CVE-2021-29517: TensorFlow - Division by 0 in Conv3D Implementation

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 is widely used in various domains, including research, industry, and academia. The Conv3D implementation in TensorFlow is responsible for performing 3D convolution operations, which are commonly used in video and volumetric data analysis. This vulnerability affects versions of TensorFlow ranging from 0 to 2.4.2.

Vulnerability Detail

The vulnerability arises from a division by 0 in the Conv3D implementation of TensorFlow. The implementation performs a modulo operation based on user-controlled input. If the fifth element of the 'filter' tensor is set to 0, a division by 0 occurs. Additionally, if the shape of the two tensors involved in the convolution operation is invalid, it can trigger an Eigen assertion, leading to a program crash. An attacker with the ability to control the input to the Conv3D operation could exploit this vulnerability to cause a denial of service (DoS) or potentially execute arbitrary code.

pip install --upgrade "tensorflow==2.21.0"

Workaround

As a workaround, users can validate the input tensors' shapes before performing the Conv3D operation. By ensuring that the shapes are valid and the fifth element of the 'filter' tensor is non-zero, the division by 0 can be avoided. Here is an example code snippet demonstrating the workaround:

import tensorflow as tf

# Validate tensor shapes
if tensor1.shape[4] != 0 and tensor2.shape[4] != 0:
    # Perform Conv3D operation
    result = tf.nn.conv3d(tensor1, tensor2, strides=[1, 1, 1, 1, 1], padding='SAME')
else:
    # Handle invalid shapes
    print('Invalid tensor shapes')

Conclusion

The division by 0 vulnerability in the Conv3D implementation of TensorFlow can be exploited by a malicious user to cause a program crash, execute arbitrary code, or launch a denial of service attack. It is crucial for users to upgrade their TensorFlow installations to the patched versions or apply the provided workaround to mitigate the risk. For further assistance and support in vulnerability remediation, please contact DevNack.com.

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