Cyber Resilience

CVE-2025-59542

XSS in Chamilo Lms ≤ 1.11.34

Published
06 March 2026
Modified
09 March 2026
Patch / advisory
CVSS Score v3.1 9.0
Click a component to see what it means
Raw vectorCVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:H
EPSS Score 0.0030 22th percentile
Risk Priority 62 floored blend · peak EPSS

Summary

CVE-2025-59542 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 22th 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-2025-59542 is a stored cross-site scripting (XSS) vulnerability (CWE-79) in Chamilo, an open-source learning management system. It affects versions prior to 1.11.34 and occurs in the course learning path Settings field, where malicious JavaScript can be injected and persistently stored. The vulnerability carries a 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 critical potential impact due to network accessibility, low complexity, and high confidentiality, integrity, and availability consequences.

An attacker with a low-privileged account, such as a trainer, can exploit this by injecting arbitrary JavaScript into the vulnerable field. The script then executes in the browser context of any user who views the affected course information page, including administrators. This allows exfiltration of sensitive data like session cookies or tokens, enabling account takeover of higher-privileged users.

The issue has been patched in Chamilo version 1.11.34. Organizations should upgrade to this version or later to mitigate the vulnerability. Additional details are available in the GitHub release notes at https://github.com/chamilo/chamilo-lms/releases/tag/v1.11.34 and the security advisory at https://github.com/chamilo/chamilo-lms/security/advisories/GHSA-pxrh-3rcp-h7m6.

OWASP Top 10 for Web (2025)

EU & UK References

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 learning path Settings field, an attacker with a low-privileged account (e.g., trainer) can execute arbitrary…

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JavaScript code 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

T1185 Browser Session Hijacking Collection
Adversaries may take advantage of security vulnerabilities and inherent functionality in browser software to change content, modify user-behaviors, and intercept information as part of various browser session hijacking techniques.
T1539 Steal Web Session Cookie Credential Access
An adversary may steal web application or service session cookies and use them to gain access to web applications or Internet services as an authenticated user without needing credentials.
T1659 Content Injection Initial Access
Adversaries may gain access and continuously communicate with victims by injecting malicious content into systems through online network traffic.
T1189 Drive-by Compromise Initial Access
Adversaries may gain access to a system through a user visiting a website over the normal course of browsing.
T1190 Exploit Public-Facing Application Initial Access
Adversaries may attempt to exploit a weakness in an Internet-facing host or system to initially access a network.
Derived from this CVE’s CWE(s) via the direct CWE→ATT&CK cross-walk.

CVEs Like This One

CVE-2023-31807Same product: Chamilo Chamilo Lms
CVE-2023-31804Same product: Chamilo Chamilo Lms
CVE-2023-31805Same product: Chamilo Chamilo Lms
CVE-2023-31799Same product: Chamilo Chamilo Lms
CVE-2023-31800Same product: Chamilo Chamilo Lms
CVE-2023-34961Same product: Chamilo Chamilo Lms
CVE-2023-31802Same product: Chamilo Chamilo Lms
CVE-2023-31801Same product: Chamilo Chamilo Lms
CVE-2023-31806Same product: Chamilo Chamilo Lms
CVE-2023-31803Same product: Chamilo Chamilo Lms

Affected Assets

chamilo
chamilo lms
≤ 1.11.34

Mitigating Controls

Control response

Prevent
Stop it (NIST 800-53)

Detect
Catch it (NIST detect / respond)

Harden
Shrink the surface (DISA STIG)

Validate
Prove the fix (OWASP ASVS)
  • V1.1.2
  • V1.3.2

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.

PR.PS-06 mostly match
prevents

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).

PR.PS-02 partial match
prevents

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.

finds

Secure-coding testing and automated code-analysis tools are applied to detect improper neutralization of script-related content during web-page generation.

prevents

Knowledge exchange on emerging attack techniques and patches reduces the likelihood that cross-site scripting flaws remain unaddressed in deployed applications.

prevents

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.

prevents

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.

prevents

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.

none

Webpage malware scanning and block-listing of known malicious sites reduce the likelihood that reflected or stored script payloads reach a user’s browser.

References