Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:L/VI:H/VA:N/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:XCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2026-33714 is a high-severity SQL Injection (CWE-89) vulnerability in Chamilo Chamilo Lms. Its CVSS base score is 7.1 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 17th 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.
Chamilo, an open-source learning management system (LMS), is affected by CVE-2026-33714, a SQL injection vulnerability in version 2.0.0-RC.2. The flaw resides in the statistics AJAX endpoint within the file public/main/inc/ajax/statistics.ajax.php, specifically the users_active action. This issue stems from an incomplete fix for CVE-2026-30881, where the date_start and date_end parameters were sanitized using Security::remove_XSS() for the get_user_registration_by_month action but left unsanitized in users_active, allowing direct interpolation into a SQL query. The vulnerability carries a CVSS v3.1 base score of 7.2 (AV:N/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:H) and is classified under CWE-89.
An authenticated administrator can exploit this vulnerability remotely over the network with low complexity. By manipulating the unsanitized date_start and date_end parameters, attackers can perform time-based blind SQL injection, enabling the extraction of arbitrary data from the database.
The Chamilo security advisory (GHSA-w8c4-c7r8-qgw2) and release notes for version 2.0.0 confirm that the issue has been addressed in that release, recommending immediate upgrades from 2.0.0-RC.2 to mitigate the risk.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-22708
Vulnerability Data
Chamilo is an open-source learning management system (LMS). Version 2.0.0-RC.2 contains a SQL Injection vulnerability in the statistics AJAX endpoint, which is an incomplete fix for CVE-2026-30881. While CVE-2026-30881 was patched by applying Security::remove_XSS() to the date_start and date_end parameters…
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in the get_user_registration_by_month action, the same parameters remain unsanitized in the users_active action within the same file (public/main/inc/ajax/statistics.ajax.php), where they are directly interpolated into a SQL query. An authenticated admin can exploit this to perform time-based blind SQL injection, enabling extraction of arbitrary data from the database. This issue has been fixed in version 2.0.0.
- 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.