Raw vector
CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:C/C:L/I:L/A:NSummary
CVE-2023-2015 is a medium-severity Cross-site Scripting (CWE-79) vulnerability in Gitlab Gitlab. Its CVSS base score is 4.4 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 46th percentile by exploit likelihood (below the median); 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.
A reflected cross-site scripting vulnerability exists in GitLab Community Edition and Enterprise Edition when users create new abuse reports. The flaw affects all versions from 15.8 prior to 15.10.8, from 15.11 prior to 15.11.7, and from 16.0 prior to 16.0.2, and is tracked under CWE-79 with a CVSS 3.1 score of 4.4.
An attacker with low-privileged access can supply a crafted payload during abuse-report creation that executes in the context of another user who views the report. Successful exploitation allows the attacker to perform arbitrary actions on behalf of the victim, such as modifying account settings or initiating other privileged operations within the GitLab instance.
GitLab has published patches that remediate the issue in the fixed releases listed above; administrators are advised to upgrade affected instances promptly. Public references, including the official CVE record and associated GitLab issue tracker entries, contain the version-specific remediation details.
EPSS for this CVE rose from a low baseline to a peak of 0.0936 before settling at the current value of 0.0605, indicating measurable post-disclosure exploitation interest that warrants renewed attention from defenders.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-33544
Vulnerability Data
An issue has been discovered in GitLab CE/EE affecting all versions starting from 15.8 before 15.10.8, all versions starting from 15.11 before 15.11.7, all versions starting from 16.0 before 16.0.2. A reflected XSS was possible when creating new abuse reports…
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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.