FKIE_CVE-2017-5546
Vulnerability from fkie_nvd - Published: 2017-02-06 06:59 - Updated: 2026-06-17 01:20
Severity
Summary
The freelist-randomization feature in mm/slab.c in the Linux kernel 4.8.x and 4.9.x before 4.9.5 allows local users to cause a denial of service (duplicate freelist entries and system crash) or possibly have unspecified other impact in opportunistic circumstances by leveraging the selection of a large value for a random number.
References
Impacted products
| Vendor | Product | Version | |
|---|---|---|---|
| linux | linux_kernel | * |
{
"affected": [
{
"affectedData": [
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"product": "n/a",
"vendor": "n/a",
"versions": [
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"status": "affected",
"version": "n/a"
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"source": "cve@mitre.org"
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"configurations": [
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"cveTags": [],
"descriptions": [
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"value": "The freelist-randomization feature in mm/slab.c in the Linux kernel 4.8.x and 4.9.x before 4.9.5 allows local users to cause a denial of service (duplicate freelist entries and system crash) or possibly have unspecified other impact in opportunistic circumstances by leveraging the selection of a large value for a random number."
},
{
"lang": "es",
"value": "La caracter\u00edstica de freelist-randomization en mm/slab.c en el kernel 4.8.x de Linux y 4.9.x en versiones anteriores a 4.9.5 permite a usuarios locales provocar una denegaci\u00f3n de servicio (entradas freelist duplicadas y ca\u00edda del sistema) o posiblemente tener otro impacto no especificado en circunstancias oportunistas aprovechando la selecci\u00f3n de un valor grande para un n\u00famero aleatorio."
}
],
"id": "CVE-2017-5546",
"lastModified": "2026-06-17T01:20:43.290",
"metrics": {
"cvssMetricV2": [
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"integrityImpact": "COMPLETE",
"vectorString": "AV:L/AC:L/Au:N/C:C/I:C/A:C",
"version": "2.0"
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"impactScore": 10.0,
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"published": "2017-02-06T06:59:00.480",
"references": [
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"url": "http://git.kernel.org/cgit/linux/kernel/git/torvalds/linux.git/commit/?id=c4e490cf148e85ead0d1b1c2caaba833f1d5b29f"
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"sourceIdentifier": "cve@mitre.org",
"vulnStatus": "Modified",
"weaknesses": [
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"value": "NVD-CWE-noinfo"
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"source": "nvd@nist.gov",
"type": "Primary"
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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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