GHSA-JJW5-XXJ6-PCV5

Vulnerability from github – Published: 2022-05-24 17:17 – Updated: 2024-10-22 17:21
VLAI
Summary
scikit-learn Deserialization of Untrusted Data
Details

scikit-learn (aka sklearn) through 0.23.0 can unserialize and execute commands from an untrusted file that is passed to the joblib.load() function, if __reduce__ makes an os.system call. NOTE: third parties dispute this issue because the joblib.load() function is documented as unsafe and it is the user's responsibility to use the function in a secure manner.

Severity
9.8 (Critical)
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
Show details on source website
To clipboard 

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "scikit-learn"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "last_affected": "0.23.0"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2020-13092"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-502"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2024-04-22T22:55:41Z",
    "nvd_published_at": "2020-05-15T19:15:00Z",
    "severity": "CRITICAL"
  },
  "details": "scikit-learn (aka sklearn) through 0.23.0 can unserialize and execute commands from an untrusted file that is passed to the `joblib.load()` function, if `__reduce__` makes an `os.system call`.\nNOTE: third parties dispute this issue because the joblib.load() function is documented as unsafe and it is the user\u0027s responsibility to use the function in a secure manner.",
  "id": "GHSA-jjw5-xxj6-pcv5",
  "modified": "2024-10-22T17:21:50Z",
  "published": "2022-05-24T17:17:59Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2020-13092"
    },
    {
      "type": "WEB",
      "url": "https://github.com/0FuzzingQ/vuln/blob/master/sklearn%20unserialize.md"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/scikit-learn/PYSEC-2020-107.yaml"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/scikit-learn/scikit-learn"
    },
    {
      "type": "WEB",
      "url": "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations"
    }
  ],
  "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": "scikit-learn Deserialization of Untrusted Data"
}



Log in or create an account to share your comment.




Tags
Taxonomy of the tags.





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

Author Source Type Date Other

Nomenclature

  • Seen: The vulnerability was mentioned, discussed, or observed by the user.
  • Confirmed: The vulnerability has been validated from an analyst's perspective.
  • Published Proof of Concept: A public proof of concept is available for this vulnerability.
  • Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
  • Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
  • Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
  • Not confirmed: The user expressed doubt about the validity of the vulnerability.
  • Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.



Related by attack behaviour

Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.