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CVE-2023-3610 (GCVE-0-2023-3610)

Vulnerability from cvelistv5 – Published: 2023-07-21 20:48 – Updated: 2025-02-13 16:56
VLAI
Title
Use-after-free in Linux kernel's netfilter: nf_tables component
Summary
A use-after-free vulnerability in the Linux kernel's netfilter: nf_tables component can be exploited to achieve local privilege escalation. Flaw in the error handling of bound chains causes a use-after-free in the abort path of NFT_MSG_NEWRULE. The vulnerability requires CAP_NET_ADMIN to be triggered. We recommend upgrading past commit 4bedf9eee016286c835e3d8fa981ddece5338795.
Severity
7.8 (High)
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
CWE
Assigner
References
Impacted products
Vendor Product Version CPE status
Linux Kernel Affected: 5.9 , < 6.4 (custom)
guessed Create a notification for this product.
Date Public
2023-06-20 20:41
Show details on NVD website
To clipboard 

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}



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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.