GSD-2022-28448
Vulnerability from gsd - Updated: 2023-12-13 01:19Details
nopCommerce 4.50.1 is vulnerable to Cross Site Scripting (XSS). An attacker (role customer) can inject javascript code to First name or Last name at Customer Info.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2022-28448",
"description": "nopCommerce 4.50.1 is vulnerable to Cross Site Scripting (XSS). An attacker (role customer) can inject javascript code to First name or Last name at Customer Info.",
"id": "GSD-2022-28448"
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
"aliases": [
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],
"details": "nopCommerce 4.50.1 is vulnerable to Cross Site Scripting (XSS). An attacker (role customer) can inject javascript code to First name or Last name at Customer Info.",
"id": "GSD-2022-28448",
"modified": "2023-12-13T01:19:34.368618Z",
"schema_version": "1.4.0"
}
},
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"affects": {
"vendor": {
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{
"product": {
"product_data": [
{
"product_name": "n/a",
"version": {
"version_data": [
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"version_value": "n/a"
}
]
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"references": {
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"name": "https://github.com/nopSolutions/nopCommerce/issues/6191",
"refsource": "MISC",
"url": "https://github.com/nopSolutions/nopCommerce/issues/6191"
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},
"impact": {
"baseMetricV2": {
"acInsufInfo": false,
"cvssV2": {
"accessComplexity": "MEDIUM",
"accessVector": "NETWORK",
"authentication": "SINGLE",
"availabilityImpact": "NONE",
"baseScore": 3.5,
"confidentialityImpact": "NONE",
"integrityImpact": "PARTIAL",
"vectorString": "AV:N/AC:M/Au:S/C:N/I:P/A:N",
"version": "2.0"
},
"exploitabilityScore": 6.8,
"impactScore": 2.9,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"severity": "LOW",
"userInteractionRequired": true
},
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"attackVector": "NETWORK",
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"baseScore": 5.4,
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"confidentialityImpact": "LOW",
"integrityImpact": "LOW",
"privilegesRequired": "LOW",
"scope": "CHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:L/I:L/A:N",
"version": "3.1"
},
"exploitabilityScore": 2.3,
"impactScore": 2.7
}
},
"lastModifiedDate": "2022-05-04T19:47Z",
"publishedDate": "2022-04-26T20:15Z"
}
}
}
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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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