PYSEC-2026-592

Vulnerability from pysec - Published: 2026-06-30 20:41 - Updated: 2026-06-30 20:41
VLAI
Details

Part of the "Hades" wave of the Shai-Hulud supply-chain campaign. On 2026-06-08, malicious phantom releases of magique were published to PyPI using stolen credentials. The package executes a bundled JavaScript payload (via the Bun runtime) on import that harvests and exfiltrates credentials and attempts self-propagation. This entry is a summary; behavior may not be fully characterized here. See the linked references for detailed analysis and indicators of compromise.

Impacted products
Name purl
magique pkg:pypi/magique
Aliases

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "magique",
        "purl": "pkg:pypi/magique"
      },
      "versions": [
        "0.6.8",
        "0.6.9"
      ]
    }
  ],
  "aliases": [
    "MAL-2026-5296"
  ],
  "details": "Part of the \"Hades\" wave of the Shai-Hulud supply-chain campaign. On 2026-06-08,\nmalicious phantom releases of magique were published to PyPI using stolen\ncredentials. The package executes a bundled JavaScript payload (via the Bun\nruntime) on import that harvests and exfiltrates credentials and attempts\nself-propagation. This entry is a summary; behavior may not be fully\ncharacterized here. See the linked references for detailed analysis and\nindicators of compromise.\n",
  "id": "PYSEC-2026-592",
  "modified": "2026-06-30T20:41:59Z",
  "published": "2026-06-30T20:41:59Z",
  "references": [
    {
      "type": "EVIDENCE",
      "url": "https://inspector.pypi.io/project/magique/0.6.9/packages/fb/cf/376a097f8893ac5c63e3d067b233bf16de9e3c980d8da0ac887a5619b297/magique-0.6.9-py3-none-any.whl//magique-setup.pth"
    },
    {
      "type": "ARTICLE",
      "url": "https://www.endorlabs.com/learn/shai-hulud-hades-wave-hits-six-pypi-bioinformatics-packages"
    },
    {
      "type": "ARTICLE",
      "url": "https://www.stepsecurity.io/blog/the-hades-campaign-pypi-pack"
    }
  ],
  "summary": "Malicious code in magique (PyPI)"
}


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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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