VAR-201708-0034

Vulnerability from variot - Updated: 2023-12-18 12:57

The DCMProvider service in Samsung LibQjpeg on a Samsung SM-G925V device running build number LRX22G.G925VVRU1AOE2 allows remote attackers to cause a denial of service (segmentation fault and process crash) and execute arbitrary code via a crafted JPG. Samsung SM-G925V The device contains a buffer error vulnerability.Information is obtained, information is altered, and service operation is disrupted (DoS) There is a possibility of being put into a state. Samsung LibQjpeg is prone to a remote memory-corruption vulnerability. Attackers can exploit this issue to execute arbitrary code in the context of the user running the affected applications. Failed exploit attempts will likely cause a denial-of-service condition. Samsung Galaxy S6 is a smart phone released by South Korea's Samsung (Samsung). Samsung LibQjpeg is one of the image decoding libraries

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Taxonomy of the tags.


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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.

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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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