GHSA-9H33-65CJ-82PC
Vulnerability from github – Published: 2026-05-27 15:33 – Updated: 2026-06-16 15:33
VLAI
Details
In the Linux kernel, the following vulnerability has been resolved:
scsi: sd: fix missing put_disk() when device_add(&disk_dev) fails
If device_add(&sdkp->disk_dev) fails, put_device() runs scsi_disk_release(), which frees the scsi_disk but leaves the gendisk referenced. The device_add_disk() error path in sd_probe() calls put_disk(gd); call put_disk(gd) here to mirror that cleanup.
Severity
5.5 (Medium)
{
"affected": [],
"aliases": [
"CVE-2026-45997"
],
"database_specific": {
"cwe_ids": [],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2026-05-27T14:17:17Z",
"severity": "MODERATE"
},
"details": "In the Linux kernel, the following vulnerability has been resolved:\n\nscsi: sd: fix missing put_disk() when device_add(\u0026disk_dev) fails\n\nIf device_add(\u0026sdkp-\u003edisk_dev) fails, put_device() runs\nscsi_disk_release(), which frees the scsi_disk but leaves the gendisk\nreferenced. The device_add_disk() error path in sd_probe() calls\nput_disk(gd); call put_disk(gd) here to mirror that cleanup.",
"id": "GHSA-9h33-65cj-82pc",
"modified": "2026-06-16T15:33:40Z",
"published": "2026-05-27T15:33:20Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-45997"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/13e550fbfccdb311e76ec96892dfe35f0dba0657"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/1e111c4b3a726df1254670a5cc4868cedb946d37"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/262152ec37101f9dc524743ccdbd6c7641d14573"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/2c2c14b7dfccad8c5a28802849e40c21252e4c28"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/a95d38c5701431bfc826e7b18acc0785919d5c88"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/b64b4f499801b12d0e2785447e4df6c164c608a9"
}
],
"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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