CVE-2026-0958
Gitlab 18.4.0 – 18.6.6
Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:HSummary
CVE-2026-0958 is a high-severity Interpretation Conflict (CWE-436) vulnerability in Gitlab Gitlab. Its CVSS base score is 7.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 32th 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 SI-10 (Information Input Validation) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2026-0958 is a denial-of-service vulnerability in GitLab Community Edition (CE) and Enterprise Edition (EE), affecting all versions from 18.4 prior to 18.6.6, 18.7 prior to 18.7.4, and 18.8 prior to 18.8.4. The issue stems from a bypass of JSON validation middleware limits, enabling memory or CPU exhaustion. It has a CVSS v3.1 base score of 7.5 (AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H) and is associated with CWE-436 (Interpretation Conflict).
An unauthenticated attacker with network access can exploit this vulnerability by sending crafted requests that evade the JSON size or complexity limits enforced by the middleware. Successful exploitation leads to resource exhaustion, causing denial of service through high memory or CPU usage on the affected GitLab instance, with no impact on confidentiality or integrity.
GitLab has remediated the vulnerability in patch releases, including GitLab 18.8.4, as detailed in the official release notes. Security practitioners should upgrade to GitLab 18.6.6, 18.7.4, or 18.8.4 or later versions. Additional details are available in the GitLab issue tracker (gitlab.com/gitlab-org/gitlab/-/issues/586202) and the corresponding HackerOne disclosure (hackerone.com/reports/3463363).
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-7060
Vulnerability Data
GitLab has remediated an issue in GitLab CE/EE affecting all versions from 18.4 before 18.6.6, 18.7 before 18.7.4, and 18.8 before 18.8.4 that could have allowed an unauthenticated user to cause denial of service through memory or CPU exhaustion by…
more
bypassing JSON validation middleware limits.
- 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 evaluation can discover cases where two products interpret the same inputs or state transitions differently.
Strict, consistently applied input validation reduces the chance that one product will accept data the other product rejects or interprets differently.
Applying security engineering principles during design can require unambiguous protocol and data-format specifications that eliminate divergent interpretations between products.
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 reduce the chance of introducing parser or state-machine inconsistencies.
Correlating logs from multiple products can surface discrepancies caused by interpretation conflicts.
Runtime monitoring of software behavior can detect adverse outcomes stemming from differing interpretations.
Supplier risk assessments can identify products whose differing interpretations create systemic exposure.
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.
Security testing can detect and correct cases where one component misinterprets another’s state or messages.
Secure development lifecycle can require consistent interface contracts and canonicalization rules that reduce interpretation conflicts between components.
Explicit application security requirements can mandate unambiguous protocol and data-format specifications that prevent divergent interpretations.
Secure architecture principles include well-defined component boundaries and shared data models that limit conflicting state perceptions.
Secure coding standards can enforce canonical input handling and strict protocol compliance to avoid misinterpretation between products.