GHSA-G94M-267G-8J9M

Vulnerability from github – Published: 2026-07-25 12:31 – Updated: 2026-07-25 12:31
VLAI
Details

In the Linux kernel, the following vulnerability has been resolved:

iio: accel: kxsd9: fix runtime PM imbalance on write_raw() error

kxsd9_write_raw() takes a runtime PM reference with pm_runtime_get_sync() but returns -EINVAL directly when a scale with a non-zero integer part is requested, skipping the matching pm_runtime_put_autosuspend(). This leaks a runtime PM usage-counter reference on every such write, after which the device can no longer autosuspend.

Set the error code and fall through to the existing put instead of returning early.

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{
  "affected": [],
  "aliases": [
    "CVE-2026-64503"
  ],
  "database_specific": {
    "cwe_ids": [],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2026-07-25T10:17:36Z",
    "severity": null
  },
  "details": "In the Linux kernel, the following vulnerability has been resolved:\n\niio: accel: kxsd9: fix runtime PM imbalance on write_raw() error\n\nkxsd9_write_raw() takes a runtime PM reference with pm_runtime_get_sync()\nbut returns -EINVAL directly when a scale with a non-zero integer part is\nrequested, skipping the matching pm_runtime_put_autosuspend(). This leaks\na runtime PM usage-counter reference on every such write, after which the\ndevice can no longer autosuspend.\n\nSet the error code and fall through to the existing put instead of\nreturning early.",
  "id": "GHSA-g94m-267g-8j9m",
  "modified": "2026-07-25T12:31:38Z",
  "published": "2026-07-25T12:31:38Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-64503"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/13a91e8631cfeb68e5b7fd6687f194f5a86e83fe"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/191fcfeb729ededd8dd2a999c6bf351ddfa0cec7"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/223703d6e8bed50b6a0b47e160877909518d94b9"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/36154171385a8a2444a4b3c6eaa0c5294cb02478"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/44a5fd874bb6873bdaec59f722c1d57832fbc9df"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/6293211d142605bec435229ef0aa3668b8964164"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/a93fd69c1ab0854ac4f5b8439c26dfadb25dfd20"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/eeece4a85ece6f3837c75ef26a9b2bf5a1d0fcfc"
    }
  ],
  "schema_version": "1.4.0",
  "severity": []
}



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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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