CVE-2016-5180 (GCVE-0-2016-5180)
Vulnerability from cvelistv5 – Published: 2016-10-03 15:00 – Updated: 2024-08-06 00:53
VLAI
EPSS
VEX
Summary
Heap-based buffer overflow in the ares_create_query function in c-ares 1.x before 1.12.0 allows remote attackers to cause a denial of service (out-of-bounds write) or possibly execute arbitrary code via a hostname with an escaped trailing dot.
Severity
9.8 (Critical)
CWE
- n/a
Assigner
References
9 references
| URL | Tags |
|---|---|
| https://source.android.com/security/bulletin/2017… | x_refsource_CONFIRM |
| http://www.securityfocus.com/bid/93243 | vdb-entryx_refsource_BID |
| http://www.ubuntu.com/usn/USN-3143-1 | vendor-advisoryx_refsource_UBUNTU |
| https://security.gentoo.org/glsa/201701-28 | vendor-advisoryx_refsource_GENTOO |
| http://www.debian.org/security/2016/dsa-3682 | vendor-advisoryx_refsource_DEBIAN |
| https://c-ares.haxx.se/adv_20160929.html | x_refsource_CONFIRM |
| https://c-ares.haxx.se/CVE-2016-5180.patch | x_refsource_CONFIRM |
| https://googlechromereleases.blogspot.in/2016/09/… | x_refsource_CONFIRM |
| http://rhn.redhat.com/errata/RHSA-2017-0002.html | vendor-advisoryx_refsource_REDHAT |
Date Public
2016-09-29 00:00
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"source": "af854a3a-2127-422b-91ae-364da2661108",
"url": "https://source.android.com/security/bulletin/2017-01-01.html"
}
],
"sourceIdentifier": "chrome-cve-admin@google.com",
"vulnStatus": "Modified",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-787"
}
],
"source": "nvd@nist.gov",
"type": "Primary"
}
]
}
},
"redhat_vex": {
"aggregate_severity": "Moderate",
"current_release_date": "2026-06-28T06:35:26+00:00",
"cve": "CVE-2016-5180",
"id": "CVE-2016-5180",
"initial_release_date": "2016-09-29T00:00:00+00:00",
"product_status:fixed": "72",
"product_status:known_affected": "12",
"product_status:known_not_affected": "14",
"product_status:under_investigation": "1",
"source": "Red Hat CSAF VEX",
"status": "final",
"title": "c-ares: Single byte out of buffer write",
"url": "https://security.access.redhat.com/data/csaf/v2/vex/2016/cve-2016-5180.json",
"version": "3"
}
}
}
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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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