Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:NSummary
CVE-2022-1175 is a high-severity Cross-site Scripting (CWE-79) vulnerability in Gitlab Gitlab. Its CVSS base score is 8.7 (High).
Operationally, ranked in the top 0.4% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
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.
CVE-2022-1175 is a cross-site scripting vulnerability arising from improper neutralization of user input, tracked as CWE-79. It affects GitLab Community Edition and Enterprise Edition in versions 14.4 prior to 14.7.7, all releases from 14.8 prior to 14.8.5, and all releases from 14.9 prior to 14.9.2. The flaw permits an attacker to inject HTML into notes, which is then rendered without adequate sanitization.
An authenticated user with permission to add notes can supply crafted HTML that executes in the context of other users who view the affected content. Because the CVSS vector includes a scope change and high impact on confidentiality and integrity, successful exploitation can lead to session hijacking, privilege escalation within the GitLab instance, or theft of sensitive project data.
Public advisories and the associated GitLab security tracker entries direct administrators to upgrade to the fixed releases listed above. The referenced HackerOne report and GitLab issue tracker entries confirm that the patches restore proper input handling for note content and that no additional configuration changes are required beyond applying the updates. The EPSS score has remained flat at its peak value with no material post-disclosure rise.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2022-24516
Vulnerability Data
Improper neutralization of user input in GitLab CE/EE versions 14.4 before 14.7.7, all versions starting from 14.8 before 14.8.5, all versions starting from 14.9 before 14.9.2 allowed an attacker to exploit XSS by injecting HTML in notes.
- 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 and neutralization of untrusted input before it is rendered, which would have blocked the HTML injection into notes that enables this XSS flaw.
Mandates output filtering/encoding of user-supplied content, preventing the unsanitized HTML notes from executing as script in other users' browsers.
Requires timely installation of vendor patches that restore proper input handling, directly addressing the version-specific flaw in GitLab note rendering.
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.