CVE-2021-37675: Division by 0 in TensorFlow Convolution Operators

Background

TensorFlow is an open-source machine learning framework widely used for developing and training deep neural networks. It provides a comprehensive set of tools and libraries for building and deploying machine learning models. TensorFlow includes various operators for convolution, a fundamental operation in deep learning that involves applying filters to input data. These convolution operators are used in many applications, including image and speech recognition. However, most implementations of convolution operators in TensorFlow are vulnerable to a division by 0 issue.

Vulnerability Detail

The division by 0 vulnerability in TensorFlow convolution operators allows an attacker to trigger a denial of service (DoS) by causing a crash. By providing specific input data, an attacker can exploit the vulnerability and cause the program to divide by zero, leading to a crash. The vulnerability arises from a lack of validation in the shape inference implementation of TensorFlow. The shape inference process fails to perform necessary validations before performing divisions and modulo operations, allowing the division by 0 vulnerability to occur. This vulnerability has a CVSS score of 5.5, indicating a moderate severity level.

pip install --upgrade "tensorflow==2.21.0"

Workaround

As a workaround, users can implement input validation in their code to prevent the division by 0 vulnerability. By checking the dimensions and shapes of input tensors before passing them to convolution operators, developers can ensure that the division operation will not result in a division by 0 error. Here is an example of how to perform input validation for a Conv2D operation in TensorFlow:

import tensorflow as tf

def conv2d_with_validation(input, filter, strides, padding):
  if input.shape[1] == 0 or input.shape[2] == 0 or filter.shape[1] == 0 or filter.shape[2] == 0:
    raise ValueError('Invalid input or filter shape')
  return tf.raw_ops.Conv2D(input=input, filter=filter, strides=strides, padding=padding)

By adding this validation step, the division by 0 vulnerability can be mitigated.

Conclusion

The division by 0 vulnerability in TensorFlow convolution operators can be mitigated by updating to the patched versions of TensorFlow. Users should regularly update their TensorFlow installation to ensure the latest security patches are applied. Implementing input validation in code can also help prevent the vulnerability. For further support and assistance in vulnerability remediation, please contact DevNack.com.

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