Incident Score: Analysis & Impact (BER1781007894)
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Rankiteo Score Impact Analysis
Key Highlights From The Incident Analysis
- Timeline of Berry AI'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 Berry AI 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 Berry AI breach identified under incident ID BER1781007894.
The analysis begins with a detailed overview of Berry AI's information like the linkedin page: https://www.linkedin.com/company/berry-ai, the number of followers: 1332, the industry type: Software Development and the number of employees: 36 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 751 and after the incident was 748 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 Berry AI and their customers.
BerryAI recently reported "Critical Command Injection Flaw in LiteLLM AI Gateway Under Active Exploitation", a noteworthy cybersecurity incident.
The U.S.
The disruption is felt across the environment, affecting LiteLLM host system, connected AI infrastructure, downstream systems, and exposing Model provider credentials and API keys.
In response, moved swiftly to contain the threat with measures like Block access to vulnerable MCP test endpoints and Restrict network access to trusted segments, and began remediation that includes Upgrade to LiteLLM v1.83.7 and Rotate credentials stored by the proxy.
The case underscores how Ongoing, and recommending next steps like Upgrade to LiteLLM v1.83.7, Block access to vulnerable MCP test endpoints and Restrict network access to trusted segments, with advisories going out to stakeholders covering CISA mandate for U.S. federal civilian agencies to remediate by June 22, 2026.
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%), supported by evidence indicating cVE-2026-42271 in LiteLLM AI gateway under active exploitation, Valid Accounts (T1078) with moderate to high confidence (80%), supported by evidence indicating exploitation required only a valid proxy API key, and External Remote Services (T1133) with moderate to high confidence (70%), supported by evidence indicating liteLLM used as a standalone proxy server for AI traffic. Under the Execution tactic, the analysis identified Command and Scripting Interpreter (T1059) with high confidence (95%), supported by evidence indicating arbitrary command execution on the LiteLLM host via MCP test endpoints. Under the Privilege Escalation tactic, the analysis identified Exploitation for Privilege Escalation (T1068) with moderate to high confidence (80%), supported by evidence indicating cVE-2026-48710 (Starlette auth bypass) chained with CVE-2026-42271. Under the Credential Access tactic, the analysis identified Unsecured Credentials: Credentials In Files (T1552.001) with high confidence (90%), supported by evidence indicating theft of model provider credentials and API keys stored by the proxy. Under the Lateral Movement tactic, the analysis identified Exploitation of Remote Services (T1210) with moderate to high confidence (80%), supported by evidence indicating lateral movement into connected AI infrastructure and downstream systems. Under the Defense Evasion tactic, the analysis identified Valid Accounts (T1078) with moderate to high confidence (80%), supported by evidence indicating exploitation via low-privilege API keys to evade detection and Impair Defenses: Disable or Modify Tools (T1562.001) with moderate confidence (60%), supported by evidence indicating authentication bypass (CVE-2026-48710) to evade access controls. Under the Exfiltration tactic, the analysis identified Exfiltration Over C2 Channel (T1041) with moderate to high confidence (70%), supported by evidence indicating theft of model provider credentials and API keys. These correlations help security teams understand the attack chain and develop appropriate defensive measures based on the observed tactics and techniques.
Sources & References
- Berry AI Rankiteo Cyber Incident Details: https://www.rankiteo.com/company/berry-ai/incident/BER1781007894
- Berry AI CyberSecurity Rating page: https://www.rankiteo.com/company/berry-ai
- Berry AI Rankiteo Cyber Incident Blog Article: https://blog.rankiteo.com/ber1781007894-berryai-vulnerability-april-2026/
- Berry AI CyberSecurity Score History: https://www.rankiteo.com/company/berry-ai/history
- Berry AI CyberSecurity Incident Source: https://www.helpnetsecurity.com/2026/06/09/litellm-vulnerability-under-active-attack-cisa-warns-cve-2026-42271/
- 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