CVE-2023-2164
XSS in Gitlab 15.9 – 16.0.8
Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:L/I:L/A:NSummary
CVE-2023-2164 is a medium-severity Cross-site Scripting (CWE-79) vulnerability in Gitlab Gitlab. Its CVSS base score is 5.4 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked in the top 0.8% 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.
An issue has been discovered in GitLab affecting all versions starting from 15.9 before 16.0.8, all versions starting from 16.1 before 16.1.3, and all versions starting from 16.2 before 16.2.2. The vulnerability is a stored cross-site scripting flaw (CWE-79) that can be triggered in the WebIDE beta through user interaction with a crafted URL, rated at CVSS 5.4 with network attack vector, low complexity, and required user interaction.
An authenticated attacker can supply the malicious URL to a target user; if the user engages with it inside the WebIDE beta, the stored XSS executes in the victim's browser context, enabling limited impacts on confidentiality and integrity under the reported scope change.
The referenced GitLab issue 407783 and HackerOne report 1940598 document the flaw and point to the availability of patched releases that remediate the exposure. The associated EPSS score has remained flat at its peak value of 0.5217 with no material post-disclosure rise observed.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-33681
Vulnerability Data
An issue has been discovered in GitLab affecting all versions starting from 15.9 before 16.0.8, all versions starting from 16.1 before 16.1.3, all versions starting from 16.2 before 16.2.2. It was possible for an attacker to trigger a stored XSS…
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vulnerability via user interaction with a crafted URL in the WebIDE beta.
- 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.