GHSA-pvrc-hg3f-58r6

### Impact An attacker can write outside the bounds of heap allocated arrays by passing invalid arguments to `tf.raw_ops.Dilation2DBackpropInput`: ```python import tensorflow as tf input_tensor = tf.constant([1.1] * 81, shape=[3, 3, 3, 3], dtype=tf.float32) filter = tf.constant([], shape=[0, 0, 3], dtype=tf.float32) out_backprop = tf.constant([1.1] * 1062, shape=[3, 2, 59, 3], dtype=tf.float32) tf.raw_ops.Dilation2DBackpropInput( input=input_tensor, filter=filter, out_backprop=out_backprop, strides=[1, 40, 1, 1], rates=[1, 56, 56, 1], padding='VALID') ``` This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/afd954e65f15aea4d438d0a219136fc4a63a573d/tensorflow/core/kernels/dilation_ops.cc#L321-L322) does not validate before writing to the output array. ```cc in_backprop(b, h_in_max, w_in_max, d) += out_backprop(b, h_out, w_out, d); ``` The values for `h_out` and `w_out` are guaranteed to be in range for `out_backprop` (as they are loop indices bounded by the size of the array). However, there are no similar guarantees relating `h_in_max`/`w_in_max` and `in_backprop`. ### Patches We have patched the issue in GitHub commit [3f6fe4dfef6f57e768260b48166c27d148f3015f](https://github.com/tensorflow/tensorflow/commit/3f6fe4dfef6f57e768260b48166c27d148f3015f). 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 Yakun Zhang and Ying Wang of Baidu X-Team.