FKIE_CVE-2022-1695
Vulnerability from fkie_nvd - Published: 2022-06-08 10:15 - Updated: 2026-06-17 04:22
Severity
Summary
The WP Simple Adsense Insertion WordPress plugin before 2.1 does not perform CSRF checks on updates to its admin page, allowing an attacker to trick a logged in user to manipulate ads and inject arbitrary javascript via submitting a form.
References
| URL | Tags | ||
|---|---|---|---|
| contact@wpscan.com | https://wpscan.com/vulnerability/2ac5b87b-1390-41ce-af6e-c50e5709baaa | Exploit, Third Party Advisory | |
| af854a3a-2127-422b-91ae-364da2661108 | https://wpscan.com/vulnerability/2ac5b87b-1390-41ce-af6e-c50e5709baaa | Exploit, Third Party Advisory |
Impacted products
| Vendor | Product | Version | |
|---|---|---|---|
| tipsandtricks-hq | wp_simple_adsense_insertion | * |
{
"affected": [
{
"affectedData": [
{
"product": "WP Simple Adsense Insertion",
"vendor": "Unknown",
"versions": [
{
"lessThan": "2.1",
"status": "affected",
"version": "2.1",
"versionType": "custom"
}
]
}
],
"source": "contact@wpscan.com"
}
],
"configurations": [
{
"nodes": [
{
"cpeMatch": [
{
"criteria": "cpe:2.3:a:tipsandtricks-hq:wp_simple_adsense_insertion:*:*:*:*:*:wordpress:*:*",
"matchCriteriaId": "DE3717A6-B218-4476-9B68-48BF6A618734",
"versionEndExcluding": "2.1",
"vulnerable": true
}
],
"negate": false,
"operator": "OR"
}
]
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "The WP Simple Adsense Insertion WordPress plugin before 2.1 does not perform CSRF checks on updates to its admin page, allowing an attacker to trick a logged in user to manipulate ads and inject arbitrary javascript via submitting a form."
},
{
"lang": "es",
"value": "El plugin WP Simple Adsense Insertion de WordPress versiones anteriores a 2.1, no lleva a cabo comprobaciones de tipo CSRF en las actualizaciones de su p\u00e1gina de administraci\u00f3n, lo que permite a un atacante enga\u00f1ar a un usuario con sesi\u00f3n iniciada para manipular anuncios e inyectar javascript arbitrario por medio del env\u00edo de un formulario"
}
],
"id": "CVE-2022-1695",
"lastModified": "2026-06-17T04:22:56.250",
"metrics": {
"cvssMetricV2": [
{
"acInsufInfo": false,
"baseSeverity": "MEDIUM",
"cvssData": {
"accessComplexity": "MEDIUM",
"accessVector": "NETWORK",
"authentication": "NONE",
"availabilityImpact": "NONE",
"baseScore": 4.3,
"confidentialityImpact": "NONE",
"integrityImpact": "PARTIAL",
"vectorString": "AV:N/AC:M/Au:N/C:N/I:P/A:N",
"version": "2.0"
},
"exploitabilityScore": 8.6,
"impactScore": 2.9,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"source": "nvd@nist.gov",
"type": "Primary",
"userInteractionRequired": true
}
],
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 4.3,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "NONE",
"integrityImpact": "LOW",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:N/I:L/A:N",
"version": "3.1"
},
"exploitabilityScore": 2.8,
"impactScore": 1.4,
"source": "nvd@nist.gov",
"type": "Primary"
}
]
},
"published": "2022-06-08T10:15:10.677",
"references": [
{
"source": "contact@wpscan.com",
"tags": [
"Exploit",
"Third Party Advisory"
],
"url": "https://wpscan.com/vulnerability/2ac5b87b-1390-41ce-af6e-c50e5709baaa"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Exploit",
"Third Party Advisory"
],
"url": "https://wpscan.com/vulnerability/2ac5b87b-1390-41ce-af6e-c50e5709baaa"
}
],
"sourceIdentifier": "contact@wpscan.com",
"vulnStatus": "Modified",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-352"
}
],
"source": "contact@wpscan.com",
"type": "Secondary"
},
{
"description": [
{
"lang": "en",
"value": "CWE-352"
}
],
"source": "nvd@nist.gov",
"type": "Primary"
}
]
}
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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.
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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.