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CVE-2024-39090 (GCVE-0-2024-39090)

Vulnerability from cvelistv5 – Published: 2024-07-18 00:00 – Updated: 2024-08-02 04:19
VLAI
Summary
The PHPGurukul Online Shopping Portal Project version 2.0 contains a vulnerability that allows Cross-Site Request Forgery (CSRF) to lead to Stored Cross-Site Scripting (XSS). An attacker can exploit this vulnerability to execute arbitrary JavaScript code in the context of a user's session, potentially leading to account takeover.
Severity
6.1 (Medium)
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/A:N
SSVC
Exploitation: poc Automatable: no Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-07-23 13:15 UTC
CWE
  • n/a
  • CWE-352 - Cross-Site Request Forgery (CSRF)
Assigner
References
Impacted products
Vendor Product Version CPE status
phpgurukul online_shopping_portal Affected: 2.0
    cpe:2.3:a:phpgurukul:online_shopping_portal:2.0:*:*:*:*:*:*:*
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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.