CVE-2026-41172 (GCVE-0-2026-41172)

Vulnerability from cvelistv5 – Published: 2026-04-22 21:22 – Updated: 2026-04-23 12:52
VLAI
Title
Squidex vulnerable to Server-Side Request Forgery (SSRF) via URL-based asset upload (/api/apps/{app}/assets)
Summary
Squidex is an open source headless content management system and content management hub. Prior to version 7.23.0, an SSRF vulnerability allows a user with asset upload permission to force the server to fetch arbitrary URLs, including localhost/private network targets, and persist the response as an asset. Version 7.23.0 contains a fix.
Severity
7.3 (High)
CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:N/SC:N/SI:N/SA:N/E:P
SSVC
Exploitation: poc Automatable: yes Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-04-23 12:52 UTC
CWE
  • CWE-918 - Server-Side Request Forgery (SSRF)
Assigner
References
Impacted products
Vendor Product Version
Squidex squidex Affected: < 7.23.0
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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

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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.



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.