GSD-2022-33142
Vulnerability from gsd - Updated: 2023-12-13 01:19Details
Authenticated (subscriber+) Denial Of Service (DoS) vulnerability in WordPlus WordPress Better Messages plugin <= 1.9.10.57 at WordPress.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2022-33142",
"description": "Authenticated (subscriber+) Denial Of Service (DoS) vulnerability in WordPlus WordPress Better Messages plugin \u003c= 1.9.10.57 at WordPress.",
"id": "GSD-2022-33142"
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
"aliases": [
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],
"details": "Authenticated (subscriber+) Denial Of Service (DoS) vulnerability in WordPlus WordPress Better Messages plugin \u003c= 1.9.10.57 at WordPress.",
"id": "GSD-2022-33142",
"modified": "2023-12-13T01:19:23.054894Z",
"schema_version": "1.4.0"
}
},
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"DATE_PUBLIC": "2022-08-22T07:36:00.000Z",
"ID": "CVE-2022-33142",
"STATE": "PUBLIC",
"TITLE": "WordPress Better Messages plugin \u003c= 1.9.10.57 - Denial Of Service (DoS) vulnerability"
},
"affects": {
"vendor": {
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"product": {
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"credit": [
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"value": "Vulnerability discovered by Dhakal Ananda (Patchstack Alliance)"
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"impact": {
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"baseScore": 7.7,
"baseSeverity": "HIGH",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "LOW",
"scope": "CHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:N/I:N/A:H",
"version": "3.1"
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"refsource": "CONFIRM",
"url": "https://wordpress.org/plugins/bp-better-messages/#developers"
},
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}
]
},
"solution": [
{
"lang": "eng",
"value": "Update to 1.9.10.58 or higher version."
}
],
"source": {
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}
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"versionEndExcluding": "1.9.10.58",
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"references": {
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"tags": [
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},
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"attackVector": "NETWORK",
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"baseScore": 6.5,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "NONE",
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"scope": "UNCHANGED",
"userInteraction": "NONE",
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"version": "3.1"
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"exploitabilityScore": 2.8,
"impactScore": 3.6
}
},
"lastModifiedDate": "2022-08-25T03:08Z",
"publishedDate": "2022-08-23T16:15Z"
}
}
}
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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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