CVE-2025-37105
Hpe Autopass License Server ≤ 9.18
Raw vector
CVSS:3.1/AV:A/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2025-37105 is a high-severity Code Injection (CWE-94) vulnerability in Hpe Autopass License Server. Its CVSS base score is 7.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Command and Scripting Interpreter (T1059); ranked at the 46th 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.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2025-37105 is an hsqldb-related remote code execution vulnerability affecting HPE AutoPass License Server (APLS) versions prior to 9.18. Classified under CWE-94 (code injection), it carries a CVSS v3.1 base score of 7.5 (AV:A/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H) and was published on 2025-07-16.
The vulnerability can be exploited by an attacker with adjacent network access who performs a high-complexity attack without requiring privileges or user interaction. Successful exploitation enables remote code execution, resulting in high impacts to confidentiality, integrity, and availability on the affected APLS instance.
The HPE security bulletin at https://support.hpe.com/hpesc/public/docDisplay?docId=hpesbgn04877en_us details mitigation steps, including upgrading to APLS version 9.18 or later to address the hsqldb-related issue.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-21734
Vulnerability Data
An hsqldb-related remote code execution vulnerability exists in HPE AutoPass License Server (APLS) prior to 9.18.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.3.1
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation finds code paths that accept and execute externally influenced strings.
Input validation directly stops untrusted data from being used to construct executable code without neutralization.
Least privilege limits the damage an injected code fragment can perform once executed.
Requiring documented secure development standards and tools enforces use of safe code-generation APIs and escaping.
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.
PR.PS-06's SDLC practices directly target injection flaws via secure coding and testing (mostly), yet as a single broad outcome it leaves many code-generation specifics unaddressed (partial).
PR.DS-10 protects runtime data confidentiality/integrity but has no bearing on neutralizing externally influenced input during code generation, so neither direction shows any preventive effect.
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.
Banning unapproved code samples and unauthenticated web services, combined with secure-coding standards and SAST, prevents the dynamic generation or inclusion of attacker-supplied code.
Controls that restrict unauthorized or malicious code from being introduced via external networks or removable media limit opportunities for an attacker to inject and execute arbitrary code.