Raw vector
CVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:U/C:L/I:N/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-2023-40725 is a medium-severity Generation of Error Message Containing Sensitive Information (CWE-209) vulnerability in Siemens Qms Automotive. Its CVSS base score is 4.0 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Protocol or Service Impersonation (T1001.003); ranked at the 8th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-45279
Vulnerability Data
A vulnerability has been identified in QMS Automotive (All versions < V12.39). The affected application returns inconsistent error messages in response to invalid user credentials during login session. This allows an attacker to enumerate usernames, and identify valid usernames.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V16.5.1
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Detects error messages that leak sensitive information as evidence of disclosure.
The control directly mitigates generation of error messages containing sensitive authentication details by requiring obscured feedback instead of verbose responses.
Misdirection allows generation of misleading error messages that withhold or falsify sensitive details.
Explicitly requires error messages to avoid including sensitive or exploitable details while still supporting corrective action.
Validation ensures error messages contain only expected, non-sensitive content and blocks leakage via verbose errors.
Fail-safe procedures can be defined to suppress or sanitize error output, reducing generation of messages that contain sensitive information.
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 sanitized error handling to prevent sensitive data disclosure.
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.