Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:NSummary
CVE-2023-0050 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 in the top 0.2% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
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-2023-0050 is a stored cross-site scripting vulnerability in GitLab, present in all versions from 13.7 through 15.7.7, 15.8 through 15.8.3, and 15.9 through 15.9.1. The flaw resides in the handling of Kroki diagrams, where a specially crafted diagram can be stored and later rendered to execute arbitrary script in a victim's browser session.
An authenticated attacker with permission to create or edit content containing Kroki diagrams can upload a malicious payload that persists in the application. When another user views the affected page, the script executes in the victim's context, enabling actions such as account takeover or unauthorized data access on their behalf. The CVSS 8.7 score reflects network attack vector, low complexity, and the requirement for some user interaction combined with changed scope.
GitLab advisories and the associated issue tracker entries direct administrators to upgrade to the fixed releases 15.7.8, 15.8.4, or 15.9.2, which sanitize Kroki diagram input before storage and rendering. No additional configuration changes are specified beyond applying the patches.
The EPSS score rose from a low baseline after disclosure to a peak of 0.7828 in December 2025 before receding to the current value of 0.5651, indicating that exploitation interest increased well after the initial publication.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-12153
Vulnerability Data
An issue has been discovered in GitLab affecting all versions starting from 13.7 before 15.7.8, all versions starting from 15.8 before 15.8.4, all versions starting from 15.9 before 15.9.2. A specially crafted Kroki diagram could lead to a stored XSS…
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on the client side which allows attackers to perform arbitrary actions on behalf of victims.
- 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
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Penetration testing submits XSS payloads to web applications, detecting cross-site scripting flaws for subsequent remediation.
Validates web inputs to reject script-related content that could produce XSS.
Output validation against expected content can reject or sanitize script content in generated web pages, reducing XSS exploitability.
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.