GHSA-34QJ-QVM5-MPRQ

Vulnerability from github – Published: 2026-08-10 15:33 – Updated: 2026-08-19 18:32
VLAI
Details

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

drm/amdgpu/vce: fix integer overflow in image size

Fix a security vulnerability where malicious VCE command streams with oversized dimensions (e.g. 65536×65536) cause 32-bit integer overflow, wrapping the calculated buffer size to 0. This bypasses validation and allows GPU firmware to perform out-of-bound memory access.

The fix uses 64-bit arithmetic to detect overflow and rejects invalid dimensions before they reach the hardware.

V2: remove redundant check V3: modify max height value V4: remove size64

(cherry picked from commit cbe408dba581755ad1279a487ec786d8927d778d)

Show details on source website

{
  "affected": [],
  "aliases": [
    "CVE-2026-68108"
  ],
  "database_specific": {
    "cwe_ids": [],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2026-08-10T13:19:55Z",
    "severity": "HIGH"
  },
  "details": "In the Linux kernel, the following vulnerability has been resolved:\n\ndrm/amdgpu/vce: fix integer overflow in image size\n\nFix a security vulnerability where malicious VCE command streams\nwith oversized dimensions (e.g. 65536\u00d765536) cause 32-bit integer\noverflow, wrapping the calculated buffer size to 0. This bypasses\nvalidation and allows GPU firmware to perform out-of-bound memory\naccess.\n\nThe fix uses 64-bit arithmetic to detect overflow and rejects\ninvalid dimensions before they reach the hardware.\n\nV2: remove redundant check\nV3: modify max height value\nV4: remove size64\n\n(cherry picked from commit cbe408dba581755ad1279a487ec786d8927d778d)",
  "id": "GHSA-34qj-qvm5-mprq",
  "modified": "2026-08-19T18:32:05Z",
  "published": "2026-08-10T15:33:36Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-68108"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/00c311a13d225266800c712f2b7db2711c6897de"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/186bfdc4e26d019b2e7570cb121964a1d89b2e5b"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/7eebef042c12dfe0568593ee6a8926d16505925e"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/893db20383800cfe92e638705984eebb13bc81a5"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/a07430abd556de3707adfcadcc60db3fa64e4b2b"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/a6d7065b91a14790980ce6f4960db0ca8c3c9940"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H",
      "type": "CVSS_V3"
    }
  ]
}



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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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