CVE-2022-42268 (GCVE-0-2022-42268)
Vulnerability from cvelistv5 – Published: 2023-01-12 19:38 – Updated: 2025-04-08 13:27
VLAI
EPSS
VEX
Summary
Omniverse Kit contains a vulnerability in the reference applications Create, Audio2Face, Isaac Sim, View, Code, and Machinima. These applications allow executable Python code to be embedded in Universal Scene Description (USD) files to customize all aspects of a scene. If a user opens a USD file that contains embedded Python code in one of these applications, the embedded Python code automatically runs with the privileges of the user who opened the file. As a result, an unprivileged remote attacker could craft a USD file containing malicious Python code and persuade a local user to open the file, which may lead to information disclosure, data tampering, and denial of service.
Severity
7.8 (High)
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-04-08 13:26 UTC
CWE
- CWE-74 - Improper Neutralization of Special Elements in Output Used by a Downstream Component ('Injection')
Assigner
nvidia
CNA
References
1 reference
Impacted products
6 products
| Vendor | Product | Version | |
|---|---|---|---|
| NVIDIA | Omniverse Audio2Face |
Affected:
All versions prior to 2022.2
|
|
| NVIDIA | Omniverse Create |
Affected:
All versions prior to 2022.3
|
|
| NVIDIA | NVIDIA Isaac Sim |
Affected:
All versions prior to 2022.2.0
|
|
| NVIDIA | Omniverse Machinima |
Affected:
All versions prior to 2022.3
|
|
| NVIDIA | Omniverse Code |
Affected:
All versions prior to 2022.3.0
|
|
| NVIDIA | Omniverse View |
Affected:
All versions prior to 2022.2.1
|
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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
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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.
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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.