CVE-2022-48182 (GCVE-0-2022-48182)
Vulnerability from cvelistv5 – Published: 2023-10-09 20:55 – Updated: 2024-09-19 13:18
VLAI
EPSS
VEX
Summary
A vulnerability was reported in ThinkPad T14s Gen 3 and X13 Gen3 that could cause the BIOS tamper detection mechanism to not trigger under specific circumstances which could allow unauthorized access.
Severity
6.1 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-09-19 13:09 UTC
CWE
- CWE-1263 - Improper Physical Access Control
Assigner
References
1 reference
Impacted products
4 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Lenovo | ThinkPad T14s X13 Gen3 BIOS - Windows |
Affected:
, < 1.30 (R22ET60W)
(custom)
|
guessed | |
| Lenovo | ThinkPad X13 Gen3 BIOS - Windows |
Affected:
, < 1.30 (R22ET60W)
(semver)
|
guessed | |
| Lenovo | ThinkPad T14s Gen 3 BIOS - Linux |
Affected:
, < 1.35 (N3CET54W)
(custom)
|
guessed | |
| Lenovo | ThinkPad X13 Gen3 BIOS - Linux |
Affected:
, < 1.35 (N3CET54W)
(custom)
|
guessed |
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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.
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.