GHSA-W642-F79M-MR3C
Vulnerability from github – Published: 2025-05-01 15:31 – Updated: 2025-11-10 21:30
VLAI
Details
In the Linux kernel, the following vulnerability has been resolved:
mISDN: fix possible memory leak in mISDN_dsp_element_register()
Afer commit 1fa5ae857bb1 ("driver core: get rid of struct device's bus_id string array"), the name of device is allocated dynamically, use put_device() to give up the reference, so that the name can be freed in kobject_cleanup() when the refcount is 0.
The 'entry' is going to be freed in mISDN_dsp_dev_release(), so the kfree() is removed. list_del() is called in mISDN_dsp_dev_release(), so it need be initialized.
Severity
5.5 (Medium)
{
"affected": [],
"aliases": [
"CVE-2022-49821"
],
"database_specific": {
"cwe_ids": [
"CWE-401"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2025-05-01T15:16:05Z",
"severity": "MODERATE"
},
"details": "In the Linux kernel, the following vulnerability has been resolved:\n\nmISDN: fix possible memory leak in mISDN_dsp_element_register()\n\nAfer commit 1fa5ae857bb1 (\"driver core: get rid of struct device\u0027s\nbus_id string array\"), the name of device is allocated dynamically,\nuse put_device() to give up the reference, so that the name can be\nfreed in kobject_cleanup() when the refcount is 0.\n\nThe \u0027entry\u0027 is going to be freed in mISDN_dsp_dev_release(), so the\nkfree() is removed. list_del() is called in mISDN_dsp_dev_release(),\nso it need be initialized.",
"id": "GHSA-w642-f79m-mr3c",
"modified": "2025-11-10T21:30:27Z",
"published": "2025-05-01T15:31:48Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2022-49821"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/083a2c9ef82e184bdf0b9f9a1e5fc38d32afbb47"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/0f2c681900a01e3f23789bca26d88268c3d5b51d"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/727ed7d28348c026c7ef4d852f3d0e5054d376e8"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/7a05e3929668c8cfef495c69752a9e91fac4878f"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/98a2ac1ca8fd6eca6867726fe238d06e75eb1acd"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/b119bedbefb7dd9ed8bf8cb9f1056504250d610e"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/bbd53d05c4c892080ef3b617eff4f57903acecb9"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/d4b8394725079670be309f9a35ad88a8cbbaaefd"
}
],
"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.
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- 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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