CVE-2021-29577: TensorFlow GPU - Heap Buffer Overflow in AvgPool3DGrad

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. The TensorFlow GPU package, available on PyPI, is specifically designed to leverage the power of GPUs for accelerated machine learning computations.

Vulnerability Detail

The vulnerability (CVE-2021-29577) exists in the implementation of tf.raw_ops.AvgPool3DGrad in TensorFlow GPU. The vulnerable code can be found in the pooling_ops_3d.cc file of the TensorFlow repository. The implementation assumes that the orig_input_shape and grad tensors have similar first and last dimensions, but it fails to validate this assumption. As a result, a heap buffer overflow can occur, leading to potential memory corruption and arbitrary code execution. This vulnerability affects versions of TensorFlow GPU from 0 to 2.1.4, 2.2.0 to 2.2.3, 2.3.0 to 2.3.3, and 2.4.0 to 2.4.2.

pip install --upgrade "tensorflow-gpu==2.12.0"

Workaround

As a temporary workaround, you can avoid using the tf.raw_ops.AvgPool3DGrad function in your code. Instead, you can explore alternative pooling operations or implement a custom pooling function that does not rely on the vulnerable code. Here's an example of a custom 3D average pooling function using TensorFlow's high-level API:

import tensorflow as tf

def custom_avg_pool3d(input_tensor, pool_size, strides):
    return tf.nn.avg_pool3d(input_tensor, ksize=pool_size, strides=strides, padding='VALID')

By using this custom function, you can mitigate the risk of the heap buffer overflow.

Conclusion

The vulnerability (CVE-2021-29577) in TensorFlow GPU's tf.raw_ops.AvgPool3DGrad function can lead to a heap buffer overflow and potential memory corruption. It is crucial to upgrade to TensorFlow 2.5.0 or later to mitigate this vulnerability. Additionally, you can use the provided workaround or explore alternative pooling operations to reduce the risk. For further assistance or support in vulnerability remediation, please contact DevNack.com.

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