Find, fix and prevent vulnerabilities in your code.

Overview

Affected versions of this package are vulnerable to Infinite loop where an attacker can cause the application to stop responding by initiating a loop through functions affecting the Path module, such as joinpath, the overloaded division operator, and iterdir.

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its intended and legitimate users.

Unlike other vulnerabilities, DoS attacks usually do not aim at breaching security. Rather, they are focused on making websites and services unavailable to genuine users resulting in downtime.

One popular Denial of Service vulnerability is DDoS (a Distributed Denial of Service), an attack that attempts to clog network pipes to the system by generating a large volume of traffic from many machines.

When it comes to open source libraries, DoS vulnerabilities allow attackers to trigger such a crash or crippling of the service by using a flaw either in the application code or from the use of open source libraries.

Two common types of DoS vulnerabilities:

• High CPU/Memory Consumption- An attacker sending crafted requests that could cause the system to take a disproportionate amount of time to process. For example, commons-fileupload:commons-fileupload.

• Crash - An attacker sending crafted requests that could cause the system to crash. For Example, npm ws package

Remediation

Upgrade zipp to version 3.19.1 or higher.

References

• GitHub Commit
• GitHub Issue
• GitHub PR

Overview

urllib3 is a HTTP library with thread-safe connection pooling, file post, and more.

Affected versions of this package are vulnerable to Improper Removal of Sensitive Information Before Storage or Transfer due to the improper handling of the Proxy-Authorization header during cross-origin redirects when ProxyManager is not in use. When the conditions below are met, including non-recommended configurations, the contents of this header can be sent in an automatic HTTP redirect.

Notes:

To be vulnerable, the application must be doing all of the following:

1. Setting the Proxy-Authorization header without using urllib3's built-in proxy support.

2. Not disabling HTTP redirects (e.g. with redirects=False)

3. Either not using an HTTPS origin server, or having a proxy or target origin that redirects to a malicious origin.

Workarounds

1. Using the Proxy-Authorization header with urllib3's ProxyManager.

2. Disabling HTTP redirects using redirects=False when sending requests.

3. Not using the Proxy-Authorization header.

Remediation

Upgrade urllib3 to version 1.26.19, 2.2.2 or higher.

References

• GitHub Commit
• GitHub Commit

Overview

urllib3 is a HTTP library with thread-safe connection pooling, file post, and more.

Affected versions of this package are vulnerable to Open Redirect due to the retries parameter being ignored during PoolManager instantiation. An attacker can access unintended resources or endpoints by leveraging automatic redirects when the application expects redirects to be disabled at the connection pool level.

Note:

requests and botocore users are not affected.

Workaround

This can be mitigated by disabling redirects at the request() level instead of the PoolManager() level.

Remediation

Upgrade urllib3 to version 2.5.0 or higher.

References

• GitHub Commit

Overview

scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license.

Affected versions of this package are vulnerable to Storage of Sensitive Data in a Mechanism without Access Control due to the unexpected storage of all tokens present in the training data within the stop_words_ attribute. An attacker can access sensitive information, such as passwords or keys, by exploiting this behavior.

PoC

Limiting vocabulary is a very common setting hence provided by the library. The expected behaviour is that the object stores the frequent tokens, and discards the rest after the fitting process. In theory and practice, the vectorizer only needs the vocabulary and the rest of the possible tokens will be simply non needed, hence should be discarded.

While the object correctly forms the required vocabulary, it stores the rest of the tokens in the `stop_words_ attribute. Therefore stores the entire unique tokens that have been passed in the fitting operation. Below it's demonstrated this:

# ╰─$ pip freeze | grep pandas
# pandas==2.2.1
import pandas as pd
# ╰─$ pip freeze | grep scikit-learn
# scikit-learn==1.4.1.post1
from sklearn.feature_extraction.text import TfidfVectorizer

if __name__ == '__main__':
    # Fitting the vectorizer will save every token presented
    vectorizer = TfidfVectorizer(
        max_features=2,
        # min_df=2/6  # Same results occur with different ways of limiting the vocabulary
    ).fit(
        pd.Series([
            "hello", "world", "hello", "world", "secretkey", "password123"
        ])
    )
    # Expected storage for frequent tokens
    print(vectorizer.vocabulary_)  # {'hello': 0, 'server': 1}
    # Unexpected data leak
    print(vectorizer.stop_words_)  # {'password123', 'secretkey'}

It is demonstrated below that the storage in the stop_words_ attribute is unnecessary. Nullifying the attribute will give the same results:

# ╰─$ pip freeze | grep pandas
# pandas==2.2.1
import pandas as pd
# ╰─$ pip freeze | grep scikit-learn
# scikit-learn==1.4.1.post1
from sklearn.feature_extraction.text import TfidfVectorizer

if __name__ == '__main__':
    # Fitting the vectorizer will save every token presented
    vectorizer = TfidfVectorizer(
        max_features=2,
        # min_df=2/6  # Same results occur with different ways of limiting the vocabulary
    ).fit(
        pd.Series([
            "hello", "world", "hello", "world", "secretkey", "password123"
        ])
    )
    # Expected storage for frequent tokens
    print(vectorizer.vocabulary_)  # {'hello': 0, 'server': 1}
    # Unexpected data leak
    print(vectorizer.stop_words_)  # {'password123', 'secretkey'}

    # Wiping-out the stop_words_ attribute does not change the behaviour
    print(vectorizer.transform(["hello world"]).toarray())  # [[0.70710678 0.70710678]]
    vectorizer.stop_words_ = None
    assert vectorizer.stop_words_ is None
    print(vectorizer.transform(["hello world"]).toarray())  # [[0.70710678 0.70710678]]

Remediation

Upgrade scikit-learn to version 1.5.0 or higher.

References

• GitHub Commit

MPL-2.0 license