GSD-2022-36422
Vulnerability from gsd - Updated: 2023-12-13 01:19Details
Rating increase/decrease via race condition in Lester 'GaMerZ' Chan WP-PostRatings plugin <= 1.89 at WordPress.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2022-36422",
"description": "Rating increase/decrease via race condition in Lester \u0027GaMerZ\u0027 Chan WP-PostRatings plugin \u003c= 1.89 at WordPress.",
"id": "GSD-2022-36422"
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
"aliases": [
"CVE-2022-36422"
],
"details": "Rating increase/decrease via race condition in Lester \u0027GaMerZ\u0027 Chan WP-PostRatings plugin \u003c= 1.89 at WordPress.",
"id": "GSD-2022-36422",
"modified": "2023-12-13T01:19:21.206039Z",
"schema_version": "1.4.0"
}
},
"namespaces": {
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"CVE_data_meta": {
"ASSIGNER": "audit@patchstack.com",
"DATE_PUBLIC": "2022-08-31T07:24:00.000Z",
"ID": "CVE-2022-36422",
"STATE": "PUBLIC",
"TITLE": "WP-PostRatings plugin \u003c= 1.89 - Rating increase/decrease via race condition"
},
"affects": {
"vendor": {
"vendor_data": [
{
"product": {
"product_data": [
{
"product_name": "WP-PostRatings (WordPress plugin)",
"version": {
"version_data": [
{
"version_affected": "\u003c=",
"version_name": "\u003c= 1.89",
"version_value": "1.89"
}
]
}
}
]
},
"vendor_name": "Lester \u0027GaMerZ\u0027 Chan"
}
]
}
},
"credit": [
{
"lang": "eng",
"value": "Vulnerability discovered by Nguy Minh Tuan (Patchstack Alliance)"
}
],
"data_format": "MITRE",
"data_type": "CVE",
"data_version": "4.0",
"description": {
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"value": "Rating increase/decrease via race condition in Lester \u0027GaMerZ\u0027 Chan WP-PostRatings plugin \u003c= 1.89 at WordPress."
}
]
},
"generator": {
"engine": "Vulnogram 0.0.9"
},
"impact": {
"cvss": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 4.3,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "NONE",
"integrityImpact": "LOW",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:N",
"version": "3.1"
}
},
"problemtype": {
"problemtype_data": [
{
"description": [
{
"lang": "eng",
"value": "Race condition"
}
]
}
]
},
"references": {
"reference_data": [
{
"name": "https://patchstack.com/database/vulnerability/wp-postratings/wordpress-wp-postratings-plugin-1-89-rating-increase-decrease-via-race-condition-vulnerability/_s_id=cve",
"refsource": "CONFIRM",
"url": "https://patchstack.com/database/vulnerability/wp-postratings/wordpress-wp-postratings-plugin-1-89-rating-increase-decrease-via-race-condition-vulnerability/_s_id=cve"
},
{
"name": "https://wordpress.org/plugins/wp-postratings/#developers",
"refsource": "CONFIRM",
"url": "https://wordpress.org/plugins/wp-postratings/#developers"
}
]
},
"solution": [
{
"lang": "eng",
"value": "Update to 1.90 or higher version."
}
],
"source": {
"discovery": "EXTERNAL"
}
},
"nvd.nist.gov": {
"configurations": {
"CVE_data_version": "4.0",
"nodes": [
{
"children": [],
"cpe_match": [
{
"cpe23Uri": "cpe:2.3:a:wp-postratings_project:wp-postratings:*:*:*:*:*:wordpress:*:*",
"cpe_name": [],
"versionEndIncluding": "1.89",
"vulnerable": true
}
],
"operator": "OR"
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},
"cve": {
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"ID": "CVE-2022-36422"
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"description": {
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}
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"problemtype": {
"problemtype_data": [
{
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{
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"value": "CWE-362"
}
]
}
]
},
"references": {
"reference_data": [
{
"name": "https://patchstack.com/database/vulnerability/wp-postratings/wordpress-wp-postratings-plugin-1-89-rating-increase-decrease-via-race-condition-vulnerability/_s_id=cve",
"refsource": "CONFIRM",
"tags": [
"Third Party Advisory"
],
"url": "https://patchstack.com/database/vulnerability/wp-postratings/wordpress-wp-postratings-plugin-1-89-rating-increase-decrease-via-race-condition-vulnerability/_s_id=cve"
},
{
"name": "https://wordpress.org/plugins/wp-postratings/#developers",
"refsource": "CONFIRM",
"tags": [
"Product",
"Release Notes"
],
"url": "https://wordpress.org/plugins/wp-postratings/#developers"
}
]
}
},
"impact": {
"baseMetricV3": {
"cvssV3": {
"attackComplexity": "HIGH",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 3.1,
"baseSeverity": "LOW",
"confidentialityImpact": "NONE",
"integrityImpact": "LOW",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:U/C:N/I:L/A:N",
"version": "3.1"
},
"exploitabilityScore": 1.6,
"impactScore": 1.4
}
},
"lastModifiedDate": "2022-09-10T03:49Z",
"publishedDate": "2022-09-09T15:15Z"
}
}
}
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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.
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- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
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- 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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