CVE-2024-5206: Scikit-learn Information Disclosure Flaw

CVE-2024-5206 Overview

A sensitive data leakage vulnerability was identified in scikit-learn's TfidfVectorizer component. The vulnerability exists in versions up to and including 1.4.1.post1 and was fixed in version 1.5.0. The flaw 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 properly.

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, API keys, or other secrets that may have been present in the training data. The impact of this vulnerability varies based on the nature of the data being processed by the vectorizer.

Critical Impact

Sensitive information including passwords and API keys may be inadvertently stored and exposed through the stop_words_ attribute when using TfidfVectorizer on sensitive training data.

Affected Products

• scikit-learn versions up to and including 1.4.1.post1
• Python applications utilizing TfidfVectorizer for text processing
• Machine learning pipelines that persist trained TfidfVectorizer models

Discovery Timeline

• 2024-06-06 - CVE-2024-5206 published to NVD
• 2024-11-21 - Last updated in NVD database

Technical Details for CVE-2024-5206

Vulnerability Analysis

The vulnerability stems from an implementation oversight in the TfidfVectorizer class within scikit-learn. When the vectorizer processes training data, it builds a vocabulary of terms and calculates TF-IDF (Term Frequency-Inverse Document Frequency) weights. As part of this process, the vectorizer identifies and stores stop words—common words that are typically filtered out during text processing.

The root issue is that the stop_words_ attribute inadvertently captures all tokens that appear in the training corpus, including sensitive data that should never be persisted. This occurs because the implementation stores the complete set of filtered tokens rather than just the predefined stop words list.

The vulnerability requires local access and involves high attack complexity, as an attacker would need access to the trained model object or its serialized form. However, successful exploitation results in high confidentiality impact due to the potential exposure of sensitive credentials or secrets embedded in training data.

Root Cause

The root cause is improper storage of unsanitized token data within the stop_words_ attribute of the TfidfVectorizer class. The implementation failed to distinguish between intentionally configured stop words and tokens that were filtered based on document frequency thresholds, causing all filtered tokens—including potentially sensitive ones—to be stored.

This represents an information storage vulnerability (CWE-921, CWE-922) where sensitive data is inappropriately persisted in an accessible object attribute.

Attack Vector

The attack vector for this vulnerability is local, requiring an attacker to have access to either:

1. A trained TfidfVectorizer model object in memory
2. A serialized (pickled) model file containing the trained vectorizer
3. Application logs or debugging output that may expose the stop_words_ attribute

An attacker with access to these resources could extract sensitive tokens by simply accessing the stop_words_ attribute of the vectorizer object. If the training data contained passwords, API keys, or other secrets in text form, these could be retrieved from the model.

The vulnerability is particularly concerning in scenarios where machine learning models are shared, deployed to production environments, or persisted to storage that may have different access controls than the original training data.

Detection Methods for CVE-2024-5206

Indicators of Compromise

• Presence of scikit-learn versions 1.4.1.post1 or earlier in application dependencies
• Trained TfidfVectorizer models with unexpectedly large stop_words_ attributes
• Serialized model files containing TfidfVectorizer instances created with older library versions
• Evidence of model file access or exfiltration from systems processing sensitive text data

Detection Strategies

• Audit Python environments for vulnerable scikit-learn versions using dependency scanning tools
• Review serialized model files to identify TfidfVectorizer instances that may contain leaked tokens
• Implement monitoring for unusual access patterns to stored machine learning model files
• Scan application code for usage of TfidfVectorizer with potentially sensitive training data

Monitoring Recommendations

• Monitor file system access to directories containing serialized scikit-learn models
• Implement alerting for unexpected read access to model files by unauthorized users or processes
• Track changes to Python package versions in production environments
• Review model deployment pipelines for proper access controls on trained model artifacts

How to Mitigate CVE-2024-5206

Immediate Actions Required

• Upgrade scikit-learn to version 1.5.0 or later to remediate the vulnerability
• Audit existing trained TfidfVectorizer models to assess potential data exposure
• Retrain and redeploy affected models using the patched library version
• Review and sanitize training data to remove sensitive information before vectorizer training

Patch Information

The vulnerability was fixed in scikit-learn version 1.5.0. The fix modifies how the stop_words_ attribute is populated to prevent storage of tokens that should not be persisted. The patch is available in the official GitHub commit.

Organizations should upgrade using pip:

pip install --upgrade scikit-learn>=1.5.0

After upgrading, any models trained with vulnerable versions should be retrained to ensure the stop_words_ attribute no longer contains leaked tokens.

Workarounds

• Preprocess training data to remove sensitive information before passing to TfidfVectorizer
• Manually clear the stop_words_ attribute after training if upgrading is not immediately possible
• Implement strict access controls on serialized model files containing TfidfVectorizer instances
• Consider using custom stop word lists rather than relying on automatic stop word detection

# Upgrade scikit-learn to patched version
pip install --upgrade "scikit-learn>=1.5.0"

# Verify installed version
python -c "import sklearn; print(sklearn.__version__)"