Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:N/VA:H/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:XSummary
CVE-2025-50189 is a high-severity SQL Injection (CWE-89) vulnerability in Chamilo Chamilo Lms. Its CVSS base score is 7.2 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 49% of CVEs by exploit likelihood; 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-2025-50189 is a SQL injection vulnerability in Chamilo, an open-source learning management system. Prior to version 1.11.30, the application fails to sufficiently validate user-supplied data in POST parameters such as resource[document] and login within the /main/coursecopy/copy_course_session_selected.php endpoint. This flaw, classified under CWE-89, enables attackers to inject arbitrary SQL statements that alter database query logic. The vulnerability carries a CVSS v3.1 base score of 8.8 (AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H), indicating high severity due to its potential for confidentially, integrity, and availability impacts.
An authenticated attacker with low privileges, such as a registered user, can exploit this vulnerability remotely over the network with low complexity and no user interaction required. By crafting malicious POST requests to the affected endpoint, the attacker can manipulate SQL queries to achieve outcomes like data exfiltration, modification, or deletion, depending on database permissions and structure.
The issue has been addressed in Chamilo version 1.11.30, as detailed in the project's GitHub security advisory (GHSA-vxx3-648j-7p4r) and release notes. Mitigation involves upgrading to the patched version, with relevant fixes implemented in commits such as 22bb81df8f7062da20a2f6248789f47b221ca705, 75ab03c938adc48a3cd8234d98fc340e1998aa81, and 7903cef2eb41817c11a52ba6ac34a1d454bc5ef7. Security practitioners should review access controls on the course copy functionality and apply input sanitization as interim measures if immediate patching is not feasible.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-208158
Vulnerability Data
Chamilo is a learning management system. Prior to version 1.11.30, the application performs insufficient validation of data coming from the user from the POST resource[document][SQL_INJECTION_HERE] and POST login parameters found in /main/coursecopy/copy_course_session_selected.php, which allows an attacker to perform an attack…
more
aimed at modifying the database query logic by injecting an arbitrary SQL statements. 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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V6.2.5
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can discover SQLi flaws before deployment but does not stop their introduction.
Input validation directly stops untrusted data from reaching SQL query construction without neutralization.
Secure engineering principles require parameterized queries and input sanitization that structurally eliminate SQLi.
System monitoring can identify attempted SQLi exploitation via anomalous queries after the weakness exists.
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 injection flaws during coding and review so largely prevent CWE-89 introduction, yet the single broad outcome leaves residual risk from incomplete neutralization techniques or missed edge cases.
Training raises developer awareness of SQLi risks and can reduce introduction likelihood (partial) but removes none of the actual coding flaw's risk by itself since technical neutralization is still required.
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.
The same secure-coding and static-analysis activities surface missing neutralization of SQL metacharacters before the system is accepted.
Early warnings and shared best-practice information help organizations apply the latest remediation techniques against SQL-injection vulnerabilities.
Threat-intelligence feeds that surface new SQL-injection campaigns enable rapid updates to query-construction defenses and detection signatures before exploitation occurs.
Secure-coding rules and security testing phases mandate the use of parameterized queries or equivalent escaping, preventing the construction of dynamic SQL statements from untrusted input.
Language-specific secure coding rules, peer review and SAST together prevent the construction of SQL statements from untrusted data without proper parameterization or escaping.