Other Network Medium: Remote Email Collection (T1011.001) with moderate confidence (60%), supported by evidence indicating task outputs or external network requests. Under the Defense Evasion tactic, the analysis identified Exploit Public-Facing Application (T1190) with moderate to high confidence (70%), supported by evidence indicating improper handling of branch names during task execution and Indirect Command Execution (T1202) with moderate to high confidence (80%), supported by evidence indicating arbitrary shell commands into containerized environments. These correlations help security teams understand the attack chain and develop appropriate defensive measures based on the observed tactics and techniques.

Initial Access
Exploit Public-Facing Application (80%)
Drive-by Compromise (60%)
Execution
Command and Scripting Interpreter (90%)
Exploitation for Client Execution (70%)
Credential Access
Steal Application Access Token (95%)
Unsecured Credentials: Credentials In Files (80%)
Lateral Movement
Use Alternate Authentication Material: Application Access Token (85%)
Account Discovery: Cloud Account (70%)
Exfiltration
Exfiltration Over C2 Channel (80%)
Exfiltration Over Other Network Medium: Remote Email Collection (60%)
Defense Evasion
Exploit Public-Facing Application (70%)
Indirect Command Execution (80%)