OpenAI Breach Incident Score: Analysis & Impact (OPE0792807111325)

In this Rankiteo incident briefing, we review the OpenAI breach identified under incident ID OPE0792807111325.

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 757 and after the incident was 754 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 "System Prompt Extraction from OpenAI’s Sora 2 via Cross-Modal Vulnerabilities", a noteworthy cybersecurity incident.

Security researchers successfully extracted the system prompt from OpenAI’s Sora 2 video generation model by exploiting cross-modal vulnerabilities, with audio transcription proving to be the most effective extraction method.

The disruption is felt across the environment, affecting OpenAI Sora 2 (Multimodal Video Generation Model), and exposing System Prompt (Partial/Full), Model Behavior Constraints and Technical Specifications.

Formal response steps have not been shared publicly yet.

The case underscores how Disclosed by Security Researchers (No Official Response from OpenAI Mentioned), teams are taking away lessons such as Multimodal AI systems introduce unique vulnerabilities due to semantic drift across data transformations (text → image → video → audio), System prompts should be treated as sensitive configuration secrets, not harmless metadata and Traditional text-based prompt extraction safeguards (e.g., 'never reveal these rules') are ineffective in multimodal contexts where alternative modalities (e.g., audio) can bypass restrictions, and recommending next steps like Implement modality-specific guardrails to prevent cross-modal prompt extraction (e.g., audio watermarking, visual distortion for text), Treat system prompts as high-value secrets with access controls and encryption and Conduct red-team exercises focusing on multimodal attack vectors (e.g., audio transcription, OCR bypasses).

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 Exploit Public-Facing Application (T1190) with moderate to high confidence (85%), supported by evidence indicating exploited cross-modal vulnerabilities in **OpenAI’s Sora 2**—a cutting-edge multimodal AI model. Under the Credential Access tactic, the analysis identified Unsecured Credentials: Private Keys/Certificates in Configurations (T1552.004) with high confidence (90%), with evidence including treat **system prompts as confidential configuration secrets** rather than benign metadata, and extracted its system prompt, a critical security artifact defining the model’s behavioral guardrails. Under the Defense Evasion tactic, the analysis identified Obfuscated Files or Information: Audio Steganography (T1027.006) with moderate to high confidence (80%), with evidence including bypassing traditional safeguards by fragmenting and reassembling small token sequences from **generated speech clips**, and optimizing **audio output** for high-fidelity recovery and Masquerading: Match Legitimate Name or Location (T1036.005) with moderate to high confidence (75%), supported by evidence indicating semantic drift during cross-modal transformations (text → image → video → audio), where errors accumulate but **short fragments remain recoverable**. Under the Exfiltration tactic, the analysis identified Exfiltration Over Alternative Protocol: Exfiltration Over Audio (T1048.003) with high confidence (95%), with evidence including **audio transcription** as the most effective method, partial/Full System Prompt via **Audio Transcription**, and extracted its system prompt... by fragmenting and reassembling small token sequences from **generated speech clips** and Automated Exfiltration: Traffic Duplication (T1020.001) with moderate to high confidence (70%), supported by evidence indicating stitched together fragmented tokens from audio outputs. Under the Discovery tactic, the analysis identified Cloud Infrastructure Discovery: Cloud Account (T1580.001) with moderate to high confidence (70%), with evidence including exposure reveals **content restrictions, copyright protections, and technical specifications**, and defining the model’s behavioral guardrails and operational constraints. Under the Impact tactic, the analysis identified Endpoint Denial of Service: Application or System Exploitation (T1499.004) with moderate confidence (65%), with evidence including could enable follow-up attacks or **model misuse**, and facilitate adversarial exploits targeting behavioral constraints or proprietary logic. These correlations help security teams understand the attack chain and develop appropriate defensive measures based on the observed tactics and techniques.