CVE-2021-29577: Heap buffer overflow in tensorflow

Background

TensorFlow is an open-source machine learning framework developed by Google. It provides a comprehensive ecosystem of tools, libraries, and community resources that enable researchers and developers to build and deploy machine learning models. TensorFlow is widely used in various domains, including computer vision, natural language processing, and deep learning. However, a vulnerability has been discovered in TensorFlow that could lead to a heap buffer overflow.

Vulnerability Detail

The vulnerability, identified as CVE-2021-29577, is a heap buffer overflow in the AvgPool3DGrad function of TensorFlow. This function is used for calculating the gradient of the average pooling operation in 3D convolutional neural networks. The vulnerability arises from an assumption made in the implementation of AvgPool3DGrad that the orig_input_shape and grad tensors have similar first and last dimensions. However, this assumption is not validated, leading to a potential heap buffer overflow. An attacker could exploit this vulnerability to execute arbitrary code or cause a denial-of-service condition.

pip install --upgrade "tensorflow==2.21.0"

Workaround

If updating TensorFlow is not immediately feasible, users can mitigate the risk by avoiding the use of the AvgPool3DGrad function or ensuring that the orig_input_shape and grad tensors have compatible dimensions. Here is an example of how to use compatible tensors:

import tensorflow as tf

orig_input_shape = tf.constant([10, 6, 3, 7, 7], shape=[5], dtype=tf.int32)
grad = tf.constant([0.01, 0, 0], shape=[3, 1, 1, 1, 1], dtype=tf.float32)
ksize = [1, 1, 1, 1, 1]
strides = [1, 1, 1, 1, 1]
padding = "SAME"

if orig_input_shape.shape[0] == grad.shape[0] and orig_input_shape.shape[-1] == grad.shape[-1]:
  tf.raw_ops.AvgPool3DGrad(
    orig_input_shape=orig_input_shape, grad=grad, ksize=ksize, strides=strides,
    padding=padding)
else:
  print("Incompatible tensor dimensions")

By checking the dimensions of the tensors before calling AvgPool3DGrad, the risk of a heap buffer overflow can be mitigated.

Conclusion

The vulnerability in TensorFlow has been addressed by the TensorFlow team in the latest patched versions. Users are advised to update their installations to mitigate the risk of a heap buffer overflow. For further support on vulnerability remediation, please contact DevNack.com.

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