CVE-2022-35970

Description

### Impact If `QuantizedInstanceNorm` is given `x_min` or `x_max` tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack. ```python import tensorflow as tf output_range_given = False given_y_min = 0 given_y_max = 0 variance_epsilon = 1e-05 min_separation = 0.001 x = tf.constant(88, shape=[1,4,4,32], dtype=tf.quint8) x_min = tf.constant([], shape=[0], dtype=tf.float32) x_max = tf.constant(0, shape=[], dtype=tf.float32) tf.raw_ops.QuantizedInstanceNorm(x=x, x_min=x_min, x_max=x_max, output_range_given=output_range_given, given_y_min=given_y_min, given_y_max=given_y_max, variance_epsilon=variance_epsilon, min_separation=min_separation) ``` ### Patches We have patched the issue in GitHub commit [785d67a78a1d533759fcd2f5e8d6ef778de849e0](https://github.com/tensorflow/tensorflow/commit/785d67a78a1d533759fcd2f5e8d6ef778de849e0). 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 Neophytos Christou, Secure Systems Labs, Brown University.

How to fix CVE-2022-35970

To remediate CVE-2022-35970, 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-2022-35970 being exploited?

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