Raw vector
CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2026-33910 is a high-severity SQL Injection (CWE-89) vulnerability in Open-Emr Openemr. Its CVSS base score is 7.2 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 35th percentile by exploit likelihood (below the median); 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-2026-33910 is a SQL injection vulnerability (CWE-89) affecting OpenEMR, a free and open source electronic health records and medical practice management application. The flaw resides in the patient selection feature due to insufficient input validation and impacts versions up to and including 8.0.0.2. Published on 2026-03-25, it 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).
Authenticated attackers with high privileges (PR:H) can exploit the vulnerability remotely over the network with low attack complexity and no user interaction required. Exploitation enables high-impact compromise of confidentiality, integrity, and availability, potentially allowing arbitrary SQL query execution against the backend database.
Mitigation is available in OpenEMR version 8.0.0.3, which includes a patch addressing the input validation issue. Security practitioners should review the fixing commit at https://github.com/openemr/openemr/commit/73db3264aed253684532839380cae3b0a56c83d2, the release notes at https://github.com/openemr/openemr/releases/tag/v8_0_0_3, and the GitHub security advisory at https://github.com/openemr/openemr/security/advisories/GHSA-x32c-xj5g-7jx7 for implementation details.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-16016
Vulnerability Data
OpenEMR is a free and open source electronic health records and medical practice management application. Versions up to and including 8.0.0.2 contain a SQL injection vulnerability in the patient selection feature that can be exploited by authenticated attackers. The vulnerability…
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exists due to insufficient input validation in the patient selection feature. Version 8.0.0.3 contains a patch.
- 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.