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CVE-2026-66391 (GCVE-0-2026-66391)

Vulnerability from cvelistv5 – Published: 2026-07-27 16:45 – Updated: 2026-07-28 14:49
VLAI
Title
Apache Wicket: leaked and missing CSP headers
Summary
Use of Insufficiently Random Values, Protection Mechanism Failure vulnerability in Apache Wicket. This issue affects Apache Wicket: from 9.0.0 through 9.23.0, from 10.0.0 through 10.9.0. Users are recommended to upgrade to version 10.10.0, which fixes the issue.
SSVC
Exploitation: none Automatable: yes Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-07-27 18:04 UTC
CWE
  • CWE-330 - Use of Insufficiently Random Values
  • CWE-693 - Protection Mechanism Failure
Impacted products
Vendor Product Version CPE status
Apache Software Foundation Apache Wicket Affected: 9.0.0 , ≤ 9.23.0 (semver)
Affected: 10.0.0 , ≤ 10.9.0 (semver)
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

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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