CVE-2020-3948 (GCVE-0-2020-3948)

Vulnerability from cvelistv5 – Published: 2020-03-16 17:17 – Updated: 2024-08-04 07:52
VLAI
Summary
Linux Guest VMs running on VMware Workstation (15.x before 15.5.2) and Fusion (11.x before 11.5.2) contain a local privilege escalation vulnerability due to improper file permissions in Cortado Thinprint. Local attackers with non-administrative access to a Linux guest VM with virtual printing enabled may exploit this issue to elevate their privileges to root on the same guest VM.
Severity
7.8 (High)
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
CWE
  • Local privilege escalation vulnerability
Assigner
References
Impacted products
Vendor Product Version CPE status
VMware Workstation Affected: 15.x before 15.5.2
guessed Create a notification for this product.
VMware Fusion Affected: 11.x before 11.5.2
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.



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.