GHSA-8RWJ-765P-2628

Vulnerability from github – Published: 2026-07-19 18:31 – Updated: 2026-08-12 15:30
VLAI
Details

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

mm/memory_hotplug: fix memory block reference leak on remove

Patch series "mm: Fix memory block leaks and locking", v2.

This series fixes two memory block device reference leaks and one locking issue around the per-memory_block hwpoison counter.

This patch (of 2):

remove_memory_blocks_and_altmaps() looks up each memory block with find_memory_block(), which acquires a reference to the memory block device.

That reference is never dropped on this path, resulting in a leaked device reference when removing memory blocks and their altmaps. Drop the reference after retrieving mem->altmap and clearing mem->altmap, before removing the memory block device.

Show details on source website

{
  "affected": [],
  "aliases": [
    "CVE-2026-64180"
  ],
  "database_specific": {
    "cwe_ids": [],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2026-07-19T16:18:00Z",
    "severity": "MODERATE"
  },
  "details": "In the Linux kernel, the following vulnerability has been resolved:\n\nmm/memory_hotplug: fix memory block reference leak on remove\n\nPatch series \"mm: Fix memory block leaks and locking\", v2.\n\nThis series fixes two memory block device reference leaks and one locking\nissue around the per-memory_block hwpoison counter.\n\n\nThis patch (of 2):\n\nremove_memory_blocks_and_altmaps() looks up each memory block with\nfind_memory_block(), which acquires a reference to the memory block\ndevice.\n\nThat reference is never dropped on this path, resulting in a leaked device\nreference when removing memory blocks and their altmaps.  Drop the\nreference after retrieving mem-\u003ealtmap and clearing mem-\u003ealtmap, before\nremoving the memory block device.",
  "id": "GHSA-8rwj-765p-2628",
  "modified": "2026-08-12T15:30:27Z",
  "published": "2026-07-19T18:31:54Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-64180"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/09ce923071e7852ece60d7368e05249bf32c7967"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/93866f55f7e292fe3d47d36c9efe5ee10213a06b"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/b8ab30c79fc00147125b9c39f928561d9dd13d06"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/df64c0d21c3f85f844b2f656333e43d97e6ffa74"
    }
  ],
  "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"
    }
  ]
}



Log in or create an account to share your comment.




Tags
Taxonomy of the tags.


Loading…

Loading…

Loading…

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.

Sightings

Author Source Type Date Other

Nomenclature

  • Seen: The vulnerability was mentioned, discussed, or observed by the user.
  • Confirmed: The vulnerability has been validated from an analyst's perspective.
  • Published Proof of Concept: A public proof of concept is available for this vulnerability.
  • Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
  • Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
  • Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
  • Not confirmed: The user expressed doubt about the validity of the vulnerability.
  • Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.

Loading…

Detection rules are retrieved from Rulezet.

Loading…

Loading…

Related by attack behaviour

Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.


Loading…