CVE-2025-13772
Published: 09 January 2026
Summary
CVE-2025-13772 is a high-severity Missing Authorization (CWE-862) vulnerability in Gitlab Gitlab. Its CVSS base score is 7.1 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 0.5th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as Other AI Platforms.
Threat & Defense at a Glance
Threat & Defense Details
Likely Mitigating ControlsAI
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.
Requiring an access control policy ensures authorization checks are defined and applied for critical functions.
Reviews of access controls detect missing authorization checks on critical functions or resources.
Documenting permitted unauthenticated actions prevents missing authorization by making all exceptions explicit and subject to organizational review.
Requiring attribute association with information prevents authorization from being performed without necessary security or privacy context.
Mandating authorization prior to allowing remote connections addresses missing authorization for remote access.
Mandating authorization before wireless connections are allowed prevents missing authorization for wireless access.
The control requires authorization before allowing mobile device connections, directly mitigating missing authorization for system access.
Requiring approvals for account creation and specifying authorizations ensures authorization is not missing for system access.
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Missing authorization flaw in public-facing GitLab web/API service directly enables exploitation of the application to access unauthorized data/configurations.
NVD Description
GitLab has remediated an issue in GitLab EE affecting all versions from 18.4 before 18.5.5, 18.6 before 18.6.3, and 18.7 before 18.7.1 that could have allowed an authenticated user to access and utilize AI model settings from unauthorized namespaces by…
more
manipulating namespace identifiers in API requests.
Deeper analysisAI
CVE-2025-13772 is a missing authorization vulnerability (CWE-862) in GitLab Enterprise Edition (EE), affecting all versions from 18.4 prior to 18.5.5, 18.6 prior to 18.6.3, and 18.7 prior to 18.7.1. The flaw allows an authenticated user to access and utilize AI model settings from unauthorized namespaces by manipulating namespace identifiers in API requests. It has a CVSS v3.1 base score of 7.1 (AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:L/A:N), indicating high confidentiality impact with low integrity impact and no availability impact.
An authenticated attacker with low privileges (PR:L) can exploit this vulnerability over the network (AV:N) with low complexity and no user interaction required. By crafting API requests with manipulated namespace identifiers, the attacker gains unauthorized read access to sensitive AI model settings in other namespaces (C:H) and limited ability to influence them (I:L), potentially exposing or tampering with AI configurations belonging to other projects or organizations.
GitLab has remediated the issue in patch releases including 18.5.5, 18.6.3, and 18.7.1, as detailed in the release notes at https://about.gitlab.com/releases/2026/01/07/patch-release-gitlab-18-7-1-released/ and the issue tracker at https://gitlab.com/gitlab-org/gitlab/-/issues/581268. Security practitioners should upgrade to these patched versions immediately to mitigate the risk.
The vulnerability's relevance to AI/ML stems from its impact on AI model settings, which could expose proprietary configurations in multi-tenant GitLab environments. No public information indicates real-world exploitation as of the CVE publication on 2026-01-09.
Details
- CWE(s)
Affected Products
AI Security AnalysisAI
- AI Category
- Other AI Platforms
- Risk Domain
- N/A
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: ai