CVE-2026-49287 (GCVE-0-2026-49287)

Vulnerability from cvelistv5 – Published: 2026-06-19 17:36 – Updated: 2026-06-22 14:22
VLAI
Title
Statamic CMS vulnerable to unsafe method invocation via collection sorting allows data destruction
Summary
Statamic is a Laravel and Git powered content management system (CMS). Prior to 5.73.23 and 6.20.0, the fix for CVE-2026-41175 was incomplete. It addressed the issue in the query builder, but the same protection was not applied to in-memory collection sorting. Manipulating sort parameters could result in the loss of content and assets. This requires a front-end template that passes request input into a tag's sort parameter. It is not exploitable by default — a template would need to be explicitly set up to sort by a visitor-controlled value. This has been fixed in 5.73.23 and 6.20.0.
SSVC
Exploitation: none Automatable: no Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-06-22 14:21 UTC
CWE
  • CWE-470 - Use of Externally-Controlled Input to Select Classes or Code ('Unsafe Reflection')
References
Impacted products
Vendor Product Version CPE status
statamic cms Affected: < 5.73.23
Affected: >= 6.0.0, < 6.20.0
guessed Create a notification for this product.
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

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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.
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  • 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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