Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:NSummary
CVE-2026-30882 is a medium-severity Cross-site Scripting (CWE-79) vulnerability in Chamilo Chamilo Lms. 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-2026-12514
Vulnerability Data
Chamilo LMS is a learning management system. Chamilo LMS version 1.11.34 and prior contains a Reflected Cross-Site Scripting (XSS) vulnerability in the session category listing page. The keyword parameter from $_REQUEST is echoed directly into an HTML href attribute without…
more
any encoding or sanitization. An attacker can inject arbitrary HTML/JavaScript by breaking out of the attribute context using ">followed by a malicious payload. The vulnerability is triggered when the pagination controls are rendered — which occurs when the number of session categories exceeds 20 (the page limit). This issue has been patched in version 1.11.36.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Reflected XSS directly enables arbitrary JavaScript execution in victim browsers via unsanitized input in web page generation.
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
Mitigating Controls (NIST 800-53 r5) AI
Directly requires validation and sanitization of untrusted input (the keyword parameter) before it is placed into web output, preventing the reflected XSS payload from reaching the href attribute.
Requires the system to filter untrusted data from output, which would neutralize the unsanitized keyword value echoed into HTML attributes on the session category pagination page.
Enables monitoring and analysis of web requests and responses that could reveal anomalous reflected-XSS attempts or successful script injection when >20 categories trigger pagination.
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.