Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:HSummary
CVE-2025-52468 is a high-severity Cross-site Scripting (CWE-79) vulnerability in Chamilo Chamilo Lms. Its CVSS base score is 8.8 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 28th 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-2025-52468 is a stored cross-site scripting (XSS) vulnerability, classified under CWE-79, in Chamilo, an open-source learning management system. Versions prior to 1.11.30 are affected due to insufficient input sanitization when importing user data from CSV files. Attackers can inject malicious payloads into the "Last Name", "First Name", and "Username" fields, which are stored without proper validation.
The vulnerability can be exploited by an attacker who submits a crafted CSV file via the user import feature, requiring no privileges (PR:N) but user interaction (UI:R) to trigger. When an authenticated user views the profile of the injected user, the XSS payload executes in the victim's browser context, potentially compromising confidentiality, integrity, and availability with high impact (CVSS v3.1 score: 8.8, AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H).
Chamilo has patched the issue in version 1.11.30. Administrators should upgrade to this version immediately. Official resources include the fixing commit at https://github.com/chamilo/chamilo-lms/commit/790ef513aceacae6fe5b6641145901f04c7992dd, the release notes at https://github.com/chamilo/chamilo-lms/releases/tag/v1.11.30, and the GitHub security advisory at https://github.com/chamilo/chamilo-lms/security/advisories/GHSA-hc3c-8p55-xh4r.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-208173
Vulnerability Data
Chamilo is a learning management system. Prior to version 1.11.30, an input validation vulnerability exists when importing user data from CSV files. This flaw occurs due to insufficient sanitization of user data, specifically in the "Last Name", "First Name", and…
more
"Username" fields. It allows attackers to inject a stored cross-site scripting (XSS) payload that is triggered when the user profile is viewed, potentially leading to malicious script execution in the context of the authenticated use. This issue has been patched in version 1.11.30.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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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.