{"answers":{"affected":"3 open source packages ship an affected version, including scikit-learn. It affects 1 product.","exploited":"There is no known exploitation of CVE-2020-13092 in the wild and no public exploit code.","fix":"Upgrade scikit-learn to 0.23.1 or later.","what":"CVE-2020-13092 is a critical unsafe deserialization vulnerability in n/a, published on 15 May 2020. An attacker can exploit it over the internet without logging in, with no action from the victim."},"fix_prompt":"Check this codebase for CVE-2020-13092 and fix it if it is affected.\n\nWhat it is: CVE-2020-13092 is a critical unsafe deserialization vulnerability in n/a, published on 15 May 2020. An attacker can exploit it over the internet without logging in, with no action from the victim.\nExploitation: There is no known exploitation of CVE-2020-13092 in the wild and no public exploit code.\n\nAffected packages and fixed versions:\n- scikit-learn (PyPI): affected all versions up to 0.23.0; no fix listed\n- scikit-learn (PyPI): affected all versions up to 0.23.0; no fix listed\n- scikit-learn (PyPI): affected all versions; fixed in 0.23.1\n\nTask:\n1. Search the dependency manifests and lockfiles (for example package.json, package-lock.json, requirements.txt, poetry.lock, uv.lock, pom.xml, build.gradle, go.mod, Cargo.lock, Gemfile.lock) and any Dockerfiles for the affected packages, including transitive dependencies.\n2. If an affected version is in use, upgrade it to the fixed version for its release line with the project's package manager, so the lockfile is regenerated. Prefer the smallest upgrade that fixes it.\n3. If there is no fix, or the upgrade would break the build, do not guess: explain the risk and the safest workaround instead.\n4. Run the tests and summarise what changed and why.\n5. If nothing in the project is affected, say so and change nothing.\n\nReference: https://insights.sbomify.com/CVE-2020-13092/ (Markdown: https://insights.sbomify.com/CVE-2020-13092/index.md)","id":"CVE-2020-13092","markdown":"https://insights.sbomify.com/CVE-2020-13092/index.md","name":"CVE-2020-13092","record":{"aliases":["DEBIAN-CVE-2020-13092","GHSA-jjw5-xxj6-pcv5","PYSEC-2020-107"],"analysis":{"component_type":"library","fix_type":"no_fix_stated","impacts":["code_execution"],"network_reachable":"yes","non_default_config":false,"product_category":"Python machine learning library","requires_auth":"none","summary":"Attackers can execute arbitrary commands via malicious files loaded by scikit-learn's joblib.load function, which is documented as unsafe.","user_interaction":"not_required","vuln_class":"deserialization"},"analysis_generated_by":"qwen3.8-flash-next","assigner":"mitre","cpe":[{"product":"scikit-learn","vendor":"scikit-learn"}],"cvss":{"score":9.8,"severity":"critical","source":"nvd","vector":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H","version":"3.1"},"cwes":[{"id":"CWE-502","name":"Deserialization of Untrusted Data"}],"description":"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","ecosystems":["PyPI"],"exploits":{"exploitdb":0,"first_seen":null,"github_poc":0,"max_poc_stars":null,"metasploit":0,"nuclei":0,"refs":[],"tier":"none_known"},"has_fix":true,"headline":"scikit-learn Deserialization of Untrusted Data","id":"CVE-2020-13092","indexable":true,"kev":null,"kind":"cve","links":{"ecosystem":"PyPI","product":null,"products":[],"type":"deserialization","year":"2020"},"n_packages":3,"n_references":2,"next":{"headline":"Path traversal in n/a","id":"CVE-2020-13093"},"packages":[{"ecosystem":"PyPI","fixed":[],"introduced":["0"],"last_affected":["0.23.0"],"package":"scikit-learn","purl":null,"source":"GHSA-jjw5-xxj6-pcv5"},{"ecosystem":"PyPI","fixed":[],"introduced":["0"],"last_affected":["0.23.0"],"package":"scikit-learn","purl":"pkg:pypi/scikit-learn","source":"GHSA-jjw5-xxj6-pcv5"},{"ecosystem":"PyPI","fixed":["0.23.1"],"introduced":["0"],"last_affected":[],"package":"scikit-learn","purl":"pkg:pypi/scikit-learn","source":"PYSEC-2020-107"}],"prev":{"headline":"Deserialization in n/a","id":"CVE-2020-13091"},"products":["n/a"],"published":"2020-05-15","related":[{"cvss_score":7.5,"headline":"scikit-learn Denial of Service","id":"CVE-2020-28975","kev_date_added":null,"published":"2020-11-21"},{"cvss_score":4.7,"headline":"Sensitive Data Leakage in sklearn.feature_extraction.text.TfidfVectorizer in scikit-learn/scikit-learn","id":"CVE-2024-5206","kev_date_added":null,"published":"2024-06-06"}],"sources":[{"class":"attribution","licence":"CVE Terms of Use (MITRE)","name":"CVE List","prefix":"CVE"},{"class":"attribution","licence":"CC BY 4.0","name":"GitHub Advisory Database","prefix":"GHSA"},{"class":"attribution","licence":"CC BY 4.0","name":"PyPA Advisory Database","prefix":"PYSEC"},{"class":"attribution","licence":"US public information, NVD API terms","name":"NVD"},{"class":"attribution","licence":"CWE Terms of Use","name":"CWE (MITRE)"},{"class":"attribution","licence":"FIRST CVSS licence","name":"CVSS (FIRST)"}],"ssvc":{"automatable":null,"exploitation":null,"technical_impact":null},"state":"PUBLISHED","updated":"2024-08-04","vendors":["n/a"]},"summary":"Attackers can execute arbitrary commands via malicious files loaded by scikit-learn's joblib.load function, which is documented as unsafe.","url":"https://insights.sbomify.com/CVE-2020-13092/"}