GSD-2020-8037
Vulnerability from gsd - Updated: 2023-12-13 01:21Details
The ppp decapsulator in tcpdump 4.9.3 can be convinced to allocate a large amount of memory.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2020-8037",
"description": "The ppp decapsulator in tcpdump 4.9.3 can be convinced to allocate a large amount of memory.",
"id": "GSD-2020-8037",
"references": [
"https://www.suse.com/security/cve/CVE-2020-8037.html",
"https://access.redhat.com/errata/RHSA-2021:4236",
"https://advisories.mageia.org/CVE-2020-8037.html",
"https://ubuntu.com/security/CVE-2020-8037",
"https://linux.oracle.com/cve/CVE-2020-8037.html"
]
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
"aliases": [
"CVE-2020-8037"
],
"details": "The ppp decapsulator in tcpdump 4.9.3 can be convinced to allocate a large amount of memory.",
"id": "GSD-2020-8037",
"modified": "2023-12-13T01:21:54.379894Z",
"schema_version": "1.4.0"
}
},
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"STATE": "PUBLIC",
"TITLE": "ppp decapsulator can be convinced to allocate a large amount of memory"
},
"affects": {
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{
"product": {
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"version": {
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}
]
}
}
]
},
"vendor_name": "The TCPdump Group"
}
]
}
},
"credit": [
{
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"value": "Hardik Shah"
}
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}
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}
]
}
]
},
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},
"impact": {
"baseMetricV2": {
"acInsufInfo": false,
"cvssV2": {
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"accessVector": "NETWORK",
"authentication": "NONE",
"availabilityImpact": "PARTIAL",
"baseScore": 5.0,
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"vectorString": "AV:N/AC:L/Au:N/C:N/I:N/A:P",
"version": "2.0"
},
"exploitabilityScore": 10.0,
"impactScore": 2.9,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"severity": "MEDIUM",
"userInteractionRequired": false
},
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"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 7.5,
"baseSeverity": "HIGH",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
"version": "3.1"
},
"exploitabilityScore": 3.9,
"impactScore": 3.6
}
},
"lastModifiedDate": "2021-05-05T13:12Z",
"publishedDate": "2020-11-04T18:15Z"
}
}
}
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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.
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- 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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