CVE-2025-14684
Ibm Maximo Application Suite 8.10 – 8.10.26
Raw vector
CVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:U/C:N/I:L/A:NSummary
CVE-2025-14684 is a medium-severity Improper Output Neutralization for Logs (CWE-117) vulnerability in Ibm Maximo Application Suite. Its CVSS base score is 4.0 (Medium).
Operationally, ranked at the 3th 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 AU-9 (Protection of Audit Information) 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-2025-209038
Vulnerability Data
IBM Maximo Application Suite - Monitor Component 9.1, 9.0, 8.11, and 8.10 could allow an unauthorized user to inject data into log messages due to improper neutralization of special elements when written to log files.
- CWE(s)
Related Threats
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
Mitigating Controls (NIST 800-53 r5) AI
Directly requires validation and neutralization of untrusted input before it is written to log files, blocking the exact CWE-117 log-injection vector described.
Mandates cryptographic or access-control protection of audit records so that injected content from an unauthorized user cannot alter or corrupt log integrity.
Requires integrity verification mechanisms that can detect unauthorized or malformed data that has been injected into log files.
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 and coding standards directly require output sanitization for logs.
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 detect log injection flaws but does not prevent them at the source.
Logging control directly requires proper log generation and handling, which mitigates improper output neutralization.
Monitoring activities rely on trustworthy logs but do not ensure log message integrity.
Secure SDLC includes coding standards that reduce log-related weaknesses but does not specifically address logging.
Secure coding practices mandate input validation and output encoding, directly preventing log injection.