CVE-2021-29538: TensorFlow - Division by Zero Denial of Service

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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. It supports a wide range of platforms and devices, making it accessible and versatile for different applications. However, a vulnerability has been discovered in TensorFlow that can lead to a denial of service attack.

Vulnerability Detail

The vulnerability (CVE-2021-29538) in TensorFlow arises from a division by zero error in the Conv2DBackpropFilter function. The implementation of this function computes a divisor based on user-provided data, specifically the shape of the tensors given as arguments. If all shapes are empty, the work_unit_size becomes 0. However, there is no check for this case before division, resulting in a runtime exception. An attacker can exploit this vulnerability by crafting inputs that trigger the division by zero, causing the TensorFlow application to crash. This can lead to a denial of service, disrupting the availability of the affected system. The vulnerability has a medium severity rating.

Workaround

As a temporary workaround, users can implement input validation to check for empty shapes before invoking the Conv2DBackpropFilter function. By ensuring that at least one shape is non-empty, the division by zero error can be avoided. Here is an example code snippet in Python:

import tensorflow as tf

# Validate input shapes
if not any(shape for shape in input_shapes):
    raise ValueError('At least one input shape must be non-empty')

# Invoke Conv2DBackpropFilter function
output = tf.nn.conv2d_backprop_filter(...)

By performing this validation, the vulnerability can be mitigated until the updated versions of TensorFlow are installed.

Conclusion

The vulnerability in TensorFlow (CVE-2021-29538) can lead to a denial of service attack by triggering a division by zero error. It is crucial to update to the latest patched versions of TensorFlow to mitigate this vulnerability. In the meantime, implementing input validation can serve as a temporary workaround. For further support on vulnerability remediation, please contact DevNack.com.

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