GHSA-28GW-3RJM-8334
Vulnerability from github – Published: 2026-09-17 18:32 – Updated: 2026-09-17 18:32
VLAI
Details
In the Linux kernel, the following vulnerability has been resolved:
drm/amdgpu/pm/powerplay: bounds-check voltage index in Vega10 lookup
vddInd, vddciInd and mvddInd from VBIOS-parsed tables index into vddc, vddci and vddmem lookup tables without bounds checks across nine sites. Return -EINVAL when any index is out of range.
{
"affected": [],
"aliases": [
"CVE-2026-93177"
],
"database_specific": {
"cwe_ids": [],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2026-09-17T17:18:13Z",
"severity": null
},
"details": "In the Linux kernel, the following vulnerability has been resolved:\n\ndrm/amdgpu/pm/powerplay: bounds-check voltage index in Vega10 lookup\n\nvddInd, vddciInd and mvddInd from VBIOS-parsed tables index into vddc,\nvddci and vddmem lookup tables without bounds checks across nine sites.\nReturn -EINVAL when any index is out of range.",
"id": "GHSA-28gw-3rjm-8334",
"modified": "2026-09-17T18:32:13Z",
"published": "2026-09-17T18:32:13Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-93177"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/06aef6dcc1d52da5112cbcde39c062e493247ab5"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/206e478810d6af50b25d8da72e3af8d55a014365"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/25dedc13ceb9b6109c79c92a726ca4ca017c9eaa"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/46d27e56dbd6345b9c7b62b67ec57407bf3bf29a"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/6fa33f594e46e775a94097f71b486d7b006b6917"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/ae25c92d91f2cb90ede2b0eb0f58efa82ccc718b"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/b349dcf061a6dc8c6cef9a10530331ef2ff66c72"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/dfe89f1a0c7f40ef858b93881198f9728df956cf"
}
],
"schema_version": "1.4.0",
"severity": []
}
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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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