OpenAI Breach Incident Score: Analysis & Impact (OPE3692336110625)

In this Rankiteo incident briefing, we review the OpenAI breach identified under incident ID OPE3692336110625.

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: 7885491, the industry type: Research Services and the number of employees: 6872 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 743 and after the incident was 740 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 "Seven Security Flaws in OpenAI’s ChatGPT (Including GPT-5) Expose Users to Data Theft and Persistent Control", a noteworthy cybersecurity incident.

Tenable Research uncovered seven security vulnerabilities in OpenAI’s ChatGPT (including GPT-5) that enable attackers to steal private user data and gain persistent control over the AI chatbot.

The disruption is felt across the environment, affecting ChatGPT (GPT-4o, GPT-5) and LLM-Powered Systems Using ChatGPT APIs, and exposing Private User Data and Potential PII (via exfiltration).

In response, teams activated the incident response plan, moved swiftly to contain the threat with measures like Patching vulnerabilities (ongoing) and Enhancing prompt injection defenses, and stakeholders are being briefed through Public disclosure via Tenable Research report and Media statements (e.g., Hackread.com).

The case underscores how Ongoing (OpenAI addressing vulnerabilities; prompt injection remains unresolved), teams are taking away lessons such as Prompt injection remains a systemic risk for LLMs, requiring context-aware security solutions, Indirect attack vectors (e.g., hidden comments, indexed websites) exploit trust in external sources and Safety features like `url_safe` can be bypassed via trusted domains (e.g., Bing.com), and recommending next steps like Implement context-based security controls for LLMs to detect and block prompt injection, Enhance input validation for external sources (e.g., websites, comments) processed by AI and Monitor for anomalous AI behaviors (e.g., self-injected instructions, hidden code blocks), with advisories going out to stakeholders covering Companies using generative AI warned about prompt injection risks (via DryRun Security CEO).

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.

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 to high confidence (85%), supported by evidence indicating indirect prompt injection (hidden in comments/blogs) and 0-Click Attack via Search (malicious indexed websites) and Valid Accounts: Cloud Accounts (T1078.004) with moderate to high confidence (80%), supported by evidence indicating gain persistent control over the AI system via exploited ChatGPT user sessions. Under the Execution tactic, the analysis identified Command and Scripting Interpreter: JavaScript (T1059.007) with moderate to high confidence (75%), supported by evidence indicating malicious instructions hidden in external sources (e.g., websites/comments) executed by AI and User Execution: Malicious Link (T1204.001) with high confidence (90%), supported by evidence indicating safety Bypass using trusted Bing tracking links to trigger unauthorized actions. Under the Persistence tactic, the analysis identified Server Software Component: Web Shell (T1505.003) with moderate to high confidence (85%), supported by evidence indicating memory Injection (persistent control) and conversation/memory injection for long-term threats and Account Manipulation: Additional Cloud Credentials (T1098.003) with moderate to high confidence (70%), supported by evidence indicating persistent control over the AI system suggests credential/state manipulation. Under the Privilege Escalation tactic, the analysis identified Exploitation for Privilege Escalation (T1068) with moderate to high confidence (80%), supported by evidence indicating bypassing safety features like `url_safe` to execute unauthorized actions. Under the Defense Evasion tactic, the analysis identified Obfuscated Files or Information (T1027) with high confidence (90%), supported by evidence indicating code block display bug (hiding malicious instructions) and indirect prompt injection, Masquerading: Match Legitimate Name or Location (T1036.005) with high confidence (95%), supported by evidence indicating trusted Bing.com tracking links used to bypass URL protections, and Hide Artifacts: Email Hiding Rules (T1564.008) with moderate to high confidence (70%), supported by evidence indicating hidden comments in blogs to evade detection. Under the Credential Access tactic, the analysis identified Unsecured Credentials: Private Keys (T1552.004) with moderate confidence (60%), supported by evidence indicating steal private user data may include API keys/session tokens stored in memory. Under the Discovery tactic, the analysis identified Cloud Service Discovery (T1526) with moderate to high confidence (75%), supported by evidence indicating exploitation of AI trust mechanisms suggests probing for cloud service integrations. Under the Collection tactic, the analysis identified Automated Collection (T1119) with high confidence (90%), supported by evidence indicating steal private user data and leaking private conversations via automated prompt injection and Data from Local System (T1005) with moderate to high confidence (85%), supported by evidence indicating exfiltrate sensitive data from ChatGPT memory/conversation history. 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 data exfiltration via PoC (e.g., Bing.com tracking links) and Automated Exfiltration: Traffic Duplication (T1020.001) with moderate to high confidence (70%), supported by evidence indicating leaking private conversations suggests automated forwarding of user data. Under the Impact tactic, the analysis identified Resource Hijacking (T1496) with moderate to high confidence (85%), supported by evidence indicating persistent control over the AI system for malicious actions (e.g., phishing, misinformation) and Account Access Removal (T1531) with moderate confidence (60%), supported by evidence indicating compromised AI responses could lock users out of intended functionality. These correlations help security teams understand the attack chain and develop appropriate defensive measures based on the observed tactics and techniques.