VAR-201412-0412

Vulnerability from variot - Updated: 2023-12-18 13:57

Integer overflow in Trihedral Engineering VTScada (formerly VTS) 6.5 through 9.x before 9.1.20, 10.x before 10.2.22, and 11.x before 11.1.07 allows remote attackers to cause a denial of service (server crash) via a crafted request, which triggers a large memory allocation. Authentication is not required to exploit this vulnerability. The specific flaw exists within the included HTTP server. By providing a small negative content length, an attacker is able to cause an integer overflow, resulting in the allocation of too small a buffer. The resulting heap overwrite will terminate the HTTP server. Trihedral VTScada is a SCADA industrial control product. Trihedral VTScada is prone to a denial-of-service vulnerability. Successful exploits can crash the affected application, denying service to legitimate users

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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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