{"id":"CVE-2024-5206","aliases":["CLSA-2026-1787201491","DEBIAN-CVE-2024-5206","EUVD-2024-0161","GHSA-jw8x-6495-233v","MGASA-2024-0228","OESA-2024-1745","PYSEC-2024-110","SUSE-SU-2024:2029-1","UBUNTU-CVE-2024-5206","openSUSE-SU-2024:14043-1","openSUSE-SU-2026:11498-1"],"status":"published","title":"Sensitive Data Leakage in sklearn.feature_extraction.text.TfidfVectorizer in scikit-learn/scikit-learn","description":"A sensitive data leakage vulnerability was identified in scikit-learn's TfidfVectorizer, specifically in versions up to and including 1.4.1.post1, which was fixed in version 1.5.0. The vulnerability arises from the unexpected storage of all tokens present in the training data within the `stop_words_` attribute, rather than only storing the subset of tokens required for the TF-IDF technique to function. 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Data Leakage in sklearn.feature_extraction.text.TfidfVectorizer in scikit-learn/scikit-learn","description":"A sensitive data leakage vulnerability was identified in scikit-learn's TfidfVectorizer, specifically in versions up to and including 1.4.1.post1, which was fixed in version 1.5.0. The vulnerability arises from the unexpected storage of all tokens present in the training data within the `stop_words_` attribute, rather than only storing the subset of tokens required for the TF-IDF technique to function. This behavior leads to the potential leakage of sensitive information, as the `stop_words_` attribute could contain tokens that were meant to be discarded and not stored, such as passwords or keys. 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This behavior leads to the potential leakage of sensitive information, as the `stop_words_` attribute could contain tokens that were meant to be discarded and not stored, such as passwords or keys. The impact of this vulnerability varies based on the nature of the data being processed by the vectorizer.","status":"published","published":"2024-06-06T18:28:14Z","modified":"2024-08-01T21:03:11Z","cvss":[{"version":"3.0","score":4.7,"vector":"CVSS:3.0/AV:L/AC:H/PR:L/UI:N/S:U/C:H/I:N/A:N","level":"medium"}],"updated_at":"2026-11-03T03:50:35.230694Z"}],"ghsa":[{"id":"GHSA-jw8x-6495-233v","title":"scikit-learn sensitive data leakage vulnerability","description":"A sensitive data leakage vulnerability was identified in scikit-learn's TfidfVectorizer, specifically in versions up to and including 1.4.1.post1, which was fixed in version 1.5.0. The vulnerability arises from the unexpected storage of all tokens present in the training data within the `stop_words_` attribute, rather than only storing the subset of tokens required for the TF-IDF technique to function. This behavior leads to the potential leakage of sensitive information, as the `stop_words_` attribute could contain tokens that were meant to be discarded and not stored, such as passwords or keys. The impact of this vulnerability varies based on the nature of the data being processed by the vectorizer.","status":"published","published":"2024-06-06T21:30:37Z","modified":"2024-10-25T16:47:33Z","cvss":[{"version":"3.0","score":5.3,"vector":"CVSS:3.0/AV:N/AC:H/PR:L/UI:N/S:U/C:H/I:N/A:N","level":"medium"}],"cwes":["CWE-921","CWE-922"],"updated_at":"2026-11-02T14:38:13.09489Z"}],"nvd":[{"id":"CVE-2024-5206","description":"A sensitive data leakage vulnerability was identified in scikit-learn's TfidfVectorizer, specifically in versions up to and including 1.4.1.post1, which was fixed in version 1.5.0. The vulnerability arises from the unexpected storage of all tokens present in the training data within the `stop_words_` attribute, rather than only storing the subset of tokens required for the TF-IDF technique to function. This behavior leads to the potential leakage of sensitive information, as the `stop_words_` attribute could contain tokens that were meant to be discarded and not stored, such as passwords or keys. The impact of this vulnerability varies based on the nature of the data being processed by the vectorizer.","status":"published","published":"2024-06-06T19:16:06.363Z","modified":"2026-06-17T08:15:24.69Z","cvss":[{"version":"3.1","score":4.7,"vector":"CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:H/I:N/A:N","level":"medium","assessor":"primary"},{"version":"3.0","score":4.7,"vector":"CVSS:3.0/AV:L/AC:H/PR:L/UI:N/S:U/C:H/I:N/A:N","level":"medium","assessor":"secondary"}],"cwes":["CWE-921","CWE-922"],"updated_at":"2026-11-02T14:10:04.111836Z"}],"osv":[{"id":"CVE-2024-5206","title":"Sensitive Data Leakage in sklearn.feature_extraction.text.TfidfVectorizer in scikit-learn/scikit-learn","description":"A sensitive data leakage vulnerability was identified in scikit-learn's TfidfVectorizer, specifically in versions up to and including 1.4.1.post1, which was fixed in version 1.5.0. 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The vulnerability arises from the unexpected storage of all tokens present in the training data within the `stop_words_` attribute, rather than only storing the subset of tokens required for the TF-IDF technique to function. This behavior leads to the potential leakage of sensitive information, as the `stop_words_` attribute could contain tokens that were meant to be discarded and not stored, such as passwords or keys. The impact of this vulnerability varies based on the nature of the data being processed by the vectorizer.","status":"published","published":"2024-06-06T21:30:37Z","modified":"2026-09-10T03:50:15.628224747Z","cvss":[{"version":"3.0","score":5.3,"vector":"CVSS:3.0/AV:N/AC:H/PR:L/UI:N/S:U/C:H/I:N/A:N","level":"medium"}],"cwes":["CWE-921","CWE-922"],"updated_at":"2026-11-02T15:46:51.10002Z"},{"id":"OESA-2024-1745","title":"python-scikit-learn security update","description":"A Python module for machine learning built on top of SciPy\r\n\r\nSecurity Fix(es):\r\n\r\nA sensitive data leakage vulnerability was identified in scikit-learn\u0026apos;s TfidfVectorizer, specifically in versions up to and including 1.4.1.post1, which was fixed in version 1.5.0. The vulnerability arises from the unexpected storage of all tokens present in the training data within the `stop_words_` attribute, rather than only storing the subset of tokens required for the TF-IDF technique to function. This behavior leads to the potential leakage of sensitive information, as the `stop_words_` attribute could contain tokens that were meant to be discarded and not stored, such as passwords or keys. The impact of this vulnerability varies based on the nature of the data being processed by the vectorizer.(CVE-2024-5206)","status":"published","published":"2024-06-21T11:07:12Z","modified":"2026-08-18T01:17:25.196927736Z","cvss":[{"version":"3.1","score":5.3,"vector":"CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:U/C:H/I:N/A:N","level":"medium"}],"updated_at":"2026-11-02T15:52:41.095407Z"},{"id":"PYSEC-2024-110","description":"A sensitive data leakage vulnerability was identified in scikit-learn's TfidfVectorizer, specifically in versions up to and including 1.4.1.post1, which was fixed in version 1.5.0. The vulnerability arises from the unexpected storage of all tokens present in the training data within the `stop_words_` attribute, rather than only storing the subset of tokens required for the TF-IDF technique to function. This behavior leads to the potential leakage of sensitive information, as the `stop_words_` attribute could contain tokens that were meant to be discarded and not stored, such as passwords or keys. The impact of this vulnerability varies based on the nature of the data being processed by the vectorizer.","status":"published","published":"2024-06-06T19:16:00Z","modified":"2026-06-10T17:02:43.910139851Z","cvss":[{"version":"3.1","score":4.7,"vector":"CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:H/I:N/A:N","level":"medium"}],"updated_at":"2026-11-02T15:52:53.837392Z"},{"id":"UBUNTU-CVE-2024-5206","description":"A sensitive data leakage vulnerability was identified in scikit-learn's TfidfVectorizer, specifically in versions up to and including 1.4.1.post1, which was fixed in version 1.5.0. The vulnerability arises from the unexpected storage of all tokens present in the training data within the `stop_words_` attribute, rather than only storing the subset of tokens required for the TF-IDF technique to function. This behavior leads to the potential leakage of sensitive information, as the `stop_words_` attribute could contain tokens that were meant to be discarded and not stored, such as passwords or keys. The impact of this vulnerability varies based on the nature of the data being processed by the vectorizer.","status":"published","published":"2024-06-06T19:16:00Z","modified":"2026-11-09T12:49:26.493740128Z","cvss":[{"version":"3.0","score":4.7,"vector":"CVSS:3.0/AV:L/AC:H/PR:L/UI:N/S:U/C:H/I:N/A:N","level":"medium"},{"version":"3.1","score":4.7,"vector":"CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:H/I:N/A:N","level":"medium"}],"updated_at":"2026-11-09T13:27:42.046613Z"},{"id":"CLSA-2026-1787201491","title":"TuxCare security update for scikit-learn (1 CVE)","description":"TuxCare rebuilt scikit-learn to address 1 CVE. Fixed in 1.0.2.post2+tuxcare.","status":"published","published":"2026-09-08T17:08:23Z","modified":"2026-10-15T09:48:05.579893671Z","updated_at":"2026-11-02T15:43:47.20725Z"},{"id":"MGASA-2024-0228","title":"Updated python-scikit-learn packages fix security vulnerability","description":"A sensitive data leakage vulnerability was identified in scikit-learn's\nTfidfVectorizer, specifically in versions up to and including\n1.4.1.post1, which was fixed in version 1.5.0. The vulnerability arises\nfrom the unexpected storage of all tokens present in the training data\nwithin the `stop_words_` attribute, rather than only storing the subset\nof tokens required for the TF-IDF technique to function. This behavior\nleads to the potential leakage of sensitive information, as the\n`stop_words_` attribute could contain tokens that were meant to be\ndiscarded and not stored, such as passwords or keys. The impact of this\nvulnerability varies based on the nature of the data being processed by\nthe vectorizer.\n","status":"published","published":"2024-06-20T02:32:09Z","modified":"2026-04-16T04:43:40.879965039Z","updated_at":"2026-11-02T15:49:42.684501Z"},{"id":"openSUSE-SU-2024:14043-1","title":"python310-scikit-learn-1.5.0-1.1 on GA media","description":"These are all security issues fixed in the python310-scikit-learn-1.5.0-1.1 package on the GA media of openSUSE Tumbleweed.","status":"published","published":"2024-06-15T00:00:00Z","modified":"2026-02-04T03:11:08.377519Z","updated_at":"2026-11-02T16:58:36.662953Z"},{"id":"openSUSE-SU-2026:11498-1","title":"python313-scikit-learn-1.9.0-1.1 on GA media","description":"These are all security issues fixed in the python313-scikit-learn-1.9.0-1.1 package on the GA media of openSUSE Tumbleweed.","status":"published","published":"2026-08-11T00:00:00Z","modified":"2026-08-12T10:45:07.668905558Z","updated_at":"2026-11-02T16:58:39.516824Z"},{"id":"SUSE-SU-2024:2029-1","title":"Security update for python-scikit-learn","description":"This update for python-scikit-learn fixes the following issues:\n\n-CVE-2024-5206: Fixed a possible sensitive data leak in TfidfVectorizer. (bsc#1226185)\n","status":"published","published":"2024-06-13T15:13:20Z","modified":"2026-02-04T03:32:49.184434Z","updated_at":"2026-11-02T15:53:54.652421Z"}]}}