GSD-2020-36748
Vulnerability from gsd - Updated: 2023-12-13 01:21Details
The Dokan plugin for WordPress is vulnerable to Cross-Site Request Forgery in versions up to, and including, 3.0.8. This is due to missing or incorrect nonce validation on the handle_order_export() function. This makes it possible for unauthenticated attackers to trigger an order export via a forged request granted they can trick a site administrator into performing an action such as clicking on a link.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2020-36748",
"id": "GSD-2020-36748"
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
"aliases": [
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],
"details": "The Dokan plugin for WordPress is vulnerable to Cross-Site Request Forgery in versions up to, and including, 3.0.8. This is due to missing or incorrect nonce validation on the handle_order_export() function. This makes it possible for unauthenticated attackers to trigger an order export via a forged request granted they can trick a site administrator into performing an action such as clicking on a link.",
"id": "GSD-2020-36748",
"modified": "2023-12-13T01:21:55.910705Z",
"schema_version": "1.4.0"
}
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"product_name": "Dokan \u2013 Best WooCommerce Multivendor Marketplace Solution \u2013 Build Your Own Amazon, eBay, Etsy",
"version": {
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{
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}
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},
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"url": "https://blog.nintechnet.com/multiple-wordpress-plugins-fixed-csrf-vulnerabilities-part-4/"
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{
"name": "https://plugins.trac.wordpress.org/changeset/2368433/dokan-lite/trunk/includes/Dashboard/Templates/Orders.php",
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"url": "https://plugins.trac.wordpress.org/changeset/2368433/dokan-lite/trunk/includes/Dashboard/Templates/Orders.php"
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"nvd.nist.gov": {
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},
"impact": {
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"cvssV3": {
"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
}
},
"lastModifiedDate": "2023-07-08T00:01Z",
"publishedDate": "2023-07-01T06:15Z"
}
}
}
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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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