Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:NSummary
CVE-2023-2442 is a high-severity Cross-site Scripting (CWE-79) vulnerability in Gitlab Gitlab. Its CVSS base score is 8.7 (High).
Operationally, ranked in the top 0.1% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
The strongest mitigations our analysis identified map to SI-10 (Information Input Validation) and SI-15 (Information Output Filtering) — 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.
A stored cross-site scripting vulnerability exists in GitLab Community Edition and Enterprise Edition. It affects all versions from 15.11 up to but not including 15.11.7 and all versions from 16.0 up to but not including 16.0.2. The flaw is triggered when a specially crafted merge request is processed, allowing attacker-controlled script to execute persistently in victims' browsers and resulting in a client-side stored XSS condition tracked as CWE-79.
An authenticated user who can submit merge requests is able to exploit the issue. Successful exploitation grants the attacker the ability to perform arbitrary actions within the victim's session, including actions that could lead to full account compromise given the CVSS vector of AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:N.
GitLab has published patches that remediate the vulnerability in the fixed releases 15.11.7 and 16.0.2; the associated advisories and issue trackers are available at the referenced GitLab CVE JSON entries and the linked GitLab issue 409346. The current EPSS score of 0.8181 with a recorded peak of 0.8436 indicates sustained exploitation interest after disclosure.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-33927
Vulnerability Data
An issue has been discovered in GitLab CE/EE affecting all versions starting from 15.11 before 15.11.7, all versions starting from 16.0 before 16.0.2. A specially crafted merge request could lead to a stored XSS on the client side which allows…
more
attackers to perform arbitrary actions on behalf of victims.
- CWE(s)
Related Threats
Likely ATT&CK TechniquesAI
Techniques this vulnerability likely enables, inferred from its description, weakness type, and attributed-actor tradecraft. Confidence is per-technique.
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
Mitigating Controls (NIST 800-53 r5) AI
Enforces validation and sanitization of merge-request content to block attacker-supplied scripts that produce the stored XSS.
Filters or encodes output when rendering merge-request data, neutralizing persistent scripts before they execute in victims' browsers.
Detects and blocks malicious code patterns introduced via crafted merge requests before they are stored and served.
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 target introduction of XSS via coding standards/testing (mostly), yet the single broad outcome leaves many specific neutralization vectors unaddressed (partial).
Patching and EOL replacement can remediate known XSS instances in libraries or frameworks (partial) but do nothing to enforce input neutralization in application code (none).
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.
Secure-coding testing and automated code-analysis tools are applied to detect improper neutralization of script-related content during web-page generation.
Knowledge exchange on emerging attack techniques and patches reduces the likelihood that cross-site scripting flaws remain unaddressed in deployed applications.
Operational indicators of compromise for web-application attacks can be incorporated into WAF or input-filtering rules, lowering the likelihood that unsanitized data reaches the browser.
Requiring language-specific secure-coding standards and automated scanning during the SDLC catches missing output encoding or improper neutralization of untrusted data before the software reaches production.
Secure-coding standards, SAST scans and removal of insecure code samples together eliminate the failure to neutralize script content that produces cross-site scripting flaws.
Webpage malware scanning and block-listing of known malicious sites reduce the likelihood that reflected or stored script payloads reach a user’s browser.