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CVE-2023-1390 (GCVE-0-2023-1390)

Vulnerability from cvelistv5 – Published: 2023-03-16 00:00 – Updated: 2025-04-23 16:23
VLAI
Summary
A remote denial of service vulnerability was found in the Linux kernel’s TIPC kernel module. The while loop in tipc_link_xmit() hits an unknown state while attempting to parse SKBs, which are not in the queue. Sending two small UDP packets to a system with a UDP bearer results in the CPU utilization for the system to instantly spike to 100%, causing a denial of service condition.
Severity
7.5 (High)
CVSS:3.1/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 2025-04-23 13:26 UTC
CWE
Assigner
References
Impacted products
Vendor Product Version CPE status
n/a Linux kernel (TIPC kernel module) Affected: Fixed in kernel 5.11-rc4
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

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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.