CVE-2024-37387
Raw vector
CVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:U/C:N/I:L/A:NSummary
CVE-2024-37387 is a medium-severity Use of Potentially Dangerous Function (CWE-676) vulnerability in Jvn (inferred from references). Its CVSS base score is 4.0 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploitation for Privilege Escalation (T1068); ranked at the 6th 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 SA-15 (Development Process, Standards, and Tools) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-36629
Vulnerability Data
Use of potentially dangerous function issue exists in Ricoh Streamline NX PC Client. If this vulnerability is exploited, files in the PC where the product is installed may be altered.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and code analysis directly locate calls to unsafe functions.
Documented standards and tools can explicitly disallow dangerous functions during development.
Engineering principles can mandate safe APIs and ban dangerous functions at design time.
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 avoiding or safely wrapping dangerous functions during development.
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 can surface instances of dangerous functions but does not prevent their introduction.
Developer security awareness training can teach safe alternatives to risky functions.
Secure SDLC processes include code review and static analysis that can detect use of risky functions.
Secure coding standards explicitly ban or restrict dangerous functions such as strcpy, gets, or system.
Controlled software installation reduces exposure to unsafe third-party libraries that may contain dangerous calls.