Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2026-30881 is a high-severity SQL Injection (CWE-89) vulnerability in Chamilo Chamilo Lms. Its CVSS base score is 8.8 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 20th 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 LMS, an open-source learning management system, versions 1.11.34 and prior are affected by CVE-2026-30881, a SQL injection vulnerability (CWE-89) in the statistics AJAX endpoint. The date_start and date_end parameters from $_REQUEST are embedded directly into raw SQL strings without proper sanitization. While Database::escape_string() is called downstream, its output is immediately undermined by str_replace("\'", "'"), which restores injected single quotes and fully bypasses the escaping mechanism.
An authenticated attacker with low privileges (PR:L) can exploit this vulnerability remotely (AV:N) with low attack complexity (AC:L) and no user interaction (UI:N), without changing the scope (S:U). Successful exploitation allows injection of arbitrary SQL statements into database queries, enabling blind time-based and conditional data extraction, with high impacts on confidentiality, integrity, and availability (CVSS 8.8, CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H).
The issue has been patched in Chamilo LMS version 1.11.36. Security practitioners should upgrade to this version immediately. Additional details are available in the GitHub release notes at https://github.com/chamilo/chamilo-lms/releases/tag/v1.11.36 and the security advisory at https://github.com/chamilo/chamilo-lms/security/advisories/GHSA-5ggx-x2cv-4h44.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-12500
Vulnerability Data
Chamilo LMS is a learning management system. Version 1.11.34 and prior contains a SQL Injection vulnerability in the statistics AJAX endpoint. The parameters date_start and date_end from $_REQUEST are embedded directly into a raw SQL string without proper sanitization. Although…
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Database::escape_string() is called downstream, its output is immediately neutralized by str_replace("\'", "'", ...), which restores any injected single quotes — effectively bypassing the escaping mechanism entirely. This allows an authenticated attacker to inject arbitrary SQL statements into the database query, enabling blind time-based and conditional data extraction. This issue has been patched in version 1.11.36.
- 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.