CVE-2025-39891 (GCVE-0-2025-39891)
Vulnerability from cvelistv5 – Published: 2025-10-01 07:42 – Updated: 2026-05-11 21:38
VLAI
EPSS
VEX
Title
wifi: mwifiex: Initialize the chan_stats array to zero
Summary
In the Linux kernel, the following vulnerability has been resolved:
wifi: mwifiex: Initialize the chan_stats array to zero
The adapter->chan_stats[] array is initialized in
mwifiex_init_channel_scan_gap() with vmalloc(), which doesn't zero out
memory. The array is filled in mwifiex_update_chan_statistics()
and then the user can query the data in mwifiex_cfg80211_dump_survey().
There are two potential issues here. What if the user calls
mwifiex_cfg80211_dump_survey() before the data has been filled in.
Also the mwifiex_update_chan_statistics() function doesn't necessarily
initialize the whole array. Since the array was not initialized at
the start that could result in an information leak.
Also this array is pretty small. It's a maximum of 900 bytes so it's
more appropriate to use kcalloc() instead vmalloc().
Severity
7.1 (High)
Assigner
References
10 references
Impacted products
2 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Linux | Linux |
Affected:
bf35443314acb43fa8a3f9f8046e14cbe178762b , < 9eb0118b3470b4d2e4e3bbb1fc088b30c0285d65
(git)
Affected: bf35443314acb43fa8a3f9f8046e14cbe178762b , < 05daef0442d28350a1a0d6d0e2cab4a7a91df475 (git) Affected: bf35443314acb43fa8a3f9f8046e14cbe178762b , < acdf26a912190fc6746e2a890d7d0338190527b4 (git) Affected: bf35443314acb43fa8a3f9f8046e14cbe178762b , < 32c124c9c03aa755cbaf60ef7f76afd918d47659 (git) Affected: bf35443314acb43fa8a3f9f8046e14cbe178762b , < 9df29aa5637d94d24f7c5f054ef4feaa7b766111 (git) Affected: bf35443314acb43fa8a3f9f8046e14cbe178762b , < 06616410a3e5e6cd1de5b7cbc668f1a7edeedad9 (git) Affected: bf35443314acb43fa8a3f9f8046e14cbe178762b , < 5285b7009dc1e09d5bb9e05fae82e1a807882dbc (git) Affected: bf35443314acb43fa8a3f9f8046e14cbe178762b , < 0e20450829ca3c1dbc2db536391537c57a40fe0b (git) |
guessed | |
| Linux | Linux |
Affected:
3.19
Unaffected: 0 , < 3.19 (semver) Unaffected: 5.4.299 , ≤ 5.4.* (semver) Unaffected: 5.10.243 , ≤ 5.10.* (semver) Unaffected: 5.15.192 , ≤ 5.15.* (semver) Unaffected: 6.1.151 , ≤ 6.1.* (semver) Unaffected: 6.6.105 , ≤ 6.6.* (semver) Unaffected: 6.12.46 , ≤ 6.12.* (semver) Unaffected: 6.16.6 , ≤ 6.16.* (semver) Unaffected: 6.17 , ≤ * (original_commit_for_fix) |
guessed |
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"value": "In the Linux kernel, the following vulnerability has been resolved:\n\nwifi: mwifiex: Initialize the chan_stats array to zero\n\nThe adapter-\u003echan_stats[] array is initialized in\nmwifiex_init_channel_scan_gap() with vmalloc(), which doesn\u0027t zero out\nmemory. The array is filled in mwifiex_update_chan_statistics()\nand then the user can query the data in mwifiex_cfg80211_dump_survey().\n\nThere are two potential issues here. What if the user calls\nmwifiex_cfg80211_dump_survey() before the data has been filled in.\nAlso the mwifiex_update_chan_statistics() function doesn\u0027t necessarily\ninitialize the whole array. Since the array was not initialized at\nthe start that could result in an information leak.\n\nAlso this array is pretty small. It\u0027s a maximum of 900 bytes so it\u0027s\nmore appropriate to use kcalloc() instead vmalloc()."
}
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"url": "https://git.kernel.org/stable/c/32c124c9c03aa755cbaf60ef7f76afd918d47659"
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"url": "https://git.kernel.org/stable/c/06616410a3e5e6cd1de5b7cbc668f1a7edeedad9"
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},
{
"url": "https://git.kernel.org/stable/c/0e20450829ca3c1dbc2db536391537c57a40fe0b"
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"title": "wifi: mwifiex: Initialize the chan_stats array to zero",
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"redhat_vex": {
"aggregate_severity": "Moderate",
"current_release_date": "2026-06-30T01:24:20+00:00",
"cve": "CVE-2025-39891",
"id": "CVE-2025-39891",
"initial_release_date": "2025-10-01T00:00:00+00:00",
"product_status:known_affected": "232",
"product_status:known_not_affected": "42",
"source": "Red Hat CSAF VEX",
"status": "final",
"title": "kernel: wifi: mwifiex: Initialize the chan_stats array to zero",
"url": "https://security.access.redhat.com/data/csaf/v2/vex/2025/cve-2025-39891.json",
"version": "3"
}
}
}
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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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