CVE-2024-35150
Ibm Maximo Application Suite 8.10.12 – 8.10.15
Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:L/A:NCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2024-35150 is a medium-severity Improper Output Neutralization for Logs (CWE-117) vulnerability in Ibm Maximo Application Suite. Its CVSS base score is 5.3 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Indicator Removal (T1070); ranked at the 20th 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 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-2024-35549
Vulnerability Data
IBM Maximo Application Suite 8.10.12, 8.11.0, 9.0.1, and 9.1.0 - Monitor Component does not neutralize output that is written to logs, which could allow an attacker to inject false log entries.
- 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 evaluation can discover missing or incorrect output neutralization when log messages are constructed from untrusted input.
Input validation reduces the chance that specially crafted data reaches log-message construction routines.
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.