A sensitive data leakage vulnerability was identified in scikit-learns 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.
The product stores sensitive information in a file system or device that does not have built-in access control.
| Name | Vendor | Start Version | End Version |
|---|---|---|---|
| Scikit-learn | Ubuntu | mantic | * |
While many modern file systems or devices utilize some form of access control in order to restrict access to data, not all storage mechanisms have this capability. For example, memory cards, floppy disks, CDs, and USB devices are typically made accessible to any user within the system. This can become a problem when sensitive data is stored in these mechanisms in a multi-user environment, because anybody on the system can read or write this data. On Android devices, external storage is typically globally readable and writable by other applications on the device. External storage may also be easily accessible through the mobile device’s USB connection or physically accessible through the device’s memory card port.