CVE-2026-64276 (GCVE-0-2026-64276)
Vulnerability from cvelistv5 – Published: 2026-07-25 08:49 – Updated: 2026-08-17 04:52
VLAI
EPSS
VEX
Title
Input: synaptics-rmi4 - bound the F30 keymap to the GPIO/LED count
Summary
In the Linux kernel, the following vulnerability has been resolved:
Input: synaptics-rmi4 - bound the F30 keymap to the GPIO/LED count
rmi_f30_map_gpios() allocates gpioled_key_map with
min(gpioled_count, TRACKSTICK_RANGE_END) == at most 6 entries, but
rmi_f30_attention() iterates the full f30->gpioled_count (device query
register, range 0..31) and dereferences gpioled_key_map[i], and
input->keycodemax is set to the full gpioled_count while input->keycode
points at the 6-entry allocation.
A device that reports gpioled_count > 6 with GPIO support enabled
therefore causes an out-of-bounds read on the attention interrupt and
out-of-bounds read/write through the EVIOCGKEYCODE/EVIOCSKEYCODE ioctls,
which bound the index only against keycodemax. This is the same defect
as the F3A handler, which was copied from F30.
Size the keymap for the full gpioled_count; the mapping loop still
assigns only the first min(gpioled_count, TRACKSTICK_RANGE_END) entries.
Severity
7.8 (High)
Assigner
References
8 references
Impacted products
2 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Linux | Linux |
Affected:
3e64fcbdbd10e46dede502d507dbcc104837cd59 , < 8c6d18d61bb6fe0e6edf848413391c590552e8a9
(git)
Affected: 3e64fcbdbd10e46dede502d507dbcc104837cd59 , < d162a1ead7de404d8b41a093c83ed0db6487cded (git) Affected: 3e64fcbdbd10e46dede502d507dbcc104837cd59 , < f0be9eba946e9200b43265e0a748d38bd0a56954 (git) Affected: 3e64fcbdbd10e46dede502d507dbcc104837cd59 , < 26c895928d7118436a24f564587cb4aefc40cdd8 (git) Affected: 3e64fcbdbd10e46dede502d507dbcc104837cd59 , < 4e3689c26854356f41fbaa1eafa382e58ac79e00 (git) Affected: 3e64fcbdbd10e46dede502d507dbcc104837cd59 , < e849c6f51e6877104c765da084e001ec37c8e119 (git) Affected: 3e64fcbdbd10e46dede502d507dbcc104837cd59 , < bfe622efecd4ff0a792d0ecd1a8dce535a902f50 (git) Affected: 3e64fcbdbd10e46dede502d507dbcc104837cd59 , < d577e46785d45484b2ab7e7309c49b18764bf56c (git) |
guessed | |
| Linux | Linux |
Affected:
4.14
Unaffected: 0 , < 4.14 (semver) Unaffected: 5.10.261 , ≤ 5.10.* (semver) Unaffected: 5.15.212 , ≤ 5.15.* (semver) Unaffected: 6.1.178 , ≤ 6.1.* (semver) Unaffected: 6.6.145 , ≤ 6.6.* (semver) Unaffected: 6.12.96 , ≤ 6.12.* (semver) Unaffected: 6.18.39 , ≤ 6.18.* (semver) Unaffected: 7.1.4 , ≤ 7.1.* (semver) Unaffected: 7.2 , ≤ * (original_commit_for_fix) |
guessed |
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"value": "In the Linux kernel, the following vulnerability has been resolved:\n\nInput: synaptics-rmi4 - bound the F30 keymap to the GPIO/LED count\n\nrmi_f30_map_gpios() allocates gpioled_key_map with\nmin(gpioled_count, TRACKSTICK_RANGE_END) == at most 6 entries, but\nrmi_f30_attention() iterates the full f30-\u003egpioled_count (device query\nregister, range 0..31) and dereferences gpioled_key_map[i], and\ninput-\u003ekeycodemax is set to the full gpioled_count while input-\u003ekeycode\npoints at the 6-entry allocation.\n\nA device that reports gpioled_count \u003e 6 with GPIO support enabled\ntherefore causes an out-of-bounds read on the attention interrupt and\nout-of-bounds read/write through the EVIOCGKEYCODE/EVIOCSKEYCODE ioctls,\nwhich bound the index only against keycodemax. This is the same defect\nas the F3A handler, which was copied from F30.\n\nSize the keymap for the full gpioled_count; the mapping loop still\nassigns only the first min(gpioled_count, TRACKSTICK_RANGE_END) entries."
}
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"lastModified": "2026-08-17T05:17:26.747",
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"references": [
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"source": "416baaa9-dc9f-4396-8d5f-8c081fb06d67",
"tags": [
"Patch"
],
"url": "https://git.kernel.org/stable/c/26c895928d7118436a24f564587cb4aefc40cdd8"
},
{
"source": "416baaa9-dc9f-4396-8d5f-8c081fb06d67",
"tags": [
"Patch"
],
"url": "https://git.kernel.org/stable/c/4e3689c26854356f41fbaa1eafa382e58ac79e00"
},
{
"source": "416baaa9-dc9f-4396-8d5f-8c081fb06d67",
"tags": [
"Patch"
],
"url": "https://git.kernel.org/stable/c/8c6d18d61bb6fe0e6edf848413391c590552e8a9"
},
{
"source": "416baaa9-dc9f-4396-8d5f-8c081fb06d67",
"tags": [
"Patch"
],
"url": "https://git.kernel.org/stable/c/bfe622efecd4ff0a792d0ecd1a8dce535a902f50"
},
{
"source": "416baaa9-dc9f-4396-8d5f-8c081fb06d67",
"tags": [
"Patch"
],
"url": "https://git.kernel.org/stable/c/d162a1ead7de404d8b41a093c83ed0db6487cded"
},
{
"source": "416baaa9-dc9f-4396-8d5f-8c081fb06d67",
"tags": [
"Patch"
],
"url": "https://git.kernel.org/stable/c/d577e46785d45484b2ab7e7309c49b18764bf56c"
},
{
"source": "416baaa9-dc9f-4396-8d5f-8c081fb06d67",
"tags": [
"Patch"
],
"url": "https://git.kernel.org/stable/c/e849c6f51e6877104c765da084e001ec37c8e119"
},
{
"source": "416baaa9-dc9f-4396-8d5f-8c081fb06d67",
"tags": [
"Patch"
],
"url": "https://git.kernel.org/stable/c/f0be9eba946e9200b43265e0a748d38bd0a56954"
}
],
"sourceIdentifier": "416baaa9-dc9f-4396-8d5f-8c081fb06d67",
"vulnStatus": "Analyzed",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-787"
}
],
"source": "nvd@nist.gov",
"type": "Primary"
}
]
}
},
"redhat_vex": {
"aggregate_severity": "Moderate",
"current_release_date": "2026-09-07T19:38:03+00:00",
"cve": "CVE-2026-64276",
"id": "CVE-2026-64276",
"initial_release_date": "2026-07-25T00:00:00+00:00",
"product_status:fixed": "762",
"product_status:known_affected": "50",
"product_status:known_not_affected": "14",
"source": "Red Hat CSAF VEX",
"status": "final",
"title": "kernel: Input: synaptics-rmi4 - bound the F30 keymap to the GPIO/LED count",
"url": "https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-64276.json",
"version": "3"
},
"suse_vex": {
"aggregate_severity": "moderate",
"current_release_date": "2026-10-03T00:47:28Z",
"cve": "CVE-2026-64276",
"id": "CVE-2026-64276",
"initial_release_date": "2026-07-25T16:46:47Z",
"product_status:known_affected": "580",
"product_status:known_not_affected": "155",
"product_status:recommended": "359",
"source": "SUSE CSAF VEX",
"status": "interim",
"title": "SUSE CVE CVE-2026-64276",
"url": "https://ftp.suse.com/pub/projects/security/csaf-vex/cve-2026-64276.json",
"version": "10"
}
}
}
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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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