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VAR-201808-0506

Vulnerability from variot - Updated: 2023-12-18 13:08

An Out-of-Bounds Read Privilege Escalation vulnerability in Trend Micro Security 2018 (Consumer) products could allow a local attacker to escalate privileges on vulnerable installations. An attacker must first obtain the ability to execute low-privileged code on the target system in order to exploit the vulnerability. Trend Micro Security 2018 (Consumer) The product contains vulnerabilities related to out-of-bounds reading and vulnerabilities related to authorization, authority, and access control.Information is obtained, information is altered, and service operation is disrupted (DoS) There is a possibility of being put into a state. The issue results from the lack of proper validation of user-supplied data, which can result in a memory access past the end of an allocated buffer

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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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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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