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CVE-2022-3543 (GCVE-0-2022-3543)

Vulnerability from cvelistv5 – Published: 2022-10-17 00:00 – Updated: 2024-08-03 01:14
VLAI
Title
Linux Kernel BPF af_unix.c unix_release_sock memory leak
Summary
A vulnerability, which was classified as problematic, has been found in Linux Kernel. This issue affects the function unix_sock_destructor/unix_release_sock of the file net/unix/af_unix.c of the component BPF. The manipulation leads to memory leak. It is recommended to apply a patch to fix this issue. The associated identifier of this vulnerability is VDB-211043.
Severity
3.5 (Low)
CVSS:3.1/AV:A/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:L
CWE
  • CWE-404 - Denial of Service -> CWE-401 Memory Leak
Assigner
References
Impacted products
Vendor Product Version CPE status
Linux Kernel Affected: n/a
guessed Create a notification for this product.
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.