Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/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-2026-28430 is a critical-severity SQL Injection (CWE-89) vulnerability in Chamilo Chamilo Lms. Its CVSS base score is 9.3 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 25th 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.
CVE-2026-28430 is an unauthenticated SQL injection vulnerability (CWE-89) affecting Chamilo LMS, an open-source learning management system, in versions prior to 1.11.34. The flaw resides in the handling of the custom_dates parameter, enabling remote attackers to execute arbitrary SQL commands against the backend database. With a CVSS v3.1 base score of 9.8 (AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H), it poses a critical risk due to its high confidentiality, integrity, and availability impacts.
Unauthenticated attackers can exploit this vulnerability remotely over the network with low complexity and no privileges required. Successful exploitation allows arbitrary SQL execution, potentially dumping the entire database—including personally identifiable information (PII) and system configurations. By chaining the SQL injection with a predictable legacy password reset mechanism, attackers can achieve full administrative account takeover without any prior credentials, granting complete control over the LMS instance.
The vulnerability has been addressed in Chamilo LMS version 1.11.34, as detailed in the project's GitHub release notes (https://github.com/chamilo/chamilo-lms/releases/tag/v1.11.34) and security advisory (https://github.com/chamilo/chamilo-lms/security/advisories/GHSA-84gw-qjw9-v8jv). Security practitioners should prioritize upgrading to the patched version and review database access logs for signs of exploitation.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-12492
Vulnerability Data
Chamilo LMS is a learning management system. Prior to version 1.11.34, there is an unauthenticated SQL injection vulnerability which allows remote attackers to execute arbitrary SQL commands via the custom_dates parameter. By chaining this with a predictable legacy password reset…
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mechanism, an attacker can achieve full administrative account takeover without any prior credentials. The vulnerability also exposes the entire database, including PII and system configurations. This issue has been patched in version 1.11.34.
- 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.