CVE-2023-52683 (GCVE-0-2023-52683)
Vulnerability from cvelistv5 – Published: 2024-05-17 14:24 – Updated: 2026-05-11 19:31
VLAI
EPSS
VEX
Title
ACPI: LPIT: Avoid u32 multiplication overflow
Summary
In the Linux kernel, the following vulnerability has been resolved:
ACPI: LPIT: Avoid u32 multiplication overflow
In lpit_update_residency() there is a possibility of overflow
in multiplication, if tsc_khz is large enough (> UINT_MAX/1000).
Change multiplication to mul_u32_u32().
Found by Linux Verification Center (linuxtesting.org) with SVACE.
Severity
No CVSS data available.
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-05-28 19:43 UTC
Assigner
References
10 references
Impacted products
2 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Linux | Linux |
Affected:
eeb2d80d502af28e5660ff4bbe00f90ceb82c2db , < 647d1d50c31e60ef9ccb9756a8fdf863329f7aee
(git)
Affected: eeb2d80d502af28e5660ff4bbe00f90ceb82c2db , < 6c38e791bde07d6ca2a0a619ff9b6837e0d5f9ad (git) Affected: eeb2d80d502af28e5660ff4bbe00f90ceb82c2db , < f39c3d578c7d09a18ceaf56750fc7f20b02ada63 (git) Affected: eeb2d80d502af28e5660ff4bbe00f90ceb82c2db , < c1814a4ffd016ce5392c6767d22ef3aa2f0d4bd1 (git) Affected: eeb2d80d502af28e5660ff4bbe00f90ceb82c2db , < 72222dfd76a79d9666ab3117fcdd44ca8cd0c4de (git) Affected: eeb2d80d502af28e5660ff4bbe00f90ceb82c2db , < d1ac288b2742aa4af746c5613bac71760fadd1c4 (git) Affected: eeb2d80d502af28e5660ff4bbe00f90ceb82c2db , < b7aab9d906e2e252a7783f872406033ec49b6dae (git) Affected: eeb2d80d502af28e5660ff4bbe00f90ceb82c2db , < 56d2eeda87995245300836ee4dbd13b002311782 (git) |
guessed | |
| Linux | Linux |
Affected:
4.15
Unaffected: 0 , < 4.15 (semver) Unaffected: 4.19.306 , ≤ 4.19.* (semver) Unaffected: 5.4.268 , ≤ 5.4.* (semver) Unaffected: 5.10.209 , ≤ 5.10.* (semver) Unaffected: 5.15.148 , ≤ 5.15.* (semver) Unaffected: 6.1.75 , ≤ 6.1.* (semver) Unaffected: 6.6.14 , ≤ 6.6.* (semver) Unaffected: 6.7.2 , ≤ 6.7.* (semver) Unaffected: 6.8 , ≤ * (original_commit_for_fix) |
guessed |
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
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.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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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.
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