FKIE_CVE-2022-39035
Vulnerability from fkie_nvd - Published: 2022-09-28 04:15 - Updated: 2026-06-17 04:57
Severity
6.1 (Medium) - CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:N
6.1 (Medium) - CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:N
6.1 (Medium) - CVSS:3.1/
Summary
Smart eVision has insufficient filtering for special characters in the POST Data parameter in the specific function. An unauthenticated remote attacker can inject JavaScript to perform XSS (Stored Cross-Site Scripting) attack.
References
| URL | Tags | ||
|---|---|---|---|
| twcert@cert.org.tw | https://www.twcert.org.tw/tw/cp-132-6572-5c2c8-1.html | Third Party Advisory | |
| af854a3a-2127-422b-91ae-364da2661108 | https://www.twcert.org.tw/tw/cp-132-6572-5c2c8-1.html | Third Party Advisory |
Impacted products
| Vendor | Product | Version | |
|---|---|---|---|
| lcnet | smart_evision | 2022.02.21 |
{
"affected": [
{
"affectedData": [
{
"product": "Smart eVision",
"vendor": "Smart eVision Information Technology Inc.",
"versions": [
{
"status": "affected",
"version": "2022.02.21"
}
]
}
],
"source": "twcert@cert.org.tw"
}
],
"configurations": [
{
"nodes": [
{
"cpeMatch": [
{
"criteria": "cpe:2.3:a:lcnet:smart_evision:2022.02.21:*:*:*:*:*:*:*",
"matchCriteriaId": "C0BD6F48-0614-4FCE-ACA4-EAB16D9B1E22",
"vulnerable": true
}
],
"negate": false,
"operator": "OR"
}
]
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "Smart eVision has insufficient filtering for special characters in the POST Data parameter in the specific function. An unauthenticated remote attacker can inject JavaScript to perform XSS (Stored Cross-Site Scripting) attack."
},
{
"lang": "es",
"value": "Smart eVision presenta un filtrado insuficiente para caracteres especiales en el par\u00e1metro POST Data en la funci\u00f3n espec\u00edfica. Un atacante remoto no autenticado puede inyectar JavaScript y llevar a cabo un ataque de tipo XSS (Cross-Site Scripting Almacenado)"
}
],
"id": "CVE-2022-39035",
"lastModified": "2026-06-17T04:57:28.293",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 6.1,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "LOW",
"integrityImpact": "LOW",
"privilegesRequired": "NONE",
"scope": "CHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:N",
"version": "3.1"
},
"exploitabilityScore": 2.8,
"impactScore": 2.7,
"source": "twcert@cert.org.tw",
"type": "Secondary"
},
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 6.1,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "LOW",
"integrityImpact": "LOW",
"privilegesRequired": "NONE",
"scope": "CHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:N",
"version": "3.1"
},
"exploitabilityScore": 2.8,
"impactScore": 2.7,
"source": "nvd@nist.gov",
"type": "Primary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2022-39035",
"options": [
{
"exploitation": "none"
},
{
"automatable": "no"
},
{
"technicalImpact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2025-05-21T14:49:56.328543Z",
"version": "2.0.3"
}
}
]
},
"published": "2022-09-28T04:15:15.067",
"references": [
{
"source": "twcert@cert.org.tw",
"tags": [
"Third Party Advisory"
],
"url": "https://www.twcert.org.tw/tw/cp-132-6572-5c2c8-1.html"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Third Party Advisory"
],
"url": "https://www.twcert.org.tw/tw/cp-132-6572-5c2c8-1.html"
}
],
"sourceIdentifier": "twcert@cert.org.tw",
"vulnStatus": "Modified",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-79"
}
],
"source": "twcert@cert.org.tw",
"type": "Secondary"
},
{
"description": [
{
"lang": "en",
"value": "CWE-79"
}
],
"source": "nvd@nist.gov",
"type": "Primary"
}
]
}
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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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