Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:N/A:NSummary
CVE-2025-7000 is a medium-severity Insertion of Sensitive Information Into Sent Data (CWE-201) vulnerability in Gitlab Gitlab. Its CVSS base score is 4.3 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Code Repositories (T1213.003); ranked at the 28th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
The strongest mitigations our analysis identified map to AC-3 (Access Enforcement) and AC-4 (Information Flow Enforcement) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-197693
Vulnerability Data
An issue has been discovered in GitLab CE/EE affecting all versions from 17.6 before 18.3.6, 18.4 before 18.4.4, and 18.5 before 18.5.2, that, under specific conditions, could have allowed unauthorized users to view confidential branch names by accessing project issues…
more
with related merge requests.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Vuln directly enables unauthorized access to confidential branch names stored in GitLab code repository issues/MRs (T1213.003).
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
Mitigating Controls (NIST 800-53 r5) AI
Directly enforces access restrictions on project issues and linked merge requests so that confidential branch names are not exposed to unauthorized users.
Enforces information-flow rules between merge-request metadata and issue views to block leakage of sensitive branch names.
Limits user privileges to the minimum required, reducing the chance that an account can reach issue records containing confidential branch references.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Secure SDLC practices directly prevent insertion of sensitive data into application outputs and messages.
Monitoring runtime data flows and outputs can detect sensitive data being transmitted.
Protecting data-in-transit can include filtering or encrypting to avoid exposing sensitive content.
Protecting data-in-use includes removing confidential values before they are processed or sent.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Data-masking techniques can prevent sensitive values from appearing in transmitted payloads.
Classification identifies sensitive data so it is not inadvertently transmitted.
Labelling makes sensitive data visible to developers and prevents accidental inclusion in outbound messages.
Information-transfer rules directly govern what data may be sent to external parties.
PII-protection requirements reduce the chance of sending personal data to unauthorized recipients.
DLP controls inspect and block outbound flows that contain sensitive information.