GSD-2020-5238
Vulnerability from gsd - Updated: 2023-12-13 01:22Details
The table extension in GitHub Flavored Markdown before version 0.29.0.gfm.1 takes O(n * n) time to parse certain inputs. An attacker could craft a markdown table which would take an unreasonably long time to process, causing a denial of service. This issue does not affect the upstream cmark project. The issue has been fixed in version 0.29.0.gfm.1.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2020-5238",
"description": "The table extension in GitHub Flavored Markdown before version 0.29.0.gfm.1 takes O(n * n) time to parse certain inputs. An attacker could craft a markdown table which would take an unreasonably long time to process, causing a denial of service. This issue does not affect the upstream cmark project. The issue has been fixed in version 0.29.0.gfm.1.",
"id": "GSD-2020-5238",
"references": [
"https://www.suse.com/security/cve/CVE-2020-5238.html",
"https://access.redhat.com/errata/RHSA-2021:1972"
]
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
"aliases": [
"CVE-2020-5238"
],
"details": "The table extension in GitHub Flavored Markdown before version 0.29.0.gfm.1 takes O(n * n) time to parse certain inputs. An attacker could craft a markdown table which would take an unreasonably long time to process, causing a denial of service. This issue does not affect the upstream cmark project. The issue has been fixed in version 0.29.0.gfm.1.",
"id": "GSD-2020-5238",
"modified": "2023-12-13T01:22:03.850928Z",
"schema_version": "1.4.0"
}
},
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"STATE": "PUBLIC",
"TITLE": "Denial of service in table parsing in cmark-gfm"
},
"affects": {
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{
"product": {
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}
]
},
"impact": {
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"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 6.5,
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"confidentialityImpact": "NONE",
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"scope": "UNCHANGED",
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"vectorString": "AV:N/AC:L/Au:S/C:N/I:N/A:P",
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"exploitabilityScore": 8.0,
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}
},
"lastModifiedDate": "2023-01-28T01:44Z",
"publishedDate": "2020-07-01T23:15Z"
}
}
}
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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.
- 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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