CVE-2024-46725 (GCVE-0-2024-46725)
Vulnerability from cvelistv5 – Published: 2024-09-18 06:32 – Updated: 2026-08-05 11:38
VLAI
EPSS
VEX
Title
drm/amdgpu: Fix out-of-bounds write warning
Summary
In the Linux kernel, the following vulnerability has been resolved:
drm/amdgpu: Fix out-of-bounds write warning
Check the ring type value to fix the out-of-bounds
write warning
Severity
7.8 (High)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-09-29 14:55 UTC
Assigner
References
8 references
Impacted products
2 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Linux | Linux |
Affected:
d38ceaf99ed015f2a0b9af3499791bd3a3daae21 , < c253b87c7c37ec40a2e0c84e4a6b636ba5cd66b2
(git)
Affected: d38ceaf99ed015f2a0b9af3499791bd3a3daae21 , < 919f9bf9997b8dcdc132485ea96121e7d15555f9 (git) Affected: d38ceaf99ed015f2a0b9af3499791bd3a3daae21 , < cf2db220b38301b6486a0f11da24a0f317de558c (git) Affected: d38ceaf99ed015f2a0b9af3499791bd3a3daae21 , < a60d1f7ff62e453dde2d3b4907e178954d199844 (git) Affected: d38ceaf99ed015f2a0b9af3499791bd3a3daae21 , < 130bee397b9cd52006145c87a456fd8719390cb5 (git) Affected: d38ceaf99ed015f2a0b9af3499791bd3a3daae21 , < be1684930f5262a622d40ce7a6f1423530d87f89 (git) |
guessed | |
| Linux | Linux |
Affected:
4.2
Unaffected: 0 , < 4.2 (semver) Unaffected: 5.10.226 , ≤ 5.10.* (semver) Unaffected: 5.15.167 , ≤ 5.15.* (semver) Unaffected: 6.1.109 , ≤ 6.1.* (semver) Unaffected: 6.6.50 , ≤ 6.6.* (semver) Unaffected: 6.10.9 , ≤ 6.10.* (semver) Unaffected: 6.11 , ≤ * (original_commit_for_fix) |
guessed |
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"value": "En el kernel de Linux, se ha resuelto la siguiente vulnerabilidad: drm/amdgpu: Se corrige la advertencia de escritura fuera de los l\u00edmites Verifique el valor del tipo de anillo para corregir la advertencia de escritura fuera de los l\u00edmites"
}
],
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"source": "SUSE CSAF VEX",
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"url": "https://ftp.suse.com/pub/projects/security/csaf-vex/cve-2024-46725.json",
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"vulnrichment": {
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],
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"metrics": [
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"options": [
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},
{
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},
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"product": "Linux",
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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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