GSD-2021-27405
Vulnerability from gsd - Updated: 2023-12-13 01:23Details
A ReDoS (regular expression denial of service) flaw was found in the @progfay/scrapbox-parser package before 6.0.3 for Node.js.
Aliases
Aliases
{
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"alias": "CVE-2021-27405",
"description": "A ReDoS (regular expression denial of service) flaw was found in the @progfay/scrapbox-parser package before 6.0.3 for Node.js.",
"id": "GSD-2021-27405"
},
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"exploitCode": "unknown",
"remediation": "unknown",
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"id": "GSD-2021-27405",
"modified": "2023-12-13T01:23:35.694973Z",
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"affected_versions": "All versions before 6.0.3",
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"cwe_ids": [
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"CWE-400",
"CWE-937"
],
"date": "2021-03-30",
"description": "A ReDoS (regular expression denial of service) flaw was found in the @progfay/scrapbox-parser package for Node.js.",
"fixed_versions": [
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],
"identifier": "CVE-2021-27405",
"identifiers": [
"CVE-2021-27405"
],
"not_impacted": "All versions starting from 6.0.3",
"package_slug": "npm/@progfay/scrapbox-parser",
"pubdate": "2021-02-19",
"solution": "Upgrade to version 6.0.3 or above.",
"title": "Uncontrolled Resource Consumption",
"urls": [
"https://nvd.nist.gov/vuln/detail/CVE-2021-27405"
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"uuid": "6389de40-947f-4c7e-baca-51d054a7b3a6"
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"impact": {
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"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"
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"exploitabilityScore": 10.0,
"impactScore": 2.9,
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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": "2021-03-30T15:21Z",
"publishedDate": "2021-02-19T05:15Z"
}
}
}
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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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