FKIE_CVE-2017-15102
Vulnerability from fkie_nvd - Published: 2017-11-15 21:29 - Updated: 2026-06-17 01:07
Severity
Summary
The tower_probe function in drivers/usb/misc/legousbtower.c in the Linux kernel before 4.8.1 allows local users (who are physically proximate for inserting a crafted USB device) to gain privileges by leveraging a write-what-where condition that occurs after a race condition and a NULL pointer dereference.
References
Impacted products
| Vendor | Product | Version | |
|---|---|---|---|
| linux | linux_kernel | * | |
| redhat | enterprise_linux | 5.0 | |
| redhat | enterprise_linux | 6.0 | |
| canonical | ubuntu_linux | 12.04 | |
| canonical | ubuntu_linux | 14.04 |
{
"affected": [
{
"affectedData": [
{
"product": "Linux kernel through version 4.9-rc1",
"vendor": "n/a",
"versions": [
{
"status": "affected",
"version": "Linux kernel through version 4.9-rc1"
}
]
}
],
"source": "secalert@redhat.com"
}
],
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"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "The tower_probe function in drivers/usb/misc/legousbtower.c in the Linux kernel before 4.8.1 allows local users (who are physically proximate for inserting a crafted USB device) to gain privileges by leveraging a write-what-where condition that occurs after a race condition and a NULL pointer dereference."
},
{
"lang": "es",
"value": "La funci\u00f3n tower_probe en drivers/usb/misc/legousbtower.c en el kernel de Linux en versiones anteriores a la 4.8.1 permite que usuarios locales (que est\u00e9n tan cerca f\u00edsicamente como para insertar un dispositivo USB manipulado) obtengan privilegios aprovechando una condici\u00f3n de write-what-where que ocurre tras una condici\u00f3n de carrera y una desreferencia de puntero NULL"
}
],
"id": "CVE-2017-15102",
"lastModified": "2026-06-17T01:07:15.323",
"metrics": {
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"source": "nvd@nist.gov",
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"published": "2017-11-15T21:29:00.250",
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"sourceIdentifier": "secalert@redhat.com",
"vulnStatus": "Modified",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-476"
}
],
"source": "nvd@nist.gov",
"type": "Primary"
}
]
}
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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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