GHSA-W3G4-6G98-PJQR

Vulnerability from github – Published: 2026-05-01 15:30 – Updated: 2026-05-03 09:33
VLAI?
Details

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

perf/x86: Fix potential bad container_of in intel_pmu_hw_config

Auto counter reload may have a group of events with software events present within it. The software event PMU isn't the x86_hybrid_pmu and a container_of operation in intel_pmu_set_acr_caused_constr (via the hybrid helper) could cause out of bound memory reads. Avoid this by guarding the call to intel_pmu_set_acr_caused_constr with an is_x86_event check.

Show details on source website

{
  "affected": [],
  "aliases": [
    "CVE-2026-31782"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-125"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2026-05-01T15:16:41Z",
    "severity": "HIGH"
  },
  "details": "In the Linux kernel, the following vulnerability has been resolved:\n\nperf/x86: Fix potential bad container_of in intel_pmu_hw_config\n\nAuto counter reload may have a group of events with software events\npresent within it. The software event PMU isn\u0027t the x86_hybrid_pmu and\na container_of operation in intel_pmu_set_acr_caused_constr (via the\nhybrid helper) could cause out of bound memory reads. Avoid this by\nguarding the call to intel_pmu_set_acr_caused_constr with an\nis_x86_event check.",
  "id": "GHSA-w3g4-6g98-pjqr",
  "modified": "2026-05-03T09:33:10Z",
  "published": "2026-05-01T15:30:35Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-31782"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/bfee04838f636d064bc92075c65c95f739003804"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/dbde07f06226438cd2cf1179745fa1bec5d8914a"
    },
    {
      "type": "WEB",
      "url": "https://git.kernel.org/stable/c/e435a30ca6fe14c9611b1fc731c98a6d28410247"
    }
  ],
  "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"
    }
  ]
}


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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.

Sightings

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Nomenclature

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