GHSA-F7V6-C4J6-G8WV

Vulnerability from github – Published: 2026-03-25 12:30 – Updated: 2026-05-28 15:39
VLAI
Details

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

tracing: Add NULL pointer check to trigger_data_free()

If trigger_data_alloc() fails and returns NULL, event_hist_trigger_parse() jumps to the out_free error path. While kfree() safely handles a NULL pointer, trigger_data_free() does not. This causes a NULL pointer dereference in trigger_data_free() when evaluating data->cmd_ops->set_filter.

Fix the problem by adding a NULL pointer check to trigger_data_free().

The problem was found by an experimental code review agent based on gemini-3.1-pro while reviewing backports into v6.18.y.

Show details on source website

{
  "affected": [],
  "aliases": [
    "CVE-2026-23309"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-476"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2026-03-25T11:16:26Z",
    "severity": "MODERATE"
  },
  "details": "In the Linux kernel, the following vulnerability has been resolved:\n\ntracing: Add NULL pointer check to trigger_data_free()\n\nIf trigger_data_alloc() fails and returns NULL, event_hist_trigger_parse()\njumps to the out_free error path. While kfree() safely handles a NULL\npointer, trigger_data_free() does not. This causes a NULL pointer\ndereference in trigger_data_free() when evaluating\ndata-\u003ecmd_ops-\u003eset_filter.\n\nFix the problem by adding a NULL pointer check to trigger_data_free().\n\nThe problem was found by an experimental code review agent based on\ngemini-3.1-pro while reviewing backports into v6.18.y.",
  "id": "GHSA-f7v6-c4j6-g8wv",
  "modified": "2026-05-28T15:39:39Z",
  "published": "2026-03-25T12:30:22Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-23309"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/13dcd9269e225e4c4ceabdaeebe2ce4661b54c6e"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/2ce8ece5a78da67834db7728edc801889a64f643"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/42b380f97d65e76e7b310facd525f730272daf57"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/457965c13f0837a289c9164b842d0860133f6274"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/477469223b2b840f436ce204333de87cb17e5d93"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/59c15b9cc453b74beb9f04c6c398717e73612dc3"
    }
  ],
  "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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