CVE-2016-5011 (GCVE-0-2016-5011)
Vulnerability from cvelistv5 – Published: 2017-04-11 15:00 – Updated: 2024-08-06 00:46
VLAI
EPSS
VEX
Summary
The parse_dos_extended function in partitions/dos.c in the libblkid library in util-linux allows physically proximate attackers to cause a denial of service (memory consumption) via a crafted MSDOS partition table with an extended partition boot record at zero offset.
Severity
4.6 (Medium)
CWE
- n/a
Assigner
References
7 references
| URL | Tags |
|---|---|
| https://git.kernel.org/pub/scm/utils/util-linux/u… | x_refsource_CONFIRM |
| http://www.securityfocus.com/bid/91683 | vdb-entryx_refsource_BID |
| http://www.securitytracker.com/id/1036272 | vdb-entryx_refsource_SECTRACK |
| http://www.openwall.com/lists/oss-security/2016/07/11/2 | mailing-listx_refsource_MLIST |
| http://www-01.ibm.com/support/docview.wss?uid=nas… | x_refsource_CONFIRM |
| http://www-01.ibm.com/support/docview.wss?uid=isg… | x_refsource_CONFIRM |
| http://rhn.redhat.com/errata/RHSA-2016-2605.html | vendor-advisoryx_refsource_REDHAT |
Date Public
2016-07-11 00:00
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"redhat_vex": {
"aggregate_severity": "Low",
"current_release_date": "2026-05-13T04:15:41+00:00",
"cve": "CVE-2016-5011",
"id": "CVE-2016-5011",
"initial_release_date": "2016-07-11T00:00:00+00:00",
"product_status:fixed": "560",
"product_status:known_affected": "8",
"source": "Red Hat CSAF VEX",
"status": "final",
"title": "util-linux: Extended partition loop in MBR partition table leads to DOS",
"url": "https://security.access.redhat.com/data/csaf/v2/vex/2016/cve-2016-5011.json",
"version": "3"
},
"suse_vex": {
"aggregate_severity": "low",
"current_release_date": "2026-09-01T02:16:18Z",
"cve": "CVE-2016-5011",
"id": "CVE-2016-5011",
"initial_release_date": "2023-02-15T05:02:06Z",
"product_status:known_affected": "320",
"product_status:known_not_affected": "111",
"product_status:recommended": "1654",
"source": "SUSE CSAF VEX",
"status": "interim",
"title": "SUSE CVE CVE-2016-5011",
"url": "https://ftp.suse.com/pub/projects/security/csaf-vex/cve-2016-5011.json",
"version": "37"
}
}
}
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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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