Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:NSummary
CVE-2025-68387 is a medium-severity Cross-site Scripting (CWE-79) vulnerability in Elastic Kibana. Its CVSS base score is 6.1 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique JavaScript (T1059.007); ranked at the 9th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV 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.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-204409
Vulnerability Data
Improper neutralization of input during web page generation ('Cross-site Scripting') (CWE-79) allows an unauthenticated user to embed a malicious script in content that will be served to web browsers causing cross-site scripting (XSS) (CAPEC-63) via a vulnerability a function handler…
more
in the Vega AST evaluator.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
CWE-79 XSS in a public-facing web app (Vega AST) directly enables arbitrary JavaScript execution in victim browsers (T1059.007) and exploitation of a public-facing application (T1190).
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 used in web page generation, blocking the CWE-79 XSS payload at the Vega AST handler.
Requires filtering/sanitization of information output to browsers, preventing malicious script execution even if input reaches the page renderer.
Enables continuous monitoring and anomaly detection for script injection attempts or unexpected DOM changes indicative of successful XSS.
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.