CVE-2025-23848 (GCVE-0-2025-23848)

Vulnerability from cvelistv5 – Published: 2025-01-16 20:07 – Updated: 2026-05-11 22:41
VLAI
Title
WordPress Hotspots Analytics plugin <= 4.0.12 - CSRF to Stored XSS vulnerability
Summary
Cross-Site Request Forgery (CSRF) vulnerability in dpowney Hotspots Analytics hotspots allows Stored XSS.This issue affects Hotspots Analytics: from n/a through <= 4.0.12.
Severity
7.1 (High)
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:L
SSVC
Exploitation: none Automatable: no Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-01-17 17:17 UTC
CWE
  • CWE-352 - Cross-Site Request Forgery (CSRF)
Assigner
References
Impacted products
Vendor Product Version CPE status
dpowney Hotspots Analytics Affected: 0 , ≤ 4.0.12 (custom)
guessed Create a notification for this product.
Date Public
2026-04-01 16:33
Credits
SOPROBRO | Patchstack Bug Bounty Program
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.



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.