GSD-2021-36159
Vulnerability from gsd - Updated: 2023-12-13 01:23Details
libfetch before 2021-07-26, as used in apk-tools, xbps, and other products, mishandles numeric strings for the FTP and HTTP protocols. The FTP passive mode implementation allows an out-of-bounds read because strtol is used to parse the relevant numbers into address bytes. It does not check if the line ends prematurely. If it does, the for-loop condition checks for the '\0' terminator one byte too late.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2021-36159",
"description": "libfetch before 2021-07-26, as used in apk-tools, xbps, and other products, mishandles numeric strings for the FTP and HTTP protocols. The FTP passive mode implementation allows an out-of-bounds read because strtol is used to parse the relevant numbers into address bytes. It does not check if the line ends prematurely. If it does, the for-loop condition checks for the \u0027\\0\u0027 terminator one byte too late.",
"id": "GSD-2021-36159"
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
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"aliases": [
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],
"details": "libfetch before 2021-07-26, as used in apk-tools, xbps, and other products, mishandles numeric strings for the FTP and HTTP protocols. The FTP passive mode implementation allows an out-of-bounds read because strtol is used to parse the relevant numbers into address bytes. It does not check if the line ends prematurely. If it does, the for-loop condition checks for the \u0027\\0\u0027 terminator one byte too late.",
"id": "GSD-2021-36159",
"modified": "2023-12-13T01:23:16.584733Z",
"schema_version": "1.4.0"
}
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"impact": {
"baseMetricV2": {
"acInsufInfo": false,
"cvssV2": {
"accessComplexity": "LOW",
"accessVector": "NETWORK",
"authentication": "NONE",
"availabilityImpact": "PARTIAL",
"baseScore": 6.4,
"confidentialityImpact": "PARTIAL",
"integrityImpact": "NONE",
"vectorString": "AV:N/AC:L/Au:N/C:P/I:N/A:P",
"version": "2.0"
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"exploitabilityScore": 10.0,
"impactScore": 4.9,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"severity": "MEDIUM",
"userInteractionRequired": false
},
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"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 9.1,
"baseSeverity": "CRITICAL",
"confidentialityImpact": "HIGH",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:H",
"version": "3.1"
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"exploitabilityScore": 3.9,
"impactScore": 5.2
}
},
"lastModifiedDate": "2021-10-18T12:19Z",
"publishedDate": "2021-08-03T14:15Z"
}
}
}
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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
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- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
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- Not confirmed: The user expressed doubt about the validity of the vulnerability.
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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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