Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:NSummary
CVE-2022-27926 is a medium-severity Cross-site Scripting (CWE-79) vulnerability in Synacor Zimbra Collaboration Suite. Its CVSS base score is 6.1 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked in the top 3% of CVEs by exploit likelihood; CISA has added it to the Known Exploited Vulnerabilities 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 the /public/launchNewWindow.jsp component of Zimbra Collaboration (ZCS) version 9.0. The flaw, tracked as CVE-2022-27926 and assigned CWE-79, permits injection of arbitrary web script or HTML through request parameters and carries a CVSS 3.1 score of 6.1 reflecting network attack vector, low complexity, no required privileges, and required user interaction with changed scope.
Unauthenticated remote attackers can exploit the issue by supplying a crafted URL that reflects malicious content back to a victim who follows the link. Successful exploitation allows execution of attacker-controlled script in the context of the Zimbra application, enabling theft of session tokens, redirection to malicious sites, or other client-side actions limited to the confidentiality and integrity impacts described in the vector.
Zimbra security advisories and release notes for version 9.0.0/P24, published on the vendor wiki, identify the affected component and direct administrators to apply the corresponding patch to eliminate the reflected XSS vector.
The associated EPSS reaches a peak of 0.9620 with a current value of 0.9413, indicating sustained and elevated exploitation probability after disclosure.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2022-32414
Vulnerability Data
A reflected cross-site scripting (XSS) vulnerability in the /public/launchNewWindow.jsp component of Zimbra Collaboration (aka ZCS) 9.0 allows unauthenticated attackers to execute arbitrary web script or HTML via request parameters.
- CWE(s)
- KEV Date Added
- 03 April 2023
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.