GSD-2016-10375
Vulnerability from gsd - Updated: 2023-12-13 01:21Details
Yodl before 3.07.01 has a Buffer Over-read in the queue_push function in queue/queuepush.c.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2016-10375",
"description": "Yodl before 3.07.01 has a Buffer Over-read in the queue_push function in queue/queuepush.c.",
"id": "GSD-2016-10375",
"references": [
"https://www.suse.com/security/cve/CVE-2016-10375.html",
"https://advisories.mageia.org/CVE-2016-10375.html"
]
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
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],
"details": "Yodl before 3.07.01 has a Buffer Over-read in the queue_push function in queue/queuepush.c.",
"id": "GSD-2016-10375",
"modified": "2023-12-13T01:21:26.832689Z",
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}
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"name": "https://github.com/fbb-git/yodl/commit/fd85f8c94182558ff1480d06a236d6fb927979a3",
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"impact": {
"baseMetricV2": {
"acInsufInfo": true,
"cvssV2": {
"accessComplexity": "LOW",
"accessVector": "NETWORK",
"authentication": "NONE",
"availabilityImpact": "PARTIAL",
"baseScore": 7.5,
"confidentialityImpact": "PARTIAL",
"integrityImpact": "PARTIAL",
"vectorString": "AV:N/AC:L/Au:N/C:P/I:P/A:P",
"version": "2.0"
},
"exploitabilityScore": 10.0,
"impactScore": 6.4,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"severity": "HIGH",
"userInteractionRequired": false
},
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"availabilityImpact": "HIGH",
"baseScore": 9.8,
"baseSeverity": "CRITICAL",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
"version": "3.0"
},
"exploitabilityScore": 3.9,
"impactScore": 5.9
}
},
"lastModifiedDate": "2020-04-30T18:15Z",
"publishedDate": "2017-05-26T17:29Z"
}
}
}
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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
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Nomenclature
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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.
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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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