GHSA-VFMM-VGXH-2C77

Vulnerability from github – Published: 2026-09-16 12:30 – Updated: 2026-09-16 15:30
VLAI
Details

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

drm/panthor: harden firmware build-info bounds checks

panthor_fw_read_build_info() checks whether the metadata range fits in the firmware image with hdr.meta_start + hdr.meta_size. Both fields are u32, so the addition can wrap and let an out-of-bounds range pass validation.

The function also reads the "git_sha: " prefix without first checking that the metadata is long enough, and meta_size == 0 can underflow the NULL terminator index.

Use subtraction-based bounds checking and reject metadata that is too short to contain the expected prefix and trailing NULL byte.

Show details on source website

{
  "affected": [],
  "aliases": [
    "CVE-2026-89826"
  ],
  "database_specific": {
    "cwe_ids": [],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2026-09-16T11:16:49Z",
    "severity": "HIGH"
  },
  "details": "In the Linux kernel, the following vulnerability has been resolved:\n\ndrm/panthor: harden firmware build-info bounds checks\n\npanthor_fw_read_build_info() checks whether the metadata range fits in the\nfirmware image with hdr.meta_start + hdr.meta_size. Both fields are u32, so\nthe addition can wrap and let an out-of-bounds range pass validation.\n\nThe function also reads the \"git_sha: \" prefix without first checking that\nthe metadata is long enough, and meta_size == 0 can underflow the NULL\nterminator index.\n\nUse subtraction-based bounds checking and reject metadata that is too short\nto contain the expected prefix and trailing NULL byte.",
  "id": "GHSA-vfmm-vgxh-2c77",
  "modified": "2026-09-16T15:30:58Z",
  "published": "2026-09-16T12:30:38Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-89826"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/5516f1acfd07564361cf306cc90a8e513df0f096"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/6ae19dce3e8c13ae73590b3f4311abe9f96bbae8"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/8321b093fa6c297b80586460ce6914d9655df170"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:N/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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