Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:HSummary
CVE-2024-8233 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 in the top 48% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
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.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-49593
Vulnerability Data
An issue has been discovered in GitLab CE/EE affecting all versions from 9.4 before 17.4.6, 17.5 before 17.5.4, and 17.6 before 17.6.2. An attacker could cause a denial of service with requests for diff files on a commit or merge…
more
request.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
—
—
—
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.