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CVE-2018-15727 (GCVE-0-2018-15727)

Vulnerability from cvelistv5 – Published: 2018-08-29 15:00 – Updated: 2024-08-05 10:01
VLAI
Summary
Grafana 2.x, 3.x, and 4.x before 4.6.4 and 5.x before 5.2.3 allows authentication bypass because an attacker can generate a valid "remember me" cookie knowing only a username of an LDAP or OAuth user.
Severity
9.8 (Critical)
CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
CWE
  • n/a
Assigner
References
URL Tags
http://www.securityfocus.com/bid/105184 vdb-entryx_refsource_BID
https://access.redhat.com/errata/RHSA-2019:0019 vendor-advisoryx_refsource_REDHAT
https://access.redhat.com/errata/RHSA-2018:3829 vendor-advisoryx_refsource_REDHAT
https://grafana.com/blog/2018/08/29/grafana-5.2.3… x_refsource_CONFIRM
Date Public
2018-08-29 00:00
Show details on NVD website
To clipboard 

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}



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Tags
Taxonomy of the tags.





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.


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.