Vulnerability: Incomplete validation in `QuantizeV2` ### Impact                                                                                                                                                                                                                                                                                
Due to incomplete validation in `tf.raw_ops.QuantizeV2`, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays:

```python
import tensorflow as tf

tf.raw_ops.QuantizeV2(
  input=[1,2,3],
  min_range=[1,2],
  max_range=[],
  T=tf.qint32,
  mode='SCALED',
  round_mode='HALF_AWAY_FROM_ZERO',
  narrow_range=False,
  axis=1,
  ensure_minimum_range=3)
```

The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/quantize_op.cc#L59) has some validation but does not check that `min_range` and `max_range` both have the same non-zero number of elements. If `axis` is provided (i.e., not `-1`), then validation should check that it is a value in range for the rank of `input` tensor and then the lengths of `min_range` and `max_range` inputs match the `axis` dimension of the `input` tensor.
  
### Patches
We have patched the issue in GitHub commit [6da6620efad397c85493b8f8667b821403516708](https://github.com/tensorflow/tensorflow/commit/6da6620efad397c85493b8f8667b821403516708).
  
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, 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 members of the Aivul Team from Qihoo 360.
Vulnerability ID: GHSA-g25h-jr74-qp5j
CWE: CWE-20
Severity score: 7.8
Project: https://github.com/tensorflow/tensorflow
Published: 2021-08-25
File: tensorflow/core/kernels/quantize_op.cc
Commit message: Secure tf.raw_ops.QuantizeV2

Validate size and shape of min_range and max_range
Ensure axis is within input dims limits

PiperOrigin-RevId: 387232799
Change-Id: I36975281f7b5758e9e31a8dcc73fe610ef456318
Commit author: Laura Pak

Vulnerable version (before fix):
 class QuantizeV2Op : public OpKernel {      int num_slices = 1;     if (axis_ > -1) {       num_slices = input.dim_size(axis_);     }      const TensorShape& minmax_shape = ctx->input(1).shape();

Fixed version (after fix):
 class QuantizeV2Op : public OpKernel {      int num_slices = 1;     if (axis_ > -1) {       OP_REQUIRES(           ctx, input.dims() > axis_,           errors::InvalidArgument(               "Axis is on a zero-based index, so its value must always be less "               "than number of input's dims, but given axis value was ",               axis_, " and input's dims was ", input.dims()));       num_slices = input.dim_size(axis_);       OP_REQUIRES(ctx, input_min_range.dims() == 1,                   errors::InvalidArgument(                       "If axis is specified, min_range must be a 1-D tensor "                       "whose size matches the axis dimension of the input and "                       "output tensors, but min_range dims are ",                       input_min_range.dims()));       OP_REQUIRES(ctx, input_min_range.dim_size(0) == num_slices,                   errors::InvalidArgument(                       "If axis is specified, min_range must be a 1-D tensor "                       "whose size matches the axis dimension of the input and "                       "output tensors, but min_range is a 1-D tensor of size ",                       input_min_range.dim_size(0),                       " and input's axis dimension is of size ", num_slices));       OP_REQUIRES(ctx, input_max_range.dims() == 1,                   errors::InvalidArgument(                       "If axis is specified, max_range must be a 1-D tensor "                       "whose size matches the axis dimension of the input and "                       "output tensors, but max_range dims are ",                       input_max_range.dims()));       OP_REQUIRES(ctx, input_max_range.dim_size(0) == num_slices,                   errors::InvalidArgument(                       "If axis is specified, max_range must be a 1-D tensor "                       "whose size matches the axis dimension of the input and "                       "output tensors, but max_range is a 1-D tensor of size ",                       input_max_range.dim_size(0),                       " and input's axis dimension is of size ", num_slices));     } else {       OP_REQUIRES(ctx, input_min_range.NumElements() == 1,                   errors::InvalidArgument(                       "If axis is not specified, min_range must contain a "                       "single float element, but it contains ",                       input_min_range.NumElements(), " elements"));       OP_REQUIRES(ctx, input_max_range.NumElements() == 1,                   errors::InvalidArgument(                       "If axis is not specified, max_range must contain a "                       "single float element, but it contains ",                       input_max_range.NumElements(), " elements"));     }      const TensorShape& minmax_shape = ctx->input(1).shape();

Patch:
patch: @@ -113,7 +113,50 @@ class QuantizeV2Op : public OpKernel {
 
     int num_slices = 1;
     if (axis_ > -1) {
+      OP_REQUIRES(
+          ctx, input.dims() > axis_,
+          errors::InvalidArgument(
+              "Axis is on a zero-based index, so its value must always be less "
+              "than number of input's dims, but given axis value was ",
+              axis_, " and input's dims was ", input.dims()));
       num_slices = input.dim_size(axis_);
+      OP_REQUIRES(ctx, input_min_range.dims() == 1,
+                  errors::InvalidArgument(
+                      "If axis is specified, min_range must be a 1-D tensor "
+                      "whose size matches the axis dimension of the input and "
+                      "output tensors, but min_range dims are ",
+                      input_min_range.dims()));
+      OP_REQUIRES(ctx, input_min_range.dim_size(0) == num_slices,
+                  errors::InvalidArgument(
+                      "If axis is specified, min_range must be a 1-D tensor "
+                      "whose size matches the axis dimension of the input and "
+                      "output tensors, but min_range is a 1-D tensor of size ",
+                      input_min_range.dim_size(0),
+                      " and input's axis dimension is of size ", num_slices));
+      OP_REQUIRES(ctx, input_max_range.dims() == 1,
+                  errors::InvalidArgument(
+                      "If axis is specified, max_range must be a 1-D tensor "
+                      "whose size matches the axis dimension of the input and "
+                      "output tensors, but max_range dims are ",
+                      input_max_range.dims()));
+      OP_REQUIRES(ctx, input_max_range.dim_size(0) == num_slices,
+                  errors::InvalidArgument(
+                      "If axis is specified, max_range must be a 1-D tensor "
+                      "whose size matches the axis dimension of the input and "
+                      "output tensors, but max_range is a 1-D tensor of size ",
+                      input_max_range.dim_size(0),
+                      " and input's axis dimension is of size ", num_slices));
+    } else {
+      OP_REQUIRES(ctx, input_min_range.NumElements() == 1,
+                  errors::InvalidArgument(
+                      "If axis is not specified, min_range must contain a "
+                      "single float element, but it contains ",
+                      input_min_range.NumElements(), " elements"));
+      OP_REQUIRES(ctx, input_max_range.NumElements() == 1,
+                  errors::InvalidArgument(
+                      "If axis is not specified, max_range must contain a "
+                      "single float element, but it contains ",
+                      input_max_range.NumElements(), " elements"));
     }
 
     const TensorShape& minmax_shape = ctx->input(1).shape();
path: tensorflow/core/kernels/quantize_op.cc