GHSA-59W4-GMXC-G79M
Vulnerability from github – Published: 2025-10-07 18:31 – Updated: 2026-02-06 15:30
VLAI
Details
In the Linux kernel, the following vulnerability has been resolved:
drm/amd/display: Fix potential null-deref in dm_resume
[Why] Fixing smatch error: dm_resume() error: we previously assumed 'aconnector->dc_link' could be null
[How] Check if dc_link null at the beginning of the loop, so further checks can be dropped.
Severity
5.5 (Medium)
{
"affected": [],
"aliases": [
"CVE-2022-50535"
],
"database_specific": {
"cwe_ids": [
"CWE-476"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2025-10-07T16:15:37Z",
"severity": "MODERATE"
},
"details": "In the Linux kernel, the following vulnerability has been resolved:\n\ndrm/amd/display: Fix potential null-deref in dm_resume\n\n[Why]\nFixing smatch error:\ndm_resume() error: we previously assumed \u0027aconnector-\u003edc_link\u0027 could be null\n\n[How]\nCheck if dc_link null at the beginning of the loop,\nso further checks can be dropped.",
"id": "GHSA-59w4-gmxc-g79m",
"modified": "2026-02-06T15:30:59Z",
"published": "2025-10-07T18:31:08Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2022-50535"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/00b655fa96b4e941351cc4bf5ca755a65ae94a8e"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/7a7175a2cd84b7874bebbf8e59f134557a34161b"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/8e365f1bd672cc9320a936f6ae6f8087aa40e9bc"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/9f73793b81637c60ccc83cc508645310b8ab7d80"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/bb9a5562beb982aa5ebb73c521c49596ff8b8030"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/d236103782de25736996a45bd36ac2a89bdc93c6"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/fd79b61af2782f8875c78f50cdb8630ec43e2990"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
]
}
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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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