Количество 6
Количество 6
CVE-2024-5206
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.
CVE-2024-5206
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.
CVE-2024-5206
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.
CVE-2024-5206
A sensitive data leakage vulnerability was identified in scikit-learn' ...
SUSE-SU-2024:2029-1
Security update for python-scikit-learn
GHSA-jw8x-6495-233v
scikit-learn sensitive data leakage vulnerability
Уязвимостей на страницу
Уязвимость | CVSS | EPSS | Опубликовано | |
|---|---|---|---|---|
CVE-2024-5206 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. | CVSS3: 4.7 | 0% Низкий | около 2 лет назад | |
CVE-2024-5206 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. | CVSS3: 5.3 | 0% Низкий | около 2 лет назад | |
CVE-2024-5206 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. | CVSS3: 4.7 | 0% Низкий | около 2 лет назад | |
CVE-2024-5206 A sensitive data leakage vulnerability was identified in scikit-learn' ... | CVSS3: 4.7 | 0% Низкий | около 2 лет назад | |
SUSE-SU-2024:2029-1 Security update for python-scikit-learn | 0% Низкий | около 2 лет назад | ||
GHSA-jw8x-6495-233v scikit-learn sensitive data leakage vulnerability | CVSS3: 5.3 | 0% Низкий | около 2 лет назад |
Уязвимостей на страницу