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CVE-2019-17094 (GCVE-0-2019-17094)

Vulnerability from cvelistv5 – Published: 2020-01-27 17:14 – Updated: 2024-09-16 22:34
VLAI
Title
Stack-Based Overflow vulnerability in Belkin WeMo Insights Switch
Summary
A Stack-based Buffer Overflow vulnerability in libbelkin_api.so component of Belkin WeMo Insight Switch firmware allows a local attacker to obtain code execution on the device. This issue affects: Belkin WeMo Insight Switch firmware version 2.00.11396 and prior versions.
Severity
8.3 (High)
CVSS:3.1/AV:A/AC:H/PR:N/UI:N/S:C/C:H/I:H/A:H
CWE
  • CWE-121 - Stack-based Buffer Overflow
Assigner
References
Impacted products
Vendor Product Version CPE status
Belkin Belkin WeMo Insight Switch Affected: 2.00.11396 and prior
guessed Create a notification for this product.
Date Public
2019-12-20 00:00
Credits
Bitdefender Labs
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.