VAR-201804-0137

Vulnerability from variot - Updated: 2024-11-23 19:40

An information disclosure vulnerability in the Qualcomm USB driver. Product: Android. Versions: Android kernel. Android ID: A-33280689. References: QC-CR#1102418. This vulnerability Android ID: A-33280689 and Qualcomm QC-CR#1102418 It is published asInformation may be obtained. GooglePixel/PixelXL is a smartphone from Google Inc. in the United States. An attacker could exploit this vulnerability to obtain potentially sensitive information that could lead to further attacks. Google Pixel/Pixel XL is prone to an information-disclosure vulnerability. Information obtained may aid in further attacks

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          "@vocab": "https://www.variotdbs.pl/ref/sources#"
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    },
    "data": "information disclosure",
    "sources": [
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        "db": "CNNVD",
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    "trust": 0.6
  }
}



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Tags
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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Detection rules are retrieved from Rulezet.

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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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