Vulnerability-Lookup

CVE-2020-13092 (GCVE-0-2020-13092)

Vulnerability from cvelistv5 – Published: 2020-05-15 18:41 – Updated: 2024-08-04 12:11 Disputed

Summary

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

CWE

Assigner

mitre mitre root

References

2 references

URL	Tags
https://github.com/0FuzzingQ/vuln/blob/master/skl…	x_refsource_MISC
https://scikit-learn.org/stable/modules/model_per…	x_refsource_MISC

Show details on NVD website

JSON

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CNVD-2020-33501

Vulnerability from cnvd - Published: 2020-06-18

Title

scikit-learn代码问题漏洞

Description

scikit-learn是一款基于Python的开源机器学习程序包，它支持垃圾邮件检测、图像识别和关联的连续值属性预测等功能。 scikit-learn (sklearn) 0.23.0及之前版本中存在代码问题漏洞，攻击者可利用该漏洞执行命令。

Severity

高

Formal description

厂商尚未提供漏洞修复方案，请关注厂商主页更新： https://scikit-learn.org/

Reference

https://nvd.nist.gov/vuln/detail/CVE-2020-13092

Impacted products

Name
Scikit-Learn Scikit-Learn <=0.23.0

Show details on source website

JSON

To clipboard

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FKIE_CVE-2020-13092

Vulnerability from fkie_nvd - Published: 2020-05-15 19:15 - Updated: 2026-06-17 02:52

Severity

9.8 (Critical) - CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

Summary

References

URL	Tags
cve@mitre.org	https://github.com/0FuzzingQ/vuln/blob/master/sklearn%20unserialize.md	Exploit, Third Party Advisory
cve@mitre.org	https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations	Third Party Advisory
af854a3a-2127-422b-91ae-364da2661108	https://github.com/0FuzzingQ/vuln/blob/master/sklearn%20unserialize.md	Exploit, Third Party Advisory
af854a3a-2127-422b-91ae-364da2661108	https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations	Third Party Advisory

Impacted products

	Vendor	Product	Version
	scikit-learn	scikit-learn	*

JSON

To clipboard

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          "version": "3.1"
        },
        "exploitabilityScore": 3.9,
        "impactScore": 5.9,
        "source": "nvd@nist.gov",
        "type": "Primary"
      }
    ]
  },
  "published": "2020-05-15T19:15:12.277",
  "references": [
    {
      "source": "cve@mitre.org",
      "tags": [
        "Exploit",
        "Third Party Advisory"
      ],
      "url": "https://github.com/0FuzzingQ/vuln/blob/master/sklearn%20unserialize.md"
    },
    {
      "source": "cve@mitre.org",
      "tags": [
        "Third Party Advisory"
      ],
      "url": "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations"
    },
    {
      "source": "af854a3a-2127-422b-91ae-364da2661108",
      "tags": [
        "Exploit",
        "Third Party Advisory"
      ],
      "url": "https://github.com/0FuzzingQ/vuln/blob/master/sklearn%20unserialize.md"
    },
    {
      "source": "af854a3a-2127-422b-91ae-364da2661108",
      "tags": [
        "Third Party Advisory"
      ],
      "url": "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations"
    }
  ],
  "sourceIdentifier": "cve@mitre.org",
  "vulnStatus": "Modified",
  "weaknesses": [
    {
      "description": [
        {
          "lang": "en",
          "value": "CWE-502"
        }
      ],
      "source": "nvd@nist.gov",
      "type": "Primary"
    }
  ]
}

GHSA-JJW5-XXJ6-PCV5

Vulnerability from github – Published: 2022-05-24 17:17 – Updated: 2024-10-22 17:21

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

JSON

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"
}

GSD-2020-13092

Vulnerability from gsd - Updated: 2023-12-13 01:21

Details

** DISPUTED ** 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.

Aliases

CVE-2020-13092

Aliases

CVE-2020-13092

JSON

To clipboard

{
  "GSD": {
    "alias": "CVE-2020-13092",
    "description": "** DISPUTED ** 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\u0027s responsibility to use the function in a secure manner.",
    "id": "GSD-2020-13092",
    "references": [
      "https://www.suse.com/security/cve/CVE-2020-13092.html"
    ]
  },
  "gsd": {
    "metadata": {
      "exploitCode": "unknown",
      "remediation": "unknown",
      "reportConfidence": "confirmed",
      "type": "vulnerability"
    },
    "osvSchema": {
      "aliases": [
        "CVE-2020-13092"
      ],
      "details": "** DISPUTED ** 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\u0027s responsibility to use the function in a secure manner.",
      "id": "GSD-2020-13092",
      "modified": "2023-12-13T01:21:47.010142Z",
      "schema_version": "1.4.0"
    }
  },
  "namespaces": {
    "cve.org": {
      "CVE_data_meta": {
        "ASSIGNER": "cve@mitre.org",
        "ID": "CVE-2020-13092",
        "STATE": "PUBLIC"
      },
      "affects": {
        "vendor": {
          "vendor_data": [
            {
              "product": {
                "product_data": [
                  {
                    "product_name": "n/a",
                    "version": {
                      "version_data": [
                        {
                          "version_value": "n/a"
                        }
                      ]
                    }
                  }
                ]
              },
              "vendor_name": "n/a"
            }
          ]
        }
      },
      "data_format": "MITRE",
      "data_type": "CVE",
      "data_version": "4.0",
      "description": {
        "description_data": [
          {
            "lang": "eng",
            "value": "** DISPUTED ** 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\u0027s responsibility to use the function in a secure manner."
          }
        ]
      },
      "problemtype": {
        "problemtype_data": [
          {
            "description": [
              {
                "lang": "eng",
                "value": "n/a"
              }
            ]
          }
        ]
      },
      "references": {
        "reference_data": [
          {
            "name": "https://github.com/0FuzzingQ/vuln/blob/master/sklearn%20unserialize.md",
            "refsource": "MISC",
            "url": "https://github.com/0FuzzingQ/vuln/blob/master/sklearn%20unserialize.md"
          },
          {
            "name": "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations",
            "refsource": "MISC",
            "url": "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations"
          }
        ]
      }
    },
    "gitlab.com": {
      "advisories": [
        {
          "affected_range": "\u003c=0.23.0",
          "affected_versions": "All versions up to 0.23.0",
          "cvss_v2": "AV:N/AC:L/Au:N/C:P/I:P/A:P",
          "cvss_v3": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
          "cwe_ids": [
            "CWE-1035",
            "CWE-502",
            "CWE-937"
          ],
          "date": "2020-05-19",
          "description": "scikit-learn (aka sklearn) can unserialize and execute commands from an untrusted file that is passed to the `joblib.load()` function, if `__reduce__` makes an `os.system call`.",
          "fixed_versions": [
            "0.23.1"
          ],
          "identifier": "CVE-2020-13092",
          "identifiers": [
            "CVE-2020-13092"
          ],
          "not_impacted": "All versions after 0.23.0",
          "package_slug": "pypi/scikit-learn",
          "pubdate": "2020-05-15",
          "solution": "Upgrade to version 0.23.1 or above.",
          "title": "Deserialization of Untrusted Data",
          "urls": [
            "https://nvd.nist.gov/vuln/detail/CVE-2020-13092"
          ],
          "uuid": "b718f7f6-9655-4848-a0f9-45636e675d99"
        }
      ]
    },
    "nvd.nist.gov": {
      "cve": {
        "configurations": [
          {
            "nodes": [
              {
                "cpeMatch": [
                  {
                    "criteria": "cpe:2.3:a:scikit-learn:scikit-learn:*:*:*:*:*:*:*:*",
                    "matchCriteriaId": "BFA8D419-E6D7-4DBF-9554-957975CD6101",
                    "versionEndIncluding": "0.23.0",
                    "vulnerable": true
                  }
                ],
                "negate": false,
                "operator": "OR"
              }
            ]
          }
        ],
        "descriptions": [
          {
            "lang": "en",
            "value": "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\u0027s responsibility to use the function in a secure manner"
          },
          {
            "lang": "es",
            "value": "** EN DISPUTA ** scikit-learn (tambi\u00e9n se conoce como sklearn) versiones hasta 0.23.0, puede deserializar y ejecutar comandos desde un archivo no confiable que es pasado hacia la funci\u00f3n joblib.load(), si __reduce__ realiza una llamada os.system. NOTA: terceros disputan este problema porque la funci\u00f3n joblib.load () est\u00e1 documentada como insegura y es responsabilidad del usuario usar la funci\u00f3n de manera segura."
          }
        ],
        "id": "CVE-2020-13092",
        "lastModified": "2024-04-11T01:06:54.260",
        "metrics": {
          "cvssMetricV2": [
            {
              "acInsufInfo": false,
              "baseSeverity": "HIGH",
              "cvssData": {
                "accessComplexity": "LOW",
                "accessVector": "NETWORK",
                "authentication": "NONE",
                "availabilityImpact": "PARTIAL",
                "baseScore": 7.5,
                "confidentialityImpact": "PARTIAL",
                "integrityImpact": "PARTIAL",
                "vectorString": "AV:N/AC:L/Au:N/C:P/I:P/A:P",
                "version": "2.0"
              },
              "exploitabilityScore": 10.0,
              "impactScore": 6.4,
              "obtainAllPrivilege": false,
              "obtainOtherPrivilege": false,
              "obtainUserPrivilege": false,
              "source": "nvd@nist.gov",
              "type": "Primary",
              "userInteractionRequired": false
            }
          ],
          "cvssMetricV31": [
            {
              "cvssData": {
                "attackComplexity": "LOW",
                "attackVector": "NETWORK",
                "availabilityImpact": "HIGH",
                "baseScore": 9.8,
                "baseSeverity": "CRITICAL",
                "confidentialityImpact": "HIGH",
                "integrityImpact": "HIGH",
                "privilegesRequired": "NONE",
                "scope": "UNCHANGED",
                "userInteraction": "NONE",
                "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
                "version": "3.1"
              },
              "exploitabilityScore": 3.9,
              "impactScore": 5.9,
              "source": "nvd@nist.gov",
              "type": "Primary"
            }
          ]
        },
        "published": "2020-05-15T19:15:12.277",
        "references": [
          {
            "source": "cve@mitre.org",
            "tags": [
              "Exploit",
              "Third Party Advisory"
            ],
            "url": "https://github.com/0FuzzingQ/vuln/blob/master/sklearn%20unserialize.md"
          },
          {
            "source": "cve@mitre.org",
            "tags": [
              "Third Party Advisory"
            ],
            "url": "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations"
          }
        ],
        "sourceIdentifier": "cve@mitre.org",
        "vulnStatus": "Modified",
        "weaknesses": [
          {
            "description": [
              {
                "lang": "en",
                "value": "CWE-502"
              }
            ],
            "source": "nvd@nist.gov",
            "type": "Primary"
          }
        ]
      }
    }
  }
}

PYSEC-2020-107

Vulnerability from pysec - Published: 2020-05-15 19:15 - Updated: 2020-05-19 19:05

Details

** DISPUTED ** 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.

Impacted products

Name	purl
scikit-learn	pkg:pypi/scikit-learn

Aliases

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "scikit-learn",
        "purl": "pkg:pypi/scikit-learn"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "0.23.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "0.9",
        "0.10",
        "0.11",
        "0.12",
        "0.12.1",
        "0.13",
        "0.13.1",
        "0.14a1",
        "0.14",
        "0.14.1",
        "0.15.0b1",
        "0.15.0b2",
        "0.15.0",
        "0.15.1",
        "0.15.2",
        "0.16b1",
        "0.16.0",
        "0.16.1",
        "0.17b1",
        "0.17",
        "0.17.1",
        "0.18rc2",
        "0.18",
        "0.18.1",
        "0.18.2",
        "0.19b2",
        "0.19.0",
        "0.19.1",
        "0.19.2",
        "0.20rc1",
        "0.20.0",
        "0.20.1",
        "0.20.2",
        "0.20.3",
        "0.20.4",
        "0.21rc2",
        "0.21.0",
        "0.21.1",
        "0.21.2",
        "0.21.3",
        "0.22rc2.post1",
        "0.22rc3",
        "0.22",
        "0.22.1",
        "0.22.2",
        "0.22.2.post1",
        "0.23.0rc1",
        "0.23.0"
      ]
    }
  ],
  "aliases": [
    "CVE-2020-13092",
    "GHSA-jjw5-xxj6-pcv5"
  ],
  "details": "** DISPUTED ** 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\u0027s responsibility to use the function in a secure manner.",
  "id": "PYSEC-2020-107",
  "modified": "2020-05-19T19:05:00Z",
  "published": "2020-05-15T19:15:00Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/0FuzzingQ/vuln/blob/master/sklearn%20unserialize.md"
    },
    {
      "type": "WEB",
      "url": "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations"
    },
    {
      "type": "ADVISORY",
      "url": "https://github.com/advisories/GHSA-jjw5-xxj6-pcv5"
    }
  ]
}

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.

Detection rules are retrieved from Rulezet.

The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base model, served locally by ML-Gateway. They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.

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.

Action not permitted

CVE-2020-13092 (GCVE-0-2020-13092)

CNVD-2020-33501

FKIE_CVE-2020-13092

GHSA-JJW5-XXJ6-PCV5

GSD-2020-13092

PYSEC-2020-107

Tags

Sightings

Nomenclature

Related by attack behaviour