Oracle Financial Services Applications / Reports (scikit-learn) CVEs

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Overview 1 match · 0 in Waratek scope · 0 protected · 0 exploited · 0 CISA KEV · 0 public PoC · 0 EPSS ≥ 0.5

1match 0in Waratek scope0.0% 0protected0.0% 0exploited0.0% 0CISA KEV0.0% 0public PoC0.0% 0EPSS ≥ 0.50.0%

Protected by Waratek 0 0.0% Rule in development 0 0.0% Mitigation candidate 0 0.0% No exploit published 0 0.0% Outside Waratek scope 0 0.0% Not applicable 1 100.0% Withdrawn CVE 0 0.0%

Critical 0 0.0% High 0 0.0% Medium 1 100.0% Low 0 0.0% None 0 0.0% Unknown 0 0.0%

blocked by ARMR today 0 0.0% partly blocked 0 0.0% not blocked 1 100.0% not established 0 0.0% unrecorded 0 0.0%

Exploited 0 0.0% Working exploit published 0 0.0% Proof of concept only 1 100.0% Forecast only 0 0.0% No public exploit 0 0.0%

split by StatusSeverityProtectionExploit maturity peak 1 / month

Jun 241 Not applicable1 Medium1 not blocked1 Proof of concept only1

Jun 24

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CVE	Severity	Published▾	Status	Exploitation	Description
CVE-2024-5206	Medium	2024-06-06	Not applicable	Proof of concept only	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

Severity

Published▾

Status

Exploitation

Description

CVE-2024-5206

Medium

2024-06-06

Not applicable

Proof of concept only

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.