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CVE-2019-10153 (GCVE-0-2019-10153)

Vulnerability from cvelistv5 – Published: 2019-07-30 22:10 – Updated: 2024-08-04 22:10
VLAI
Summary
A flaw was discovered in fence-agents, prior to version 4.3.4, where using non-ASCII characters in a guest VM's comment or other fields would cause fence_rhevm to exit with an exception. In cluster environments, this could lead to preventing automated recovery or otherwise denying service to clusters of which that VM is a member.
CWE
Impacted products
Vendor Product Version CPE status
ClusterLabs fence-agents Affected: fixed in 4.3.4
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Show details on NVD website

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      "product_status:recommended": "115",
      "source": "SUSE CSAF VEX",
      "status": "interim",
      "title": "SUSE CVE CVE-2019-10153",
      "url": "https://ftp.suse.com/pub/projects/security/csaf-vex/cve-2019-10153.json",
      "version": "30"
    }
  }
}



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Tags
Taxonomy of the tags.


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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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