GSD-2017-6429
Vulnerability from gsd - Updated: 2023-12-13 01:21Details
Buffer overflow in the tcpcapinfo utility in Tcpreplay before 4.2.0 Beta 1 allows remote attackers to have unspecified impact via a pcap file with an over-size packet.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2017-6429",
"description": "Buffer overflow in the tcpcapinfo utility in Tcpreplay before 4.2.0 Beta 1 allows remote attackers to have unspecified impact via a pcap file with an over-size packet.",
"id": "GSD-2017-6429",
"references": [
"https://www.suse.com/security/cve/CVE-2017-6429.html",
"https://security.archlinux.org/CVE-2017-6429"
]
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
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],
"details": "Buffer overflow in the tcpcapinfo utility in Tcpreplay before 4.2.0 Beta 1 allows remote attackers to have unspecified impact via a pcap file with an over-size packet.",
"id": "GSD-2017-6429",
"modified": "2023-12-13T01:21:10.000517Z",
"schema_version": "1.4.0"
}
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"versionEndIncluding": "4.1.2",
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},
"impact": {
"baseMetricV2": {
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"accessComplexity": "MEDIUM",
"accessVector": "NETWORK",
"authentication": "NONE",
"availabilityImpact": "PARTIAL",
"baseScore": 6.8,
"confidentialityImpact": "PARTIAL",
"integrityImpact": "PARTIAL",
"vectorString": "AV:N/AC:M/Au:N/C:P/I:P/A:P",
"version": "2.0"
},
"exploitabilityScore": 8.6,
"impactScore": 6.4,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"severity": "MEDIUM",
"userInteractionRequired": true
},
"baseMetricV3": {
"cvssV3": {
"attackComplexity": "LOW",
"attackVector": "LOCAL",
"availabilityImpact": "HIGH",
"baseScore": 7.8,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
"version": "3.1"
},
"exploitabilityScore": 1.8,
"impactScore": 5.9
}
},
"lastModifiedDate": "2022-06-03T14:34Z",
"publishedDate": "2017-03-15T15:59Z"
}
}
}
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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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