CVE-2021-29573: TensorFlow-CPU - Division by 0 in tf.raw_ops.MaxPoolGradWithArgmax

Background

TensorFlow is an end-to-end open source platform for machine learning. It provides a comprehensive ecosystem of tools, libraries, and resources for developing and deploying machine learning models. TensorFlow-CPU is a specific package that focuses on providing CPU support for TensorFlow. It allows users to run TensorFlow models on CPUs without the need for GPU hardware. However, the package is vulnerable to a division by 0 issue in the implementation of tf.raw_ops.MaxPoolGradWithArgmax.

Vulnerability Detail

The vulnerability arises from a lack of validation in the implementation of tf.raw_ops.MaxPoolGradWithArgmax in TensorFlow-CPU. The code fails to check if the batch dimension of the tensor is non-zero before dividing by this quantity. As a result, when the batch dimension is 0, a division by 0 occurs, leading to a potential crash or unexpected behavior. This vulnerability can be exploited by an attacker to cause a denial-of-service (DoS) attack or to manipulate the program's behavior.

pip install --upgrade "tensorflow-cpu==2.21.0"

Workaround

As a workaround, users can manually validate the batch dimension of the tensor before calling the vulnerable function. By ensuring that the batch dimension is non-zero, the division by 0 issue can be avoided. Here is an example of how to perform the validation in Python:

import tensorflow as tf

# Assuming 'tensor' is the input tensor
batch_dim = tf.shape(tensor)[0]
if batch_dim != 0:
    result = tf.raw_ops.MaxPoolGradWithArgmax(...)
else:
    # Handle the case when batch dimension is 0
    ...

By explicitly checking the batch dimension, users can prevent the vulnerable code from executing when the batch dimension is 0.

Conclusion

The division by 0 vulnerability in TensorFlow-CPU's tf.raw_ops.MaxPoolGradWithArgmax has the potential to cause crashes or unexpected behavior. It is crucial for users of TensorFlow-CPU to update their package to version 2.5.0 or newer to fix this vulnerability. Additionally, users can implement the provided workaround to mitigate the issue. For further support on vulnerability remediation, please contact DevNack.com.

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