GHSA-WCR3-GM9F-F87Q

Vulnerability from github – Published: 2026-05-12 18:30 – Updated: 2026-05-27 22:19
VLAI
Summary
Ludwig framework is vulnerable to insecure deserialization through its predict() method.
Details

The Ludwig framework thru 0.10.4 is vulnerable to insecure deserialization (CWE-502) through its predict() method. When a user provides a dataset file path to the predict() method, the framework automatically determines the file format. If the file is a pickle (.pkl) file, it is loaded using pandas.read_pickle() without any validation or security restrictions. This allows the deserialization of arbitrary Python objects via the unsafe pickle module. A remote attacker can exploit this by providing a maliciously crafted pickle file, leading to arbitrary code execution on the system running the Ludwig prediction.

Severity
9.8 (Critical)
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
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{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "ludwig"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "last_affected": "0.10.4"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2026-31237"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-502"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2026-05-27T22:19:35Z",
    "nvd_published_at": "2026-05-12T18:16:52Z",
    "severity": "CRITICAL"
  },
  "details": "The Ludwig framework thru 0.10.4 is vulnerable to insecure deserialization (CWE-502) through its predict() method. When a user provides a dataset file path to the predict() method, the framework automatically determines the file format. If the file is a pickle (.pkl) file, it is loaded using pandas.read_pickle() without any validation or security restrictions. This allows the deserialization of arbitrary Python objects via the unsafe pickle module. A remote attacker can exploit this by providing a maliciously crafted pickle file, leading to arbitrary code execution on the system running the Ludwig prediction.",
  "id": "GHSA-wcr3-gm9f-f87q",
  "modified": "2026-05-27T22:19:35Z",
  "published": "2026-05-12T18:30:41Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-31237"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/ludwig-ai/ludwig"
    },
    {
      "type": "WEB",
      "url": "https://www.notion.so/CVE-2026-31237-35d1e139318881fb95a2ee7c5d0e17d8"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
      "type": "CVSS_V3"
    }
  ],
  "summary": "Ludwig framework is vulnerable to insecure deserialization through its predict() method."
}



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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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Detection rules are retrieved from Rulezet.