GHSA-CC2M-7VJR-3594
Vulnerability from github – Published: 2026-09-16 12:30 – Updated: 2026-09-16 12:30
VLAI
Details
In the Linux kernel, the following vulnerability has been resolved:
usb: image: mdc800: change kmalloc() to kzalloc()
Change the kmalloc() calls in usb_mdc800_init() for irq_urb_buffer and download_urb_buffer to kzalloc(), avoiding potential stack leaks if a shorter message is received in mdc800_usb_irq() and mdc800_usb_download_notify()
{
"affected": [],
"aliases": [
"CVE-2026-90034"
],
"database_specific": {
"cwe_ids": [],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2026-09-16T11:17:16Z",
"severity": null
},
"details": "In the Linux kernel, the following vulnerability has been resolved:\n\nusb: image: mdc800: change kmalloc() to kzalloc()\n\nChange the kmalloc() calls in usb_mdc800_init() for irq_urb_buffer and\ndownload_urb_buffer to kzalloc(), avoiding potential stack leaks if a\nshorter message is received in mdc800_usb_irq() and\nmdc800_usb_download_notify()",
"id": "GHSA-cc2m-7vjr-3594",
"modified": "2026-09-16T12:30:46Z",
"published": "2026-09-16T12:30:46Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-90034"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/2430eb81e44111b30eeb5273bbcf8b24ca517ef9"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/2df8f7720ed91f1aad4f128553b6e90c7c5fac1e"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/553c375e86a49882e95840565512e17bff31cc3a"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/67c6726dd6048a2781aa43a2bf9fcf1e16ee3a7d"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/6c601410d1a9ae645f604fe2f61b9f4942c77dfb"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/838455cc8bfe1278150d1d776529edea6cd4c1dd"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/8c38049879f2108f57c98f03cc7f3db51a12bdab"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/e22428f0c038c23109c0383a235aa607b0cd4c95"
}
],
"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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