CVE-2022-29430 (GCVE-0-2022-29430)

Vulnerability from cvelistv5 – Published: 2022-05-20 20:44 – Updated: 2026-04-28 16:07
VLAI
Title
WordPress PNG to JPG plugin <= 4.0 - Cross-Site Request Forgery (CSRF) leading to Persistent Cross-Site Scripting (XSS) vulnerability
Summary
Cross-Site Scripting (XSS) vulnerability in KubiQ's PNG to JPG plugin <= 4.0 at WordPress via Cross-Site Request Forgery (CSRF). Vulnerable parameter &jpg_quality.
SSVC
Exploitation: none Automatable: no Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-02-20 19:29 UTC
CWE
  • CWE-352 - Cross-Site Request Forgery (CSRF)
  • CWE-79 - Cross-site Scripting (XSS)
References
Impacted products
Vendor Product Version CPE status
KubiQ PNG to JPG (WordPress plugin) Affected: <= 4.0 , ≤ 4.0 (custom)
guessed Create a notification for this product.
Date Public
2022-05-06 00:00
Show details on NVD website

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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.
  • 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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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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