Raw vector
CVSS:3.1/AV:L/AC:H/PR:L/UI:R/S:U/C:L/I:L/A:LCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2024-40637 is a medium-severity Injection (CWE-74) vulnerability in Getdbt Dbt Core. Its CVSS base score is 4.2 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 30th 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.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-0033
Vulnerability Data
dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications. When a user installs a package in dbt, it has the ability to override macros, materializations, and other core…
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components of dbt. This is by design, as it allows packages to extend and customize dbt's functionality. However, this also means that a malicious package could potentially override these components with harmful code. This issue has been fixed in versions 1.8.0, 1.6.14 and 1.7.14. Users are advised to upgrade. There are no kn own workarounds for this vulnerability. Users updating to either 1.6.14 or 1.7.14 will need to set `flags.require_explicit_package_overrides_for_builtin_materializations: False` in their configuration in `dbt_project.yml`.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can discover SQLi flaws before deployment but does not stop their introduction.
SI-10 directly requires validation of information inputs to reject malformed or special-element content before it reaches downstream parsers.
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 require input validation and output encoding that prevent injection flaws.
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.
Security testing in development catches injection vulnerabilities before release.
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.
Logging supports detection of injection attempts but does not prevent the weakness.
Monitoring activities can identify active injection attacks after they occur.
Secure development life cycle mandates input validation and output encoding that directly prevent injection flaws.