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CVE-2017-4934 (GCVE-0-2017-4934)

Vulnerability from cvelistv5 – Published: 2017-11-17 14:00 – Updated: 2024-09-16 20:47
VLAI
Summary
VMware Workstation (12.x before 12.5.8) and Fusion (8.x before 8.5.9) contain a heap buffer-overflow vulnerability in VMNAT device. This issue may allow a guest to execute code on the host.
CWE
  • Heap buffer-overflow vulnerability
References
URL Tags
https://www.vmware.com/security/advisories/VMSA-2… x_refsource_CONFIRM
http://www.securityfocus.com/bid/101903 vdb-entryx_refsource_BID
http://www.securitytracker.com/id/1039835 vdb-entryx_refsource_SECTRACK
Impacted products
Vendor Product Version CPE status
VMware Workstation Affected: 12.x before 12.5.8
guessed Create a notification for this product.
VMware Fusion Affected: 8.x before 8.5.9
guessed Create a notification for this product.
Date Public
2017-11-16 00:00
Show details on NVD website

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  }
}



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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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