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CVE-2018-4852 (GCVE-0-2018-4852)

Vulnerability from cvelistv5 – Published: 2018-07-03 14:00 – Updated: 2024-09-17 03:07
VLAI
Summary
A vulnerability has been identified in SICLOCK TC100 (All versions) and SICLOCK TC400 (All versions). An attacker with network access to the device could potentially circumvent the authentication mechanism if he/she is able to obtain certain knowledge specific to the attacked device.
Severity
9.8 (Critical)
CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
CWE
  • CWE-288 - Authentication Bypass Using an Alternate Path or Channel
Assigner
References
Impacted products
Vendor Product Version CPE status
Siemens AG SICLOCK TC100, SICLOCK TC400 Affected: SICLOCK TC100 : All versions
Affected: SICLOCK TC400 : All versions
guessed Create a notification for this product.
Date Public
2018-07-03 00:00
Show details on NVD website
To clipboard 

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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.


Detection rules are retrieved from Rulezet.


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.