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GHSA-VPR7-CGQ2-G5RR

Vulnerability from github – Published: 2024-08-22 06:30 – Updated: 2024-08-23 03:30
VLAI
Details

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

net/mlx5: DR, Fix slab-out-of-bounds in mlx5_cmd_dr_create_fte

When adding a rule with 32 destinations, we hit the following out-of-band access issue:

BUG: KASAN: slab-out-of-bounds in mlx5_cmd_dr_create_fte+0x18ee/0x1e70

This patch fixes the issue by both increasing the allocated buffers to accommodate for the needed actions and by checking the number of actions to prevent this issue when a rule with too many actions is provided.

Show details on source website

{
  "affected": [],
  "aliases": [
    "CVE-2022-48932"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-125"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2024-08-22T04:15:16Z",
    "severity": "MODERATE"
  },
  "details": "In the Linux kernel, the following vulnerability has been resolved:\n\nnet/mlx5: DR, Fix slab-out-of-bounds in mlx5_cmd_dr_create_fte\n\nWhen adding a rule with 32 destinations, we hit the following out-of-band\naccess issue:\n\n  BUG: KASAN: slab-out-of-bounds in mlx5_cmd_dr_create_fte+0x18ee/0x1e70\n\nThis patch fixes the issue by both increasing the allocated buffers to\naccommodate for the needed actions and by checking the number of actions\nto prevent this issue when a rule with too many actions is provided.",
  "id": "GHSA-vpr7-cgq2-g5rr",
  "modified": "2024-08-23T03:30:59Z",
  "published": "2024-08-22T06:30:29Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2022-48932"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/0aec12d97b2036af0946e3d582144739860ac07b"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/4ad319cdfbe555b4ff67bc608736c46a6930c848"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/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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