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CVE-2017-2136 (GCVE-0-2017-2136)

Vulnerability from cvelistv5 – Published: 2017-04-28 16:00 – Updated: 2024-08-05 13:39
VLAI
Summary
Cross-site scripting vulnerability in WP Statistics version 12.0.4 and earlier allows remote attackers to inject arbitrary web script or HTML via specially crafted HTTP Referer headers.
Severity
6.1 (Medium)
CVSS:3.0/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:N
CWE
  • Cross-site scripting
Assigner
References
URL Tags
https://wp-statistics.com/change-log/ x_refsource_MISC
http://www.securityfocus.com/bid/97289 vdb-entryx_refsource_BID
http://jvn.jp/en/jp/JVN62392065/index.html third-party-advisoryx_refsource_JVN
Impacted products
Vendor Product Version CPE status
WP Statistics WP Statistics Affected: version 12.0.4 and earlier
guessed Create a notification for this product.
Date Public
2017-04-28 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.


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.