CVE-2019-1644 (GCVE-0-2019-1644)

Vulnerability from cvelistv5 – Published: 2019-01-23 23:00 – Updated: 2024-11-19 19:17
VLAI
Title
Cisco IoT Field Network Director Resource Exhaustion Denial of Service Vulnerability
Summary
A vulnerability in the UDP protocol implementation for Cisco IoT Field Network Director (IoT-FND) could allow an unauthenticated, remote attacker to exhaust system resources, resulting in a denial of service (DoS) condition. The vulnerability is due to improper resource management for UDP ingress packets. An attacker could exploit this vulnerability by sending a high rate of UDP packets to an affected system within a short period of time. A successful exploit could allow the attacker to exhaust available system resources, resulting in a DoS condition.
Severity
7.5 (High)
CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
SSVC
Exploitation: none Automatable: yes Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-11-19 17:25 UTC
CWE
Assigner
References
URL Tags
https://tools.cisco.com/security/center/content/C… vendor-advisoryx_refsource_CISCO
http://www.securityfocus.com/bid/106709 vdb-entryx_refsource_BID
Impacted products
Date Public
2019-01-23 00:00
Show details on NVD website
To clipboard 

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  • 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.