Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:N/A:NSummary
CVE-2026-31941 is a high-severity SSRF (CWE-918) vulnerability in Chamilo Chamilo Lms. Its CVSS base score is 7.7 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 14th 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 AC-4 (Information Flow Enforcement) 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-2026-31941 is a Server-Side Request Forgery (SSRF) vulnerability, classified under CWE-918, affecting Chamilo LMS, an open-source learning management system. The issue resides in the Social Wall feature, specifically the read_url_with_open_graph endpoint, which accepts a user-supplied URL via the social_wall_new_msg_main POST parameter and issues two server-side HTTP requests to that URL without validating whether it points to an internal or external resource. Chamilo LMS versions prior to 1.11.38 and 2.0.0-RC.3 are vulnerable, earning a CVSS v3.1 base score of 7.7 (AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:N/A:N).
An authenticated attacker with low privileges can exploit this vulnerability remotely with low complexity and no user interaction required. By supplying a malicious URL, they can compel the server to originate arbitrary HTTP requests, enabling port scanning of internal networks, access to internal services, and retrieval of sensitive data such as cloud instance metadata endpoints (e.g., on AWS, Azure, or GCP).
The vulnerability is addressed in Chamilo LMS versions 1.11.38 and 2.0.0-RC.3 through fixes documented in GitHub commits e3790c5f0ff3b4dc547c2099fadf5c438c1bb265 and ea6b7b7e90580c9b01dc4bcafe4ad737061e0ead, with further details in the security advisory at GHSA-q74c-mx8x-489h. Security practitioners should upgrade to these patched versions and review access controls on the Social Wall feature to mitigate exposure.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-21523
Vulnerability Data
Chamilo LMS is a learning management system. Prior to 1.11.38 and 2.0.0-RC.3, Chamilo LMS contains a Server-Side Request Forgery (SSRF) vulnerability in the Social Wall feature. The endpoint read_url_with_open_graph accepts a URL from the user via the social_wall_new_msg_main POST parameter…
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and performs two server-side HTTP requests to that URL without validating whether the target is an internal or external resource. This allows an authenticated attacker to force the server to make arbitrary HTTP requests to internal services, scan internal ports, and access cloud instance metadata. This vulnerability is fixed in 1.11.38 and 2.0.0-RC.3.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.3.6V1.5.3V5.3.2V10.4.7
Mitigating Controls (NIST 800-53 r5) AI
Information flow enforcement can restrict which destinations the server is allowed to contact on behalf of users.
Input validation directly stops untrusted URLs from being accepted and fetched without destination checks.
Boundary protection limits the network reach of server-initiated requests even if SSRF occurs.
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 development practices directly include input validation and destination allow-listing that prevent SSRF.
Runtime monitoring of web applications and services can detect anomalous outbound requests indicative of SSRF.
Vulnerability identification processes can discover and record SSRF flaws in web applications.
Network segmentation and egress controls can limit the damage from successful SSRF requests.
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.
Operational threat data describing SSRF campaigns can be used to tighten outbound-request allow-lists and detection rules before attackers exploit them.