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CVE-2025-68950 (GCVE-0-2025-68950)

Vulnerability from cvelistv5 – Published: 2025-12-30 16:50 – Updated: 2025-12-30 18:11
VLAI
Title
Magick's failure to limit MVG mutual references forming a loop
Summary
ImageMagick is free and open-source software used for editing and manipulating digital images. Prior to version 7.1.2-12, Magick fails to check for circular references between two MVGs, leading to a stack overflow. This is a DoS vulnerability, and any situation that allows reading the mvg file will be affected. Version 7.1.2-12 fixes the issue.
SSVC
Exploitation: none Automatable: no Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-12-30 18:09 UTC
CWE
References
Impacted products
Vendor Product Version CPE status
ImageMagick ImageMagick Affected: < 7.1.2-12
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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Detection rules are retrieved from Rulezet.

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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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