Incident Score: Analysis & Impact (ANTARCFLOIBM1776659058)
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Rankiteo Score Impact Analysis
Key Highlights From The Incident Analysis
- Timeline of IBM'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 IBM 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 IBM breach identified under incident ID ANTARCFLOIBM1776659058.
The analysis begins with a detailed overview of IBM's information like the linkedin page: https://www.linkedin.com/company/ibm, the number of followers: 19057383, the industry type: IT Services and IT Consulting and the number of employees: 341860 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 643 and after the incident was 641 with a difference of -2 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 IBM and their customers.
Flowise recently reported "Critical AI Framework Vulnerability Exposes Millions to Remote Code Execution", a noteworthy cybersecurity incident.
Researchers at OX Security uncovered a severe architectural flaw in the Model Context Protocol (MCP), a communication standard developed by Anthropic and embedded in AI frameworks across Python, TypeScript, Java, and Rust.
The disruption is felt across the environment, affecting AI frameworks (Python, TypeScript, Java, Rust), Flowise, LiteLLM, LangChain, GPT Researcher, DocsGPT, IBM’s LangFlow, and exposing API keys, Internal databases and Chat histories.
In response, moved swiftly to contain the threat with measures like Restrict public exposure of AI services, Treat MCP inputs as untrusted and Enforce sandboxed environments, and began remediation that includes Patches for affected platforms are now available.
The case underscores how Publicly disclosed, patches available, teams are taking away lessons such as Systemic risks in AI infrastructure, need for root-level patches and secure development practices, and recommending next steps like Restrict public exposure of AI services, Treat MCP inputs as untrusted and Enforce sandboxed environments.
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 7,000 publicly accessible servers at risk, and unauthenticated UI injection in major AI frameworks and Exploitation of Remote Services (T1210) with moderate to high confidence (80%), with evidence including remote code execution (RCE) via MCP adapters, and flaw affects over 200,000 vulnerable instances. Under the Execution tactic, the analysis identified Exploitation for Client Execution (T1203) with high confidence (90%), with evidence including remote code execution (RCE) enabled by architectural flaw, and live commands executed on six production platforms and Command and Scripting Interpreter (T1059) with moderate to high confidence (70%), supported by evidence indicating rCE via MCP adapters in Python, TypeScript, Java, Rust. Under the Persistence tactic, the analysis identified Server Software Component: Web Shell (T1505.003) with moderate confidence (60%), supported by evidence indicating unauthenticated UI injection in major AI frameworks. Under the Privilege Escalation tactic, the analysis identified Exploitation for Privilege Escalation (T1068) with moderate to high confidence (70%), supported by evidence indicating hardening bypasses in protected environments like Flowise. Under the Defense Evasion tactic, the analysis identified Exploitation for Defense Evasion (T1211) with moderate to high confidence (80%), with evidence including hardening bypasses in protected environments, and zero-click prompt injection in AI IDEs and Supply Chain Compromise: Compromise Software Supply Chain (T1195.002) with high confidence (90%), with evidence including 9 out of 11 MCP registries compromised, and flaw inherited by any developer building on MCP. Under the Credential Access tactic, the analysis identified Unsecured Credentials: Credentials In Files (T1552.001) with moderate to high confidence (80%), supported by evidence indicating exposing sensitive data including API keys. Under the Collection tactic, the analysis identified Data from Local System (T1005) with high confidence (90%), supported by evidence indicating exposing internal databases and chat histories. Under the Exfiltration tactic, the analysis identified Exfiltration Over C2 Channel (T1041) with moderate to high confidence (70%), supported by evidence indicating remote code execution (RCE) exposes sensitive data. Under the Impact tactic, the analysis identified Resource Hijacking (T1496) with moderate to high confidence (80%), supported by evidence indicating rCE enables potential control of AI frameworks and Endpoint Denial of Service: Application or System Exploitation (T1499.004) with moderate confidence (60%), supported by evidence indicating attack threatening the organizations existence. These correlations help security teams understand the attack chain and develop appropriate defensive measures based on the observed tactics and techniques.
Sources & References
- IBM Rankiteo Cyber Incident Details: https://www.rankiteo.com/company/ibm/incident/ANTARCFLOIBM1776659058
- IBM CyberSecurity Rating page: https://www.rankiteo.com/company/ibm
- IBM Rankiteo Cyber Incident Blog Article: https://blog.rankiteo.com/antarcfloibm1776659058-anthropic-flowise-docsgpt-ibm-vulnerability-april-2026/
- IBM CyberSecurity Score History: https://www.rankiteo.com/company/ibm/history
- IBM CyberSecurity Incident Source: https://cybersecuritynews.com/flowise-vulnerability/
- 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