GHSA-7634-6GF7-R2F6
Vulnerability from github – Published: 2026-09-16 12:30 – Updated: 2026-09-16 15:31
VLAI
Details
In the Linux kernel, the following vulnerability has been resolved:
usb: typec: qcom-pmic: cancel reset_work on stop
pdphy_stop() disables IRQs but leaves reset_work pending. If the IRQ handler schedules it just before disable_irq(), the work runs after remove() frees the struct via devm.
Call cancel_work_sync() after disabling IRQs to close the window.
This issue was found by an in-house static analysis tool.
Severity
7.8 (High)
{
"affected": [],
"aliases": [
"CVE-2026-90026"
],
"database_specific": {
"cwe_ids": [],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2026-09-16T11:17:15Z",
"severity": "HIGH"
},
"details": "In the Linux kernel, the following vulnerability has been resolved:\n\nusb: typec: qcom-pmic: cancel reset_work on stop\n\npdphy_stop() disables IRQs but leaves reset_work pending. If the IRQ\nhandler schedules it just before disable_irq(), the work runs after\nremove() frees the struct via devm.\n\nCall cancel_work_sync() after disabling IRQs to close the window.\n\nThis issue was found by an in-house static analysis tool.",
"id": "GHSA-7634-6gf7-r2f6",
"modified": "2026-09-16T15:31:07Z",
"published": "2026-09-16T12:30:45Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-90026"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/0b69b166852dbf1f9532b22bd49f502e5970eb95"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/52d556f08547733948cc40b8b11e6b68dccee7b2"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/7b0df6efd143f8085bdb68778a013a46f1349913"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/b9a7eed472edbfa8dec0fdeafd5e796550a8a8b7"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/d4e00a1eb39174e25ef759b8fb1111bba8e87b1e"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H",
"type": "CVSS_V3"
}
]
}
Loading…
Loading…
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.
Loading…
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.
Loading…
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.
Loading…