CVE-2025-40801 (GCVE-0-2025-40801)
Vulnerability from cvelistv5 – Published: 2025-12-09 10:44 – Updated: 2026-03-10 16:07
VLAI
EPSS
VEX
Summary
A vulnerability has been identified in COMOS V10.6 (All versions < V10.6.1), COMOS V10.6 (All versions < V10.6.1), JT Bi-Directional Translator for STEP (All versions), NX V2412 (All versions < V2412.8900 with Cloud Entitlement (bundled as NX X)), NX V2506 (All versions < V2506.6000 with Cloud Entitlement (bundled as NX X)), Simcenter 3D (All versions < V2506.6000 with Cloud Entitlement (bundled as Simcenter X Mechanical)), Simcenter Femap (All versions < V2506.0002 with Cloud Entitlement (bundled as Simcenter X Mechanical)), Simcenter Studio (All versions < V2506.0001), Simcenter System Architect (All versions < V2506.0001), Tecnomatix Plant Simulation (All versions < V2504.0007). The SALT SDK is missing server certificate validation while establishing TLS connections to the authorization server. This could allow an attacker to perform a man-in-the-middle attack.
Severity
8.1 (High)
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-12-09 14:26 UTC
CWE
- CWE-295 - Improper Certificate Validation
Assigner
References
Impacted products
9 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Siemens | COMOS V10.6 |
Affected:
0 , < V10.6.1
(custom)
|
guessed | |
| Siemens | JT Bi-Directional Translator for STEP |
Affected:
0 , < *
(custom)
|
guessed | |
| Siemens | NX V2412 |
Affected:
0 , < V2412.8900
(custom)
|
guessed | |
| Siemens | NX V2506 |
Affected:
0 , < V2506.6000
(custom)
|
guessed | |
| Siemens | Simcenter 3D |
Affected:
0 , < V2506.6000
(custom)
|
guessed | |
| Siemens | Simcenter Femap |
Affected:
0 , < V2506.0002
(custom)
|
guessed | |
| Siemens | Simcenter Studio |
Affected:
0 , < V2506.0001
(custom)
|
guessed | |
| Siemens | Simcenter System Architect |
Affected:
0 , < V2506.0001
(custom)
|
guessed | |
| Siemens | Tecnomatix Plant Simulation |
Affected:
0 , < V2504.0007
(custom)
|
guessed |
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}
],
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},
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}
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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