GHSA-FRHH-W545-JJH2

Vulnerability from github – Published: 2026-05-29 18:31 – Updated: 2026-05-29 18:31
VLAI
Details

The Frontier X2 device allows unauthenticated BLE read/write access to critical GATT characteristics without enforcing pairing authentication or authorization. This allows attackers within BLE range to perform unauthorized control of device functions, including starting/stopping activities, triggering vibrations, causing denial-of-service conditions, and fuzzing characteristic values to induce unexpected behavior. Additionally, the Frontier X mobile application lacks proper BLE device authentication, allowing attackers to impersonate a legitimate Frontier X2 device and connect to the application. By cloning BLE advertisements and exposing expected GATT characteristics, attackers can manipulate activity states and inject fabricated health telemetry such as breathing rate, heart rate, strain, and other health-related data into the mobile application.

Show details on source website

{
  "affected": [],
  "aliases": [
    "CVE-2026-5768"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-306"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2026-05-29T18:17:12Z",
    "severity": "HIGH"
  },
  "details": "The Frontier X2 device allows unauthenticated BLE read/write access to critical GATT characteristics without enforcing pairing authentication or authorization. This allows attackers within BLE range to perform unauthorized control of device functions, including starting/stopping activities, triggering vibrations, causing denial-of-service conditions, and fuzzing characteristic values to induce unexpected behavior. Additionally, the Frontier X mobile application lacks proper BLE device authentication, allowing attackers to impersonate a legitimate Frontier X2 device and connect to the application. By cloning BLE advertisements and exposing expected GATT characteristics, attackers can manipulate activity states and inject fabricated health telemetry such as breathing rate, heart rate, strain, and other health-related data into the mobile application.",
  "id": "GHSA-frhh-w545-jjh2",
  "modified": "2026-05-29T18:31:36Z",
  "published": "2026-05-29T18:31:35Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-5768"
    },
    {
      "type": "WEB",
      "url": "https://fourthfrontier.com/pages/contact-us"
    },
    {
      "type": "WEB",
      "url": "https://github.com/cisagov/CSAF/blob/develop/csaf_files/OT/white/2026/icsma-26-148-01.json"
    },
    {
      "type": "WEB",
      "url": "https://www.cisa.gov/news-events/ics-medical-advisories/icsma-26-148-01"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:A/AC:L/PR:N/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.

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