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CVE-2024-6433 (GCVE-0-2024-6433)

Vulnerability from cvelistv5 – Published: 2024-07-10 00:00 – Updated: 2024-08-01 21:41
VLAI
Title
Local File Inclusion in stitionai/devika
Summary
The application zips all the files in the folder specified by the user, which allows an attacker to read arbitrary files on the system by providing a crafted path. This vulnerability can be exploited by sending a request to the application with a malicious snapshot_path parameter.
SSVC
Exploitation: poc Automatable: no Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-07-23 18:49 UTC
CWE
  • CWE-23 - Relative Path Traversal
Impacted products
Vendor Product Version CPE status
stitionai stitionai/devika Affected: unspecified , < - (custom)
guessed Create a notification for this product.
stitionai devika Affected: 0
    cpe:2.3:a:stitionai:devika:*:*:*:*:*:*:*:*
Create a notification for this product.
Show details on NVD website

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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.

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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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