CVE-2026-68443 (GCVE-0-2026-68443)

Vulnerability from cvelistv5 – Published: 2026-08-12 00:07 – Updated: 2026-08-17 05:05
VLAI
Title
hwmon: (gigabyte_waterforce) Stop device IO before calling hid_hw_stop
Summary
In the Linux kernel, the following vulnerability has been resolved: hwmon: (gigabyte_waterforce) Stop device IO before calling hid_hw_stop Calling hid_hw_stop() does not stop the device IO. This results in a race condition between hid_input_report() and the point immediately following the execution of hid_device_io_start() within the driver probe function. If the probe operation fails after "io start" has been initiated, this race condition will result in a UAF vulnerability. Fix the problem by calling hid_device_io_stop() before calling hid_hw_stop().
Severity
No CVSS data available.
Assigner
References
Impacted products
Vendor Product Version
Linux Linux Affected: 42ac68e3d4ba06ad17bc56b790dbccc37e76e0ba , < a855f678ba37ef82c4dd22926ad740ecfb8dbedf (git)
Affected: 42ac68e3d4ba06ad17bc56b790dbccc37e76e0ba , < f36e12cc8cfe996d627b8a82bd9df9e43270f6e2 (git)
Affected: 42ac68e3d4ba06ad17bc56b790dbccc37e76e0ba , < 0842e9faab04f784d01125085195031252ff9695 (git)
Affected: 42ac68e3d4ba06ad17bc56b790dbccc37e76e0ba , < ff0c5c53d08274e200b48a4d53aa078265e873cb (git)
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Linux Linux Affected: 6.8
Unaffected: 0 , < 6.8 (semver)
Unaffected: 6.12.101 , ≤ 6.12.* (semver)
Unaffected: 6.18.42 , ≤ 6.18.* (semver)
Unaffected: 7.1.6 , ≤ 7.1.* (semver)
Unaffected: 7.2 , ≤ * (original_commit_for_fix)
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}



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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.

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  • 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.
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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.