GHSA-XRR8-P4PF-HFWR
Vulnerability from github – Published: 2025-04-16 15:34 – Updated: 2026-06-19 15:33
VLAI
Details
In the Linux kernel, the following vulnerability has been resolved:
nfsd: don't ignore the return code of svc_proc_register()
Currently, nfsd_proc_stat_init() ignores the return value of svc_proc_register(). If the procfile creation fails, then the kernel will WARN when it tries to remove the entry later.
Fix nfsd_proc_stat_init() to return the same type of pointer as svc_proc_register(), and fix up nfsd_net_init() to check that and fail the nfsd_net construction if it occurs.
svc_proc_register() can fail if the dentry can't be allocated, or if an identical dentry already exists. The second case is pretty unlikely in the nfsd_net construction codepath, so if this happens, return -ENOMEM.
Severity
5.5 (Medium)
{
"affected": [],
"aliases": [
"CVE-2025-22026"
],
"database_specific": {
"cwe_ids": [
"CWE-252"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2025-04-16T15:15:55Z",
"severity": "MODERATE"
},
"details": "In the Linux kernel, the following vulnerability has been resolved:\n\nnfsd: don\u0027t ignore the return code of svc_proc_register()\n\nCurrently, nfsd_proc_stat_init() ignores the return value of\nsvc_proc_register(). If the procfile creation fails, then the kernel\nwill WARN when it tries to remove the entry later.\n\nFix nfsd_proc_stat_init() to return the same type of pointer as\nsvc_proc_register(), and fix up nfsd_net_init() to check that and fail\nthe nfsd_net construction if it occurs.\n\nsvc_proc_register() can fail if the dentry can\u0027t be allocated, or if an\nidentical dentry already exists. The second case is pretty unlikely in\nthe nfsd_net construction codepath, so if this happens, return -ENOMEM.",
"id": "GHSA-xrr8-p4pf-hfwr",
"modified": "2026-06-19T15:33:07Z",
"published": "2025-04-16T15:34:39Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2025-22026"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/30405b23b4d5e2a596fb756d48119d7293194e75"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/51107e768de6821b68aa34a136d07bc7a09cf6c3"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/51da899c209a9624e48be416bd30e7ed5cd6c3d8"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/6a59b70fe71ec66c0dd19e2c279c71846a3fb2f0"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/930b64ca0c511521f0abdd1d57ce52b2a6e3476b"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/9d9456185fd5f1891c74354ee297f19538141ead"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/e31957a819e60cf0bc9a49408765e6095fd3d046"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/e4316bd85e42b01125b2aab691769234a388b8a3"
}
],
"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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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
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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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