Division by 0 in `FractionalAvgPool`

Impact

An attacker can cause a runtime division by zero error and denial of service in tf.raw_ops.FractionalAvgPool:

import tensorflow as tf

value = tf.constant([60], shape=[1, 1, 1, 1], dtype=tf.int32)
pooling_ratio = [1.0, 1.0000014345305555, 1.0, 1.0]
pseudo_random = False
overlapping = False
deterministic = False
seed = 0
seed2 = 0

tf.raw_ops.FractionalAvgPool(
  value=value, pooling_ratio=pooling_ratio, pseudo_random=pseudo_random,
  overlapping=overlapping, deterministic=deterministic, seed=seed, seed2=seed2)

This is because the implementation computes a divisor quantity by dividing two user controlled values:

for (int i = 0; i < tensor_in_and_out_dims; ++i) {
  output_size[i] = static_cast<int>(std::floor(input_size[i] / pooling_ratio_[i]));
  DCHECK_GT(output_size[i], 0); 
} 

The user controls the values of input_size[i] and pooling_ratio_[i] (via the value.shape() and pooling_ratio arguments). If the value in input_size[i] is smaller than the pooling_ratio_[i], then the floor operation results in output_size[i] being 0. The DCHECK_GT line is a no-op outside of debug mode, so in released versions of TF this does not trigger.

Later, these computed values are used as arguments to GeneratePoolingSequence. There, the first computation is a division in a modulo operation: