Raw vector
CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:H/I:N/A:NSummary
CVE-2026-37505 is a medium-severity SQL Injection (CWE-89) vulnerability in V2Board V2Board. Its CVSS base score is 4.9 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Databases (T1213.006); ranked at the 16th 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 SI-10 (Information Input Validation) and AC-6 (Least Privilege) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-26669
Vulnerability Data
SQL Injection via ORDER BY clause in V2Board thru 1.7.4. In app/Http/Controllers/Admin/UserController.php, the sort parameter from user input is passed directly to User::orderBy($sort, $sortType) without validation. An authenticated admin can sort users by any database column including password, remember_token, and…
more
other sensitive fields, enabling information disclosure through ordering analysis.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Authenticated SQL injection in ORDER BY enables direct access and inference of sensitive database fields (passwords, tokens) via information disclosure.
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
Mitigating Controls (NIST 800-53 r5) AI
Directly requires validation of the user-supplied sort parameter before it is used in the orderBy() call, blocking the ORDER BY injection.
Restricts the set of database columns an admin account is permitted to reference in queries, limiting exposure of sensitive fields such as password and remember_token.
Enables monitoring and alerting on anomalous ORDER BY clauses or repeated sorting on sensitive columns that would indicate exploitation attempts.
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.