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.
| Métrique | Valeur | Interprétation |
|---|---|---|
|
AV (Vecteur d'Attaque)
Attack Vector
|
Local (L)
|
Exploit nécessite un accès local au système |
|
AC (Complexité d'Attaque)
Attack Complexity
|
High (H)
|
Conditions spécifiques doivent être réunies pour exploiter |
|
PR (Privilèges Requis)
Privileges Required
|
Low (L)
|
Authentifié avec compte utilisateur basique |
|
UI (Interaction Utilisateur)
User Interaction
|
None (N)
|
Aucune interaction utilisateur nécessaire |
|
S (Périmètre)
Scope
|
Unchanged (U)
|
Impact reste dans le périmètre du composant compromis |
|
C (Impact Confidentialité)
Confidentiality Impact
|
High (H)
|
Divulgation totale d'informations |
|
I (Impact Intégrité)
Integrity Impact
|
None (N)
|
Aucune modification de données possible |
|
A (Impact Disponibilité)
Availability Impact
|
None (N)
|
Aucune interruption de service |
Apache NiFi: Potential Insertion of Sensitive Parameter Values in Debug Log
Sensitive Data Leakage in sklearn.feature_extraction.text.TfidfVectorizer in scikit-learn/scikit-learn