GSD-2021-25951
Vulnerability from gsd - Updated: 2023-12-13 01:23Details
XXE vulnerability in 'XML2Dict' version 0.2.2 allows an attacker to cause a denial of service.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2021-25951",
"description": "XXE vulnerability in \u0027XML2Dict\u0027 version 0.2.2 allows an attacker to cause a denial of service.",
"id": "GSD-2021-25951"
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
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"details": "XXE vulnerability in \u0027XML2Dict\u0027 version 0.2.2 allows an attacker to cause a denial of service.",
"id": "GSD-2021-25951",
"modified": "2023-12-13T01:23:21.393217Z",
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"gitlab.com": {
"advisories": [
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"cwe_ids": [
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"CWE-937"
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"not_impacted": "",
"package_slug": "pypi/XML2Dict",
"pubdate": "2021-06-30",
"solution": "Unfortunately, there is no solution available yet.",
"title": "Improper Restriction of XML External Entity Reference",
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"impact": {
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"lastModifiedDate": "2021-07-06T16:13Z",
"publishedDate": "2021-06-30T12:15Z"
}
}
}
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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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