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GHSA-VX44-4WRP-9HJF

Vulnerability from github – Published: 2026-07-28 12:31 – Updated: 2026-07-28 12:31
VLAI
Details

A flaw was found in GStreamer's gst-plugins-good. A heap-based out-of-bounds read of 4 bytes can occur when parsing FLAC audio stream headers embedded in a Matroska or WebM container file. The vulnerability is triggered by a boundary check that does not account for the full size of the data being copied, allowing a small read past the end of the allocated buffer. An attacker could exploit this by crafting a malicious Matroska or WebM file and tricking a user into opening it, potentially leaking a small amount of adjacent heap memory.

Show details on source website

{
  "affected": [],
  "aliases": [
    "CVE-2026-17072"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-125"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2026-07-28T11:17:02Z",
    "severity": "LOW"
  },
  "details": "A flaw was found in GStreamer\u0027s gst-plugins-good. A heap-based out-of-bounds read of 4 bytes can occur when parsing FLAC audio stream headers embedded in a Matroska or WebM container file. The vulnerability is triggered by a boundary check that does not account for the full size of the data being copied, allowing a small read past the end of the allocated buffer. An attacker could exploit this by crafting a malicious Matroska or WebM file and tricking a user into opening it, potentially leaking a small amount of adjacent heap memory.",
  "id": "GHSA-vx44-4wrp-9hjf",
  "modified": "2026-07-28T12:31:20Z",
  "published": "2026-07-28T12:31:20Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-17072"
    },
    {
      "type": "WEB",
      "url": "https://access.redhat.com/security/cve/CVE-2026-17072"
    },
    {
      "type": "WEB",
      "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2506750"
    },
    {
      "type": "WEB",
      "url": "https://gitlab.freedesktop.org/gstreamer/gstreamer-security/-/merge_requests/111"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:L/I:N/A:N",
      "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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