CVE-2022-29436 (GCVE-0-2022-29436)
Vulnerability from cvelistv5 – Published: 2022-05-17 19:51 – Updated: 2026-04-28 16:07
VLAI
EPSS
VEX
Title
WordPress Code Snippets Extended plugin <= 1.4.7 - Cross-Site Request Forgery (CSRF) vulnerability leading to Persistent Cross-Site Scripting (XSS)
Summary
Persistent Cross-Site Scripting (XSS) vulnerability in Alexander Stokmann's Code Snippets Extended plugin <= 1.4.7 on WordPress via Cross-Site Request Forgery (vulnerable parameters &title, &snippet_code).
Severity
4.7 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-02-20 19:30 UTC
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://wordpress.org/plugins/code-snippets-exten… | x_refsource_CONFIRM |
| https://patchstack.com/database/vulnerability/cod… | x_refsource_CONFIRM |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Alexander Stokmann | Code Snippets Extended (WordPress plugin) |
Affected:
<= 1.4.7 , ≤ 1.4.7
(custom)
|
guessed |
Date Public
2022-05-17 00:00
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"name": "https://patchstack.com/database/vulnerability/code-snippets-extended/wordpress-code-snippets-extended-plugin-1-4-7-cross-site-request-forgery-csrf-vulnerability-leading-to-persistent-cross-site-scripting-xss",
"refsource": "CONFIRM",
"url": "https://patchstack.com/database/vulnerability/code-snippets-extended/wordpress-code-snippets-extended-plugin-1-4-7-cross-site-request-forgery-csrf-vulnerability-leading-to-persistent-cross-site-scripting-xss"
}
]
},
"solution": [
{
"lang": "en",
"value": "Deactivate and delete. No patched version is available. No reply from the vendor."
}
],
"source": {
"discovery": "EXTERNAL"
}
}
}
},
"cveMetadata": {
"assignerOrgId": "21595511-bba5-4825-b968-b78d1f9984a3",
"assignerShortName": "Patchstack",
"cveId": "CVE-2022-29436",
"datePublished": "2022-05-17T19:51:56.700Z",
"dateReserved": "2022-04-18T00:00:00.000Z",
"dateUpdated": "2026-04-28T16:07:41.354Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.2"
}
}
}
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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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