Raw vector
CVSS:3.1/AV:N/AC:L/PR:H/UI:R/S:C/C:H/I:H/A:NSummary
CVE-2022-2230 is a high-severity Cross-site Scripting (CWE-79) vulnerability in Gitlab Gitlab. Its CVSS base score is 8.1 (High).
Operationally, ranked in the top 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 the project settings page of GitLab Community Edition and Enterprise Edition. It affects all versions from 14.4 prior to 14.10.5, 15.0 prior to 15.0.4, and 15.1 prior to 15.1.1, and is tracked under CWE-79. The flaw permits an attacker to store arbitrary JavaScript that later executes in a victim's browser session.
An authenticated user with project settings privileges can inject the malicious payload, which then runs with the victim's permissions when the settings page is viewed. Successful exploitation can lead to theft of session tokens or other sensitive data and actions performed on the victim's behalf, consistent with the CVSS 8.1 rating reflecting high confidentiality and integrity impact across a changed scope.
References to GitLab's CVE repository, issue tracker, and the associated HackerOne report indicate that the issue is resolved by upgrading to the fixed releases listed in the advisory.
The EPSS score rose from a low starting value to a peak of 0.0659 on 2025-12-11 before receding to the current 0.0176, signaling that exploitation interest emerged after disclosure.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2022-34510
Vulnerability Data
A Stored Cross-Site Scripting vulnerability in the project settings page in GitLab CE/EE affecting all versions from 14.4 prior to 14.10.5, 15.0 prior to 15.0.4, and 15.1 prior to 15.1.1, allows an attacker to execute arbitrary JavaScript code in GitLab…
more
on a victim's behalf.
- 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
Directly requires validation of all inputs on the project settings page to reject or sanitize script payloads before storage.
Requires filtering of all rendered output from stored project settings so that injected JavaScript cannot execute in victim browsers.
Provides malicious-code detection and blocking mechanisms that can identify and stop execution of stored XSS payloads at the application boundary.
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.