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VAR-201901-0454

Vulnerability from variot - Updated: 2024-11-23 23:08

While processing a packet decode request in MQTT, Race condition can occur leading to an out-of-bounds access in snapdragon mobile and snapdragon wear in versions MDM9206, MDM9607, SD 210/SD 212/SD 205, SD 427, SD 435, SD 450, SD 625, SD 636, SD 835, SDA660, SDM630, SDM660, Snapdragon_High_Med_2016. snapdragon mobile and snapdragon wear Contains a race condition vulnerability.Information is obtained, information is altered, and service operation is disrupted (DoS) There is a possibility of being put into a state. Qualcomm MDM9206, etc. are the central processing unit (CPU) products of Qualcomm (Qualcomm) applied to different platforms. A race condition vulnerability exists in several Qualcomm snapdragon products. Attackers can exploit this vulnerability to cause cross-border access

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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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