GSD-2022-22970
Vulnerability from gsd - Updated: 2023-12-13 01:19Details
In spring framework versions prior to 5.3.20+ , 5.2.22+ and old unsupported versions, applications that handle file uploads are vulnerable to DoS attack if they rely on data binding to set a MultipartFile or javax.servlet.Part to a field in a model object.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2022-22970",
"description": "In spring framework versions prior to 5.3.20+ , 5.2.22+ and old unsupported versions, applications that handle file uploads are vulnerable to DoS attack if they rely on data binding to set a MultipartFile or javax.servlet.Part to a field in a model object.",
"id": "GSD-2022-22970",
"references": [
"https://access.redhat.com/errata/RHSA-2022:5532"
]
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
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"id": "GSD-2022-22970",
"modified": "2023-12-13T01:19:29.496954Z",
"schema_version": "1.4.0"
}
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{
"affected_range": "(,5.2.21.RELEASE],[5.3.0,5.3.20)",
"affected_versions": "All versions up to 5.2.21.release, all versions starting from 5.3.0 before 5.3.20",
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"cwe_ids": [
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"CWE-937"
],
"date": "2022-05-25",
"description": "In spring framework versions prior to 5.3.20+, 5.2.22+ and old unsupported versions, applications that handle file uploads is vulnerable to DoS attack if they rely on data binding to set a MultipartFile or javax.servlet.Part to a field in a model object.",
"fixed_versions": [
"5.2.22.RELEASE",
"5.3.20"
],
"identifier": "CVE-2022-22970",
"identifiers": [
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"CVE-2022-22970"
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"package_slug": "maven/org.springframework/spring-core",
"pubdate": "2022-05-13",
"solution": "Upgrade to versions 5.2.22.RELEASE, 5.3.20 or above.",
"title": "Allocation of Resources Without Limits or Throttling",
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"impact": {
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"accessVector": "NETWORK",
"authentication": "SINGLE",
"availabilityImpact": "PARTIAL",
"baseScore": 3.5,
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"vectorString": "AV:N/AC:M/Au:S/C:N/I:N/A:P",
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"confidentialityImpact": "NONE",
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"scope": "UNCHANGED",
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},
"lastModifiedDate": "2022-10-07T13:17Z",
"publishedDate": "2022-05-12T20:15Z"
}
}
}
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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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