GHSA-5VWG-96Q8-MM82

Vulnerability from github – Published: 2025-04-16 15:34 – Updated: 2025-05-06 18:30
VLAI
Details

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

ublk: make sure ubq->canceling is set when queue is frozen

Now ublk driver depends on ubq->canceling for deciding if the request can be dispatched via uring_cmd & io_uring_cmd_complete_in_task().

Once ubq->canceling is set, the uring_cmd can be done via ublk_cancel_cmd() and io_uring_cmd_done().

So set ubq->canceling when queue is frozen, this way makes sure that the flag can be observed from ublk_queue_rq() reliably, and avoids use-after-free on uring_cmd.

Show details on source website

{
  "affected": [],
  "aliases": [
    "CVE-2025-22068"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-416"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2025-04-16T15:16:01Z",
    "severity": "HIGH"
  },
  "details": "In the Linux kernel, the following vulnerability has been resolved:\n\nublk: make sure ubq-\u003ecanceling is set when queue is frozen\n\nNow ublk driver depends on `ubq-\u003ecanceling` for deciding if the request\ncan be dispatched via uring_cmd \u0026 io_uring_cmd_complete_in_task().\n\nOnce ubq-\u003ecanceling is set, the uring_cmd can be done via ublk_cancel_cmd()\nand io_uring_cmd_done().\n\nSo set ubq-\u003ecanceling when queue is frozen, this way makes sure that the\nflag can be observed from ublk_queue_rq() reliably, and avoids\nuse-after-free on uring_cmd.",
  "id": "GHSA-5vwg-96q8-mm82",
  "modified": "2025-05-06T18:30:36Z",
  "published": "2025-04-16T15:34:42Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2025-22068"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/5491400589e7572c2d2627ed6384302f7672aa1d"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/7e3497d7dacb5aee69dd9be842b778083cae0e75"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/8741d0737921ec1c03cf59aebf4d01400c2b461a"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/9158359015f0eda00e521e35b7bc7ebce176aebf"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H",
      "type": "CVSS_V3"
    }
  ]
}



Log in or create an account to share your comment.




Tags
Taxonomy of the tags.


Loading…

Loading…

Loading…

Drag nodes to arrange them. Double-click a related vulnerability to open it, or a source to visit its link. The Physics button on the left tunes the layout and the simulation; the legend at the bottom left hides or shows nodes by kind and by sighting type.

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.
Vulnerability timeline

Sightings and recorded dates, grouped by day · UTC

JSON
Events included in the timeline

Loading timeline…


    Loading…

    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.