Vulnerability-Lookup

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"
    }
  },
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        "ID": "CVE-2020-13092",
        "STATE": "PUBLIC"
      },
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            "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."
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            "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"
          }
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    "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"
        }
      ]
    },
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                "cpeMatch": [
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                    "criteria": "cpe:2.3:a:scikit-learn:scikit-learn:*:*:*:*:*:*:*:*",
                    "matchCriteriaId": "BFA8D419-E6D7-4DBF-9554-957975CD6101",
                    "versionEndIncluding": "0.23.0",
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                  }
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        ],
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          {
            "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": [
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            "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"
          }
        ]
      }
    }
  }
}

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

No CVSS data available.

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

To clipboard

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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

GSD-2020-13092

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

Tags

Sightings

Nomenclature

Related by attack behaviour