Cyber Resilience

CVE-2025-52468

XSS in Chamilo Lms ≤ 1.11.30

Public PoCXSS
Published
02 March 2026
Modified
03 March 2026
Patch / advisory
CVSS Score v3.1 8.8
Click a component to see what it means
Raw vectorCVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
EPSS Score 0.0035 28th percentile
Risk Priority 63 floored blend · peak EPSS

Summary

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

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

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
≤ 1.11.30

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