CVE-2023-3640 (GCVE-0-2023-3640)

Vulnerability from cvelistv5 – Published: 2023-07-24 15:19 – Updated: 2026-11-09 03:06
VLAI
Title
Kernel: x86/mm: a per-cpu entry area leak was identified through the init_cea_offsets function when prefetchnta and prefetcht2 instructions being used for the per-cpu entry area mapping to the user space
Summary
A possible unauthorized memory access flaw was found in the Linux kernel's cpu_entry_area mapping of X86 CPU data to memory, where a user may guess the location of exception stacks or other important data. Based on the previous CVE-2023-0597, the 'Randomize per-cpu entry area' feature was implemented in /arch/x86/mm/cpu_entry_area.c, which works through the init_cea_offsets() function when KASLR is enabled. However, despite this feature, there is still a risk of per-cpu entry area leaks. This issue could allow a local user to gain access to some important data with memory in an expected location and potentially escalate their privileges on the system.
Severity
7 (High)
CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:H/I:H/A:H
SSVC
Exploitation: none Automatable: no Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-03-05 18:36 UTC
CWE
Assigner
redhat CNA under the redhat root
CNA scorecard B 82/100 over 1184 records in the last 180 days details
References
URL Tags
https://access.redhat.com/errata/RHSA-2023:6583 vendor-advisoryx_refsource_REDHAT
https://access.redhat.com/security/cve/CVE-2023-3640 vdb-entryx_refsource_REDHAT
https://bugzilla.redhat.com/show_bug.cgi?id=2217523 issue-trackingx_refsource_REDHAT
Impacted products
Date Public
2023-06-23 00:00
Credits
Red Hat would like to thank 77pray (Syclover Security Team) for reporting this issue.
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.
Vulnerability timeline

Sightings and recorded dates, grouped by day · UTC

JSON
How durations are calculated

Following the VTEM framework (Black Hat USA 2015), time to patch starts when the vendor or a coordinator was informed, total time to patch at discovery, and disclosure to patch at public disclosure; vendor response time runs from the report to its acknowledgement. Time to exploit and time unpatched start at the earliest discovery or report, falling back to public disclosure. Unpatched time ends at the first recorded patch, or the calculation time when no patch is recorded. Exploitation to patch shows the interval between those two events; a published proof of concept is a public exploit, not exploitation.

Each result shows its source dates. A negative interval means the end event came first; when date-only inputs leave the order open, the result says so. Date-only inputs are approximate. The earliest evidence wins: KEV report dates and sightings are labelled as such. Records describing another vulnerability are listed but never calculated from, and a PoC alone does not establish exploitation. Missing patch evidence does not prove that no patch exists. Filters below change the event list only.

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    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.