CVE-2018-5244 (GCVE-0-2018-5244)

Vulnerability from cvelistv5 – Published: 2018-01-05 18:00 – Updated: 2024-08-05 05:33
VLAI
Summary
In Xen 4.10, new infrastructure was introduced as part of an overhaul to how MSR emulation happens for guests. Unfortunately, one tracking structure isn't freed when a vcpu is destroyed. This allows guest OS administrators to cause a denial of service (host OS memory consumption) by rebooting many times.
Severity
6.5 (Medium)
CVSS:3.0/AV:L/AC:L/PR:L/UI:N/S:C/C:N/I:N/A:H
CWE
  • n/a
Assigner
References
URL Tags
https://security.gentoo.org/glsa/201810-06 vendor-advisoryx_refsource_GENTOO
http://www.securityfocus.com/bid/102433 vdb-entryx_refsource_BID
http://www.securitytracker.com/id/1040774 vdb-entryx_refsource_SECTRACK
https://xenbits.xen.org/xsa/advisory-253.html x_refsource_CONFIRM
Date Public
2018-01-05 00:00
Show details on NVD website
To clipboard 

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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.
  • 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.


Detection rules are retrieved from Rulezet.


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.