GSD-2021-34693
Vulnerability from gsd - Updated: 2023-12-13 01:23Details
net/can/bcm.c in the Linux kernel through 5.12.10 allows local users to obtain sensitive information from kernel stack memory because parts of a data structure are uninitialized.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2021-34693",
"description": "net/can/bcm.c in the Linux kernel through 5.12.10 allows local users to obtain sensitive information from kernel stack memory because parts of a data structure are uninitialized.",
"id": "GSD-2021-34693",
"references": [
"https://www.suse.com/security/cve/CVE-2021-34693.html",
"https://www.debian.org/security/2021/dsa-4941",
"https://ubuntu.com/security/CVE-2021-34693",
"https://advisories.mageia.org/CVE-2021-34693.html",
"https://security.archlinux.org/CVE-2021-34693",
"https://linux.oracle.com/cve/CVE-2021-34693.html"
]
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
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],
"details": "net/can/bcm.c in the Linux kernel through 5.12.10 allows local users to obtain sensitive information from kernel stack memory because parts of a data structure are uninitialized.",
"id": "GSD-2021-34693",
"modified": "2023-12-13T01:23:14.267066Z",
"schema_version": "1.4.0"
}
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"name": "[oss-security] 20210615 CVE-2021-34693: Infoleak in CAN BCM protocol in Linux kernel",
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"url": "http://www.openwall.com/lists/oss-security/2021/06/15/1"
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"value": "net/can/bcm.c in the Linux kernel through 5.12.10 allows local users to obtain sensitive information from kernel stack memory because parts of a data structure are uninitialized."
},
{
"lang": "es",
"value": "El archivo net/can/bcm.c en el kernel de Linux versiones hasta 5.12.10, permite a usuarios locales obtener informaci\u00f3n confidencial de la memoria de la pila del kernel porque partes de una estructura de datos no est\u00e1n inicializadas"
}
],
"id": "CVE-2021-34693",
"lastModified": "2024-03-25T01:15:51.150",
"metrics": {
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"published": "2021-06-14T22:15:20.820",
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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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