Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:HCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2025-59543 is a critical-severity Cross-site Scripting (CWE-79) vulnerability in Chamilo Chamilo Lms. Its CVSS base score is 9.0 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 16th 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 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-2025-59543 is a stored cross-site scripting (XSS) vulnerability, classified under CWE-79, affecting Chamilo, an open-source learning management system (LMS). The flaw exists in versions prior to 1.11.34, specifically within the course description field, where insufficient input sanitization allows the injection of malicious JavaScript code. This issue carries a high CVSS v3.1 base score of 9.0 (AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:H), reflecting its potential for significant impact across confidentiality, integrity, and availability.
An attacker with a low-privileged account, such as a trainer, can exploit this vulnerability by injecting malicious JavaScript into the course description field. When any other user, including administrators, views the course information page, the script executes in their browser context. This enables the theft of sensitive data like session cookies or authentication tokens, potentially leading to account takeover (ATO) of higher-privileged users.
The vulnerability has been addressed in Chamilo version 1.11.34, as detailed in the official release notes and a corresponding GitHub security advisory (GHSA-p32q-6gh3-3gcv). Security practitioners should prioritize upgrading to the patched version and review existing courses for injected payloads, while implementing content security policies (CSP) to further mitigate XSS risks in Chamilo deployments.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-208336
Vulnerability Data
Chamilo is a learning management system. Prior to version 1.11.34, there is a stored cross-site scripting (XSS) vulnerability. By injecting malicious JavaScript into the course description field, an attacker with a low-privileged account (e.g., trainer) can execute arbitrary JavaScript code…
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in the context of any other user viewing the course information page, including administrators. This allows an attacker to exfiltrate sensitive session cookies or tokens, resulting in account takeover (ATO) of higher-privileged users. This issue has been patched in version 1.11.34.
- 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.