VAR-202106-0607

Vulnerability from variot - Updated: 2024-08-14 13:13

There is an information leak vulnerability in Huawei products. A module does not deal with specific input sufficiently. High privilege attackers can exploit this vulnerability by performing some operations. This can lead to information leak. Affected product versions include: IPS Module versions V500R005C00, V500R005C10, V500R005C20; NGFW Module versions V500R005C00,V500R005C10, V500R005C20; SeMG9811 versions V500R005C00; USG9500 versions V500R001C00, V500R001C20, V500R001C30, V500R001C50, V500R001C60, V500R001C80, V500R005C00, V500R005C10, V500R005C20. plural Huawei The product contains an input verification vulnerability.Information may be obtained. Pillow is a Python-based image processing library. There is currently no information about this vulnerability, please feel free to follow CNNVD or manufacturer announcements

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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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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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