GHSA-33Q9-XPR9-FMXV

Vulnerability from github – Published: 2026-09-16 12:30 – Updated: 2026-09-16 15:31
VLAI
Details

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

media: saa7164: fix cleanup on resource allocation failure

saa7164_dev_setup() adds the device to the global saa7164_devlist before requesting the PCI BAR memory regions.

If get_resources() fails, saa7164_dev_setup() decrements the device count and returns an error, but leaves the device on saa7164_devlist. The probe error path then frees the device, leaving a dangling entry on the global list.

Reuse the existing MMIO mapping error path to remove the device from saa7164_devlist and decrement the device count before returning.

Also release BAR0 if it was successfully requested but the BAR2 request fails.

Show details on source website

{
  "affected": [],
  "aliases": [
    "CVE-2026-89877"
  ],
  "database_specific": {
    "cwe_ids": [],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2026-09-16T11:16:55Z",
    "severity": "HIGH"
  },
  "details": "In the Linux kernel, the following vulnerability has been resolved:\n\nmedia: saa7164: fix cleanup on resource allocation failure\n\nsaa7164_dev_setup() adds the device to the global saa7164_devlist before\nrequesting the PCI BAR memory regions.\n\nIf get_resources() fails, saa7164_dev_setup() decrements the device count\nand returns an error, but leaves the device on saa7164_devlist. The probe\nerror path then frees the device, leaving a dangling entry on the global\nlist.\n\nReuse the existing MMIO mapping error path to remove the device from\nsaa7164_devlist and decrement the device count before returning.\n\nAlso release BAR0 if it was successfully requested but the BAR2 request\nfails.",
  "id": "GHSA-33q9-xpr9-fmxv",
  "modified": "2026-09-16T15:31:00Z",
  "published": "2026-09-16T12:30:39Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-89877"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/28e84c6e2e6753ed238ea097b2842a32a6a6879b"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/4d2048466af9e2c960c5be6dcd317e3b657d268b"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/69259c627009bf7621010ae9e997dad2dad3339c"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/77f216f8de6221efb6f6c471bc9e598f1879825b"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/daa5b32cdf7db81a07ee2a91ce092a363e7754aa"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/fc65623524b5bf23baddc6ae688d954fba0d5de2"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:U/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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