CVE-2021-41222: TensorFlow - Segfault in SplitV with Negative Arguments

Background

TensorFlow is an open source platform for machine learning. It provides a comprehensive ecosystem of tools, libraries, and resources for developing and deploying machine learning models. TensorFlow is widely used in various domains, including computer vision, natural language processing, and deep learning. It offers a flexible architecture that allows users to build and train machine learning models using high-level APIs or by directly manipulating tensors. However, a vulnerability has been discovered in TensorFlow that can lead to a segmentation fault (segfault) under certain conditions.

Vulnerability Detail

The vulnerability, identified as CVE-2021-41222, resides in the implementation of the SplitV function in TensorFlow. When the function is called with negative arguments in the size_splits parameter, a segfault can occur. This issue specifically arises when size_splits contains multiple values and at least one of them is negative. An attacker can exploit this vulnerability by supplying malicious input to trigger the segfault. The impact of this vulnerability can range from denial of service (DoS) to potential remote code execution, depending on the context in which TensorFlow is used. The vulnerability has been addressed in TensorFlow versions 2.7.0, 2.6.1, 2.5.2, and 2.4.4.

pip install --upgrade "tensorflow==2.21.0"

Workaround

As a temporary workaround, users can ensure that the size_splits parameter does not contain any negative values when calling the SplitV function. This can be achieved by validating the input and rejecting any negative values. Here is an example of how to perform this validation in Python:

def safe_splitv(size_splits):
    if any(split < 0 for split in size_splits):
        raise ValueError('Negative values are not allowed in size_splits')
    # Call SplitV function with validated size_splits
    ...

Conclusion

In conclusion, TensorFlow is affected by a vulnerability in the SplitV function that can lead to a segfault when negative arguments are provided. This vulnerability has been addressed in TensorFlow versions 2.7.0, 2.6.1, 2.5.2, and 2.4.4. Users are advised to update their TensorFlow installation to one of these patched versions to mitigate the risk. For further support on vulnerability remediation, please contact DevNack.com.

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