Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:L/I:L/A:NSummary
CVE-2026-47694 is a medium-severity Cross-site Scripting (CWE-79) vulnerability in Wwbn Avideo. Its CVSS base score is 5.4 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 6th percentile by exploit likelihood (below the median); 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.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-33304
Vulnerability Data
WWBN AVideo is an open source video platform. In 29.0 and earlier, AVideo stores category descriptions from user input and later renders category_description as raw HTML in the Gallery view. A user who can create or edit categories can store…
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JavaScript in a category description, which executes when another user views the affected Gallery/category page. This is a stored XSS in the category description field, separate from previously fixed XSS issues in video titles or comments.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Stored XSS directly enables browser session hijacking via injected scripts in user views.
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
Mitigating Controls (NIST 800-53 r5) AI
Requires validation and sanitization of all user-supplied input (category descriptions) before storage or use, directly blocking the malicious script that produces this stored XSS.
Mandates filtering or encoding of information on output (the raw category_description rendered in Gallery view), neutralizing script content before it reaches other users' browsers.
Provides integrity verification of stored information, enabling detection of unauthorized script insertion into category descriptions that would otherwise be rendered verbatim.
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.