Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:NCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2025-9222 is a high-severity Cross-site Scripting (CWE-79) vulnerability in Gitlab Gitlab. Its CVSS base score is 8.7 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 31th 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-2025-9222 is a stored cross-site scripting vulnerability (CWE-79) in GitLab Community Edition (CE) and Enterprise Edition (EE). It affects all versions from 18.2.2 prior to 18.5.5, 18.6 prior to 18.6.3, and 18.7 prior to 18.7.1. The flaw stems from inadequate sanitization in GitLab Flavored Markdown, enabling injection of malicious scripts. The vulnerability carries a CVSS v3.1 base score of 8.7 (AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:N), indicating high severity due to network accessibility, low attack complexity, and potential for cross-origin impacts.
An authenticated user with low privileges (PR:L) can exploit this over the network (AV:N) by submitting specially crafted GitLab Flavored Markdown containing XSS payloads. The attack requires user interaction (UI:R) from victims viewing the stored content, such as in issues, merge requests, or wikis. Successful exploitation executes scripts in the victim's browser context with changed scope (S:C), potentially compromising high confidentiality (C:H) and integrity (I:H) by stealing session data, performing actions on behalf of users, or escalating access within the GitLab instance.
GitLab has remediated the issue through patches, including the release of version 18.7.1. Organizations should upgrade to GitLab 18.5.5, 18.6.3, 18.7.1, or later to mitigate the vulnerability. Further details on the fix are documented in GitLab's patch release notes, the associated issue tracker entry, and the HackerOne disclosure report.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-1757
Vulnerability Data
GitLab has remediated an issue in GitLab CE/EE affecting all versions from 18.2.2 before 18.5.5, 18.6 before 18.6.3, and 18.7 before 18.7.1 that could have allowed an authenticated user to achieve stored cross-site scripting by exploiting GitLab Flavored Markdown.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.1.2V1.3.2
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can discover missing or incorrect input neutralization through targeted web-application tests.
Input validation directly enforces neutralization of untrusted data before it reaches web output generation.
Output filtering can catch or sanitize unneutralized script content before it is served to users.
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.