Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:HSummary
CVE-2026-3988 is a high-severity Inefficient Algorithmic Complexity (CWE-407) vulnerability in Gitlab Gitlab. Its CVSS base score is 7.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Endpoint Denial of Service (T1499); ranked at the 39th 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 SC-5 (Denial-of-service Protection) and SC-6 (Resource Availability) — 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-2026-3988 is a denial-of-service vulnerability in GitLab Community Edition (CE) and Enterprise Edition (EE), stemming from improper input validation in GraphQL request processing. It affects all versions from 18.5 prior to 18.8.7, 18.9 prior to 18.9.3, and 18.10 prior to 18.10.1. The issue, classified under CWE-407 (Inappropriate Resource Shutdown or Modification), carries 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), highlighting its high-impact availability disruption potential over the network without authentication.
An unauthenticated attacker can exploit this vulnerability by sending specially crafted GraphQL requests, causing the GitLab instance to become unresponsive and effectively denying service to legitimate users. No privileges, user interaction, or special conditions are required, making it accessible to remote adversaries scanning for vulnerable instances.
GitLab has released patches addressing this issue, including version 18.10.1 as detailed in their patch release notes. Security practitioners should upgrade affected GitLab CE/EE installations to 18.8.7, 18.9.3, or 18.10.1 or later. Additional details are available in the GitLab work item and the originating HackerOne report.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-15937
Vulnerability Data
GitLab has remediated an issue in GitLab CE/EE affecting all versions from 18.5 before 18.8.7, 18.9 before 18.9.3, and 18.10 before 18.10.1 that could have allowed an unauthenticated user to cause a denial of service by making the GitLab instance…
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unresponsive due to improper input validation in GraphQL request processing.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.2.9
Mitigating Controls (NIST 800-53 r5) AI
Denial-of-service protection directly reduces the impact of resource exhaustion triggered by worst-case algorithmic inputs.
Resource availability allocation limits blast radius when an inefficient algorithm is forced into its worst case.
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 (code review, complexity analysis, safe algorithm selection) prevent introduction of exploitable worst-case behavior.
Runtime monitoring of software and resources can detect the performance impact of triggered worst-case complexity.
Identifying and recording algorithmic-complexity vulnerabilities directly addresses the root cause before exploitation.
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 uncover performance issues stemming from algorithmic complexity.
Redundancy of processing facilities can absorb resource exhaustion from inefficient algorithms.
Monitoring activities can identify anomalous resource consumption indicative of algorithmic complexity attacks.
Secure development life cycle includes design reviews that can catch inefficient algorithms before deployment.
Secure system architecture principles encourage selection of algorithms with acceptable worst-case complexity.
Secure coding practices can include guidelines to avoid or mitigate inefficient algorithms.