GHSA-JR6W-9RM3-JJ48

Vulnerability from github – Published: 2026-07-19 18:31 – Updated: 2026-07-19 18:31
VLAI
Details

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

iio: imu: adis16550: fix stack leak in trigger handler

adis16550_trigger_handler() declares the scan data array on the stack without initializing it. The memcpy() at the bottom fills only the first 28 bytes (TEMP + 6 channels of GYRO/ACCEL data), and iio_push_to_buffers_with_timestamp() writes the s64 timestamp at the 8-byte-aligned offset 32. Bytes 28-31 remain uninitialized stack data which leaks to userspace on ever trigger.

Fix this all by just zero-initializing the structure on the stack.

Show details on source website

{
  "affected": [],
  "aliases": [
    "CVE-2026-63966"
  ],
  "database_specific": {
    "cwe_ids": [],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2026-07-19T16:17:15Z",
    "severity": null
  },
  "details": "In the Linux kernel, the following vulnerability has been resolved:\n\niio: imu: adis16550: fix stack leak in trigger handler\n\nadis16550_trigger_handler() declares the scan data array on the stack\nwithout initializing it.  The memcpy() at the bottom fills only the\nfirst 28 bytes (TEMP + 6 channels of GYRO/ACCEL data), and\niio_push_to_buffers_with_timestamp() writes the s64 timestamp at the\n8-byte-aligned offset 32.  Bytes 28-31 remain uninitialized stack data\nwhich leaks to userspace on ever trigger.\n\nFix this all by just zero-initializing the structure on the stack.",
  "id": "GHSA-jr6w-9rm3-jj48",
  "modified": "2026-07-19T18:31:47Z",
  "published": "2026-07-19T18:31:47Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-63966"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/474f8928d50b09f7dcf507049f08732640b88b49"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/c2c255444392872cbbf46d640cb3a938e8000309"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/ce582b22dd2ff15ac99101c22ec1559d1febe2ff"
    }
  ],
  "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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