PYSEC-2026-584

Vulnerability from pysec - Published: 2026-06-29 17:05 - Updated: 2026-06-29 17:05
VLAI
Details

Part of the "Hades" wave of the Shai-Hulud supply-chain campaign. On 2026-06-08, malicious phantom releases of rlask 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
rlask pkg:pypi/rlask
Aliases

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "rlask",
        "purl": "pkg:pypi/rlask"
      },
      "versions": [
        "3.1.3",
        "3.1.4",
        "3.1.5",
        "3.1.6",
        "3.1.7"
      ]
    }
  ],
  "aliases": [
    "MAL-2026-5303"
  ],
  "details": "Part of the \"Hades\" wave of the Shai-Hulud supply-chain campaign. On 2026-06-08,\nmalicious phantom releases of rlask 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-2026-584",
  "modified": "2026-06-29T17:05:25Z",
  "published": "2026-06-29T17:05:25Z",
  "references": [
    {
      "type": "EVIDENCE",
      "url": "https://inspector.pypi.io/project/rlask/3.1.7/packages/0e/bd/825d4780b45c1a963f4c8b8687d8ca0abf699d25beac93ca4b86403fbbcf/rlask-3.1.7-py3-none-any.whl//rlask-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-packages"
    }
  ],
  "summary": "Malicious code in rlask (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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