Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:HSummary
CVE-2023-34192 is a critical-severity Cross-site Scripting (CWE-79) vulnerability in Synacor Zimbra Collaboration Suite. Its CVSS base score is 9.0 (Critical).
Operationally, ranked in the top 0.5% of CVEs by exploit likelihood; CISA has added it to the Known Exploited Vulnerabilities catalog.
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-2023-34192 is a cross-site scripting vulnerability (CWE-79) affecting Zimbra Collaboration Suite (ZCS) version 8.8.15. The flaw resides in the /h/autoSaveDraft function and permits injection of arbitrary scripts that execute in the context of other users.
A remote attacker who already possesses a valid account can supply a crafted payload to the affected endpoint. Successful exploitation yields arbitrary code execution with confidentiality, integrity, and availability impacts rated high under CVSS 3.1; the changed scope metric indicates the attack can affect resources beyond the initial component.
Zimbra publishes security advisories and patches through its Security Center and responsible-disclosure pages; administrators should apply the vendor-supplied updates referenced in those advisories to remediate the issue. The associated EPSS score remains elevated near 0.90, indicating sustained exploitation interest following disclosure.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-38291
Vulnerability Data
Cross Site Scripting vulnerability in Zimbra ZCS v.8.8.15 allows a remote authenticated attacker to execute arbitrary code via a crafted script to the /h/autoSaveDraft function.
- CWE(s)
- KEV Date Added
- 25 February 2025
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 blocks the crafted script payload supplied to /h/autoSaveDraft by enforcing validation and sanitization of untrusted web input.
Filters or encodes dynamic content returned from the autoSaveDraft function so that injected scripts cannot execute in other users' browsers.
Requires prompt application of the vendor patch that eliminates the XSS flaw in the affected ZCS endpoint.
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.