Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:U/C:H/I:H/A:NSummary
CVE-2026-24672 is a high-severity Cross-site Scripting (CWE-79) vulnerability in Gunet Open Eclass Platform. Its CVSS base score is 7.3 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 8th 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 SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — 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-2026-24672 is a Stored Cross-Site Scripting (XSS) vulnerability, classified under CWE-79, in the Open eClass platform (formerly known as GUnet eClass), a complete course management system. It affects versions prior to 4.2, specifically in user profile fields that fail to sanitize input properly, allowing injected JavaScript to persist and execute.
Authenticated students can exploit this vulnerability by injecting malicious JavaScript into their own user profile fields. The payload executes in the browsers of users with viewing privileges when they access affected application pages, potentially leading to high confidentiality and integrity impacts such as session hijacking, data theft, or page manipulation. The CVSS v3.1 base score of 7.3 (AV:N/AC:L/PR:L/UI:R/S:U/C:H/I:H/A:N) indicates network accessibility, low attack complexity, low privileges required, and user interaction needed, with no availability disruption.
The issue has been patched in Open eClass version 4.2. Additional details on the vulnerability and mitigation are available in the GitHub security advisory at https://github.com/gunet/openeclass/security/advisories/GHSA-3p2x-qgxw-qvxh.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-5237
Vulnerability Data
The Open eClass platform (formerly known as GUnet eClass) is a complete course management system. Prior to version 4.2, a Stored Cross-Site Scripting (XSS) vulnerability allows authenticated students to inject malicious JavaScript into user profile fields, which is executed when…
more
users with viewing privileges access affected application pages. This issue has been patched in version 4.2.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
—
—
—
V1.1.2V1.3.2
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can discover missing or incorrect input neutralization through targeted web-application tests.
Input validation directly enforces neutralization of untrusted data before it reaches web output generation.
Output filtering can catch or sanitize unneutralized script content before it is served to users.
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.