Incident Score: Analysis & Impact (FLODEEANTOPEN8N1777984637)
The details regarding individual company incidents & reports gives you full view from every side.
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 FLODEEANTOPEN8N1777984637.
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 699 and after the incident was 693 with a difference of -6 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.
A newly reported cybersecurity incident, "AI Infrastructure Security Crisis: Exposed Systems, Hardcoded Flaws, and Rampant Misconfigurations", has drawn attention.
A recent investigation by the Intruder team reveals an alarming trend in AI infrastructure security, as rapid adoption outpaces safeguards.
The disruption is felt across the environment, affecting Self-hosted AI projects, Agent management platforms (n8n, Flowise) and Ollama APIs, and exposing LLM conversation logs, API keys and Business logic.
Formal response steps have not been shared publicly yet.
The case underscores how Completed, teams are taking away lessons such as The investigation highlights the security debt accumulating in the AI gold rush, where rapid deployment is prioritized over security best practices. Key lessons include the need for default authentication, secure deployment practices, and avoiding hardcoded credentials, and recommending next steps like Enable authentication by default in AI projects, Secure Docker setups and avoid running applications as root and Remove hardcoded credentials from setup examples.
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 high confidence (90%), with evidence including scanning over 2 million hosts with 1 million exposed services, and exposed APIs, unauthenticated access, Valid Accounts (T1078) with moderate to high confidence (80%), with evidence including no authentication by default in self-hosted AI projects, and static credentials in setup examples, and External Remote Services (T1133) with moderate to high confidence (85%), supported by evidence indicating 5,200+ exposed Ollama APIs with 31% responding to unauthenticated queries. Under the Execution tactic, the analysis identified Exploitation for Client Execution (T1203) with moderate to high confidence (70%), supported by evidence indicating arbitrary code execution found in a popular AI project and Command and Scripting Interpreter (T1059) with moderate to high confidence (75%), supported by evidence indicating server-side code execution via exposed parsing tools and local functions. Under the Persistence tactic, the analysis identified Account Manipulation (T1098) with moderate to high confidence (70%), with evidence including users granted high-privilege access by default, and misconfigured agent platforms enabling workflow modification and Server Software Component: Web Shell (T1505.003) with moderate confidence (60%), supported by evidence indicating exposed agent platforms (n8n, Flowise) enabling traffic redirection. Under the Privilege Escalation tactic, the analysis identified Valid Accounts (T1078) with moderate to high confidence (80%), with evidence including applications running as root, and high-privilege access granted by default and Abuse Elevation Control Mechanism: Setuid and Setgid (T1548.001) with moderate confidence (60%), supported by evidence indicating poor deployment practices, misconfigured Docker setups. Under the Defense Evasion tactic, the analysis identified Impair Defenses: Disable or Modify Tools (T1562.001) with moderate to high confidence (70%), with evidence including no authentication by default in AI projects, and weak sandboxing amplifies risks and Valid Accounts (T1078) with moderate to high confidence (75%), with evidence including hardcoded credentials in setup examples, and static credentials in docker-compose files. Under the Credential Access tactic, the analysis identified Unsecured Credentials: Credentials In Files (T1552.001) with high confidence (90%), with evidence including hardcoded credentials in setup examples, and static credentials in docker-compose files and Unsecured Credentials: Container API (T1552.007) with moderate to high confidence (80%), with evidence including aPI keys leaked in plaintext by NSFW chatbots, and exposed credential lists in Flowise deployments. Under the Discovery tactic, the analysis identified File and Directory Discovery (T1083) with moderate to high confidence (70%), with evidence including exposed business logic in Flowise deployments, and lLM conversation logs accessible and Network Service Scanning (T1046) with moderate to high confidence (85%), supported by evidence indicating scanning over 2 million hosts with 1 million exposed services. Under the Collection tactic, the analysis identified Data from Local System (T1005) with high confidence (90%), with evidence including lLM conversation logs exposed, and user conversation histories accessible and Data from Information Repositories (T1213) with moderate to high confidence (80%), with evidence including business logic exposed in Flowise deployments, and credential lists accessible. Under the Exfiltration tactic, the analysis identified Exfiltration Over C2 Channel (T1041) with moderate to high confidence (70%), supported by evidence indicating data exposure via exposed APIs and unauthenticated access and Transfer Data to Cloud Account (T1537) with moderate confidence (60%), supported by evidence indicating cloud management integrations exposed via Ollama APIs. Under the Impact tactic, the analysis identified Defacement: Internal Defacement (T1491.001) with moderate to high confidence (70%), supported by evidence indicating unauthorized modification of workflows in agent platforms, Data Manipulation: Transmitted Data Manipulation (T1565.002) with moderate to high confidence (75%), supported by evidence indicating response poisoning via exposed agent platforms, and Endpoint Denial of Service: Application or System Exploitation (T1499.004) with moderate confidence (60%), supported by evidence indicating server-side code execution via exposed parsing tools. 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/FLODEEANTOPEN8N1777984637
- OpenAI CyberSecurity Rating page: https://www.rankiteo.com/company/openai
- OpenAI Rankiteo Cyber Incident Blog Article: https://blog.rankiteo.com/flodeeantopen8n1777984637-deepseek-anthropic-openai-n8n-flowise-vulnerability-may-2025/
- OpenAI CyberSecurity Score History: https://www.rankiteo.com/company/openai/history
- OpenAI CyberSecurity Incident Source: https://thehackernews.com/2026/05/we-scanned-1-million-exposed-ai.html
- 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