CVE-2025-11203
Published: 29 October 2025
Summary
CVE-2025-11203 is a low-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Litellm (inferred from references). Its CVSS base score is 3.5 (Low).
Operationally, ranked at the 27.3th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-36712
Vulnerability details
LiteLLM Information health API_KEY Information Disclosure Vulnerability. This vulnerability allows remote attackers to disclose sensitive information on affected installations of LiteLLM. Authentication is required to exploit this vulnerability. The specific flaw exists within the handling of the API_KEY parameter provided…
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to the health endpoint. The issue results from exposing sensitive information to an unauthorized actor. An attacker can leverage this vulnerability to disclose stored credentials, leading to further compromise. Was ZDI-CAN-26585.
- CWE(s)
Related Threats
No named actor attribution yet. ATT&CK technique mapping in progress for this CVE.
Affected Assets
Mitigating Controls
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Automated marking applies security attributes to system outputs, making it harder for attackers to exploit unmarked sensitive information leading to unauthorized exposure.
Proper attribute retention and permitted-value enforcement limits unauthorized actors from accessing sensitive information lacking correct labels.
Prevents unauthorized exposure of sensitive information by prohibiting untrusted external systems from processing or storing it.
By enforcing authorization matching prior to sharing, the control reduces the risk of exposing sensitive information to unauthorized actors.
Review and removal of nonpublic information from publicly accessible systems directly prevents exposure of sensitive data to unauthorized actors.
Data mining protection mechanisms detect and block unauthorized bulk extraction of sensitive data, directly mitigating exposure to unauthorized actors.
Literacy training teaches users to recognize and avoid actions that result in unauthorized exposure of sensitive information.
Retaining and monitoring training records confirms personnel have completed privacy and security awareness training on handling sensitive data, reducing the chance of unauthorized exposure due to lack of knowledge.