CVE-2024-9596
Info Disclosure in Gitlab 16.6.0 – 17.2.9
Raw vector
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:N/A:NSummary
CVE-2024-9596 is a low-severity Inclusion of Sensitive Information in Source Code (CWE-540) vulnerability in Gitlab Gitlab. Its CVSS base score is 3.7 (Low).
Operationally, exploitation aligns with the MITRE ATT&CK technique Code Repositories (T1213.003); ranked at the 25th 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 SA-11 (Developer Testing and Evaluation) and AC-3 (Access Enforcement) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-50047
Vulnerability Data
An issue has been discovered in GitLab EE affecting all versions starting from 16.6 prior to 17.2.9, from 17.3 prior to 17.3.5, and from 17.4 prior to 17.4.2. It was possible for an unauthenticated attacker to determine the GitLab version…
more
number for a GitLab instance.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and static analysis locate hard-coded sensitive values before code is deployed.
Enforces access so that source containing embedded secrets cannot be reached by unauthorized parties.
Engineering principles require secrets to be externalized rather than embedded in source.
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 prohibit embedding secrets or sensitive data in source code.
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.
Directly restricts access to source code, preventing exposure of embedded sensitive information.
Secure coding standards explicitly prohibit hard-coding credentials or sensitive data.
Classification helps identify source code containing sensitive data so it can be protected.
Access control limits who can view or retrieve source code that may contain secrets.
Secure SDLC practices include removing secrets from code before commit or deployment.
Environment separation reduces risk of accidental exposure but does not address secrets already in code.