CVE-2021-29587: Division by zero in TFLite's implementation of SpaceToDepth

Background

The TensorFlow package is an open-source machine learning framework developed by Google. It provides a comprehensive set of tools and libraries for building and deploying machine learning models. TensorFlow Lite (TFLite) is a lightweight version of TensorFlow designed for mobile and embedded devices. TFLite includes various operators, including the SpaceToDepth operator, which rearranges data spatially by dividing it into blocks and rearranging them. This vulnerability specifically affects the implementation of the SpaceToDepth operator in TFLite.

Vulnerability Detail

The vulnerability arises from a lack of validation in the Prepare step of the SpaceToDepth operator's implementation. The code does not check for a zero value before performing division operations, which can lead to a division by zero error. An attacker can exploit this vulnerability by crafting a malicious model that sets the block_size parameter to zero. This can cause the application to crash or behave unexpectedly. The impact of this vulnerability is considered low, as it requires the attacker to have control over the model being executed.

pip install --upgrade "tensorflow==2.21.0"

Workaround

As a workaround, users can manually validate the block_size parameter before using it in the SpaceToDepth operator. By checking if the block_size is zero, the division operation can be avoided, preventing the division by zero error. Here is an example of how to implement the workaround in Python:

block_size = params['block_size']
if block_size != 0:
    output_height = input_height // block_size
    output_width = input_width // block_size
else:
    # Handle the case when block_size is zero
    output_height = 0
    output_width = 0

Conclusion

In conclusion, the Division by zero vulnerability in TFLite's implementation of the SpaceToDepth operator can be exploited by crafting a malicious model with a zero block_size parameter. This can lead to application crashes or unexpected behavior. The vulnerability has been patched in TensorFlow and users are advised to update their installations to the patched versions once they are released. Regularly updating TensorFlow is recommended to ensure the latest security fixes are applied. For further support on vulnerability remediation, please contact DevNack.com.

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