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CVE-2017-8627 (GCVE-0-2017-8627)

Vulnerability from cvelistv5 – Published: 2017-08-08 21:00 – Updated: 2024-09-16 22:19
VLAI
Summary
Windows Subsystem for Linux in Windows 10 1703, allows a denial of service vulnerability due to the way it handles objects in memory, aka "Windows Subsystem for Linux Denial of Service Vulnerability".
Severity
4.7 (Medium)
CVSS:3.0/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:H
CWE
  • Denial of Service
Assigner
References
URL Tags
https://portal.msrc.microsoft.com/en-US/security-… x_refsource_CONFIRM
http://www.securityfocus.com/bid/100065 vdb-entryx_refsource_BID
http://www.securitytracker.com/id/1039103 vdb-entryx_refsource_SECTRACK
Impacted products
Vendor Product Version CPE status
Microsoft Corporation Windows Subsystem for Linux Affected: Windows 10 1703
guessed Create a notification for this product.
Date Public
2017-08-08 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

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.


Detection rules are retrieved from Rulezet.


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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.