Incident Score: Analysis & Impact (LIT1781000708)
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Rankiteo Score Impact Analysis
Key Highlights From The Incident Analysis
- Timeline of LiteLLM'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 LiteLLM 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 LiteLLM breach identified under incident ID LIT1781000708.
The analysis begins with a detailed overview of LiteLLM's information like the linkedin page: https://www.linkedin.com/company/litellm, the number of followers: 8480, the industry type: Software Development and the number of employees: 7 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 315 and after the incident was 310 with a difference of -5 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 LiteLLM and their customers.
On 01 June 2026, LiteLLM disclosed Remote Code Execution (RCE) issues under the banner "Critical LiteLLM RCE Vulnerability Actively Exploited in the Wild".
Threat actors are actively exploiting a critical unauthenticated remote code execution (RCE) vulnerability in LiteLLM, a widely used open-source AI proxy gateway, by chaining two CVEs to bypass authentication and execute arbitrary commands on vulnerable systems.
The disruption is felt across the environment, affecting LiteLLM proxy gateway and connected AI systems, and exposing Model provider credentials, API keys, stored secrets, and downstream AI infrastructure data.
In response, moved swiftly to contain the threat with measures like Block external access to `/mcp-rest/test/connection` and `/mcp-rest/test/tools/list` and Restrict network access to trusted segments, and began remediation that includes Upgrade to LiteLLM 1.83.7, Upgrade Starlette to 1.0.1 or later and Rotate all stored credentials and API keys.
The case underscores how Confirmed exploitation in the wild, and recommending next steps like Upgrade LiteLLM to version 1.83.7 or later, Upgrade Starlette to version 1.0.1 or later and Rotate all stored credentials and API keys.
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 critical unauthenticated RCE vulnerability in LiteLLM, a widely used open-source AI proxy gateway and Exploitation of Remote Services (T1210) with high confidence (90%), supported by evidence indicating exploiting CVE-2026-42271 and CVE-2026-48710 to achieve unauthenticated RCE. Under the Execution tactic, the analysis identified Command and Scripting Interpreter (T1059) with high confidence (95%), supported by evidence indicating arbitrary OS command execution on the host via malicious commands in MCP server endpoints. Under the Credential Access tactic, the analysis identified Unsecured Credentials: Credentials In Files (T1552.001) with high confidence (90%), supported by evidence indicating access to model provider credentials and API keys (e.g., OpenAI, Anthropic, Azure OpenAI) and Unsecured Credentials: Private Keys (T1552.004) with moderate to high confidence (80%), supported by evidence indicating theft of stored secrets within 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. Under the Collection tactic, the analysis identified Data from Local System (T1005) with high confidence (90%), supported by evidence indicating theft of stored secrets, model provider credentials, and API keys. Under the Exfiltration tactic, the analysis identified Exfiltration Over C2 Channel (T1041) with moderate to high confidence (70%), supported by evidence indicating compromise of downstream systems integrated with the gateway. Under the Defense Evasion tactic, the analysis identified Exploit Public-Facing Application (T1190) with moderate to high confidence (80%), supported by evidence indicating bypassing authentication via Host header manipulation (CVE-2026-48710) and Impair Defenses: Disable or Modify Tools (T1562.001) with moderate confidence (60%), supported by evidence indicating manipulation of Host header values to bypass authentication controls. These correlations help security teams understand the attack chain and develop appropriate defensive measures based on the observed tactics and techniques.
Sources & References
- LiteLLM Rankiteo Cyber Incident Details: https://www.rankiteo.com/company/litellm/incident/LIT1781000708
- LiteLLM CyberSecurity Rating page: https://www.rankiteo.com/company/litellm
- LiteLLM Rankiteo Cyber Incident Blog Article: https://blog.rankiteo.com/lit1781000708-litellm-vulnerability-june-2026/
- LiteLLM CyberSecurity Score History: https://www.rankiteo.com/company/litellm/history
- LiteLLM CyberSecurity Incident Source: https://cyberpress.org/litellm-rce-vulnerability-exploited/
- 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