GSD-2016-2517
Vulnerability from gsd - Updated: 2023-12-13 01:21Details
NTP before 4.2.8p7 and 4.3.x before 4.3.92 allows remote attackers to cause a denial of service (prevent subsequent authentication) by leveraging knowledge of the controlkey or requestkey and sending a crafted packet to ntpd, which changes the value of trustedkey, controlkey, or requestkey. NOTE: this vulnerability exists because of a CVE-2016-2516 regression.
Aliases
Aliases
{
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"description": "NTP before 4.2.8p7 and 4.3.x before 4.3.92 allows remote attackers to cause a denial of service (prevent subsequent authentication) by leveraging knowledge of the controlkey or requestkey and sending a crafted packet to ntpd, which changes the value of trustedkey, controlkey, or requestkey. NOTE: this vulnerability exists because of a CVE-2016-2516 regression.",
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"references": [
"https://www.suse.com/security/cve/CVE-2016-2517.html"
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"modified": "2023-12-13T01:21:19.780199Z",
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},
{
"name": "FreeBSD-SA-16:16",
"refsource": "FREEBSD",
"tags": [],
"url": "https://security.FreeBSD.org/advisories/FreeBSD-SA-16:16.ntp.asc"
}
]
}
},
"impact": {
"baseMetricV2": {
"cvssV2": {
"accessComplexity": "HIGH",
"accessVector": "NETWORK",
"authentication": "SINGLE",
"availabilityImpact": "COMPLETE",
"baseScore": 4.9,
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"vectorString": "AV:N/AC:H/Au:S/C:N/I:N/A:C",
"version": "2.0"
},
"exploitabilityScore": 3.9,
"impactScore": 6.9,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"severity": "MEDIUM",
"userInteractionRequired": false
},
"baseMetricV3": {
"cvssV3": {
"attackComplexity": "HIGH",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 5.3,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.0/AV:N/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:H",
"version": "3.0"
},
"exploitabilityScore": 1.6,
"impactScore": 3.6
}
},
"lastModifiedDate": "2017-11-21T02:29Z",
"publishedDate": "2017-01-30T21:59Z"
}
}
}
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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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