Vulnerability: Heap out of bounds read in `RequantizationRange` ### Impact
The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs:

```python
import tensorflow as tf

input = tf.constant([1], shape=[1], dtype=tf.qint32) 
input_max = tf.constant([], dtype=tf.float32)
input_min = tf.constant([], dtype=tf.float32)

tf.raw_ops.RequantizationRange(input=input, input_min=input_min, input_max=input_max)
```

The [implementation](https://github.com/tensorflow/tensorflow/blob/ac328eaa3870491ababc147822cd04e91a790643/tensorflow/core/kernels/requantization_range_op.cc#L49-L50) assumes that the `input_min` and `input_max` tensors have at least one element, as it accesses the first element in two arrays:

```cc
const float input_min_float = ctx->input(1).flat<float>()(0);
const float input_max_float = ctx->input(2).flat<float>()(0);
```

If the tensors are empty, `.flat<T>()` is an empty object, backed by an empty array. Hence, accesing even the 0th element is a read outside the bounds.

### Patches
We have patched the issue in GitHub commit [ef0c008ee84bad91ec6725ddc42091e19a30cf0e](https://github.com/tensorflow/tensorflow/commit/ef0c008ee84bad91ec6725ddc42091e19a30cf0e).

The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, 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 Ying Wang and Yakun Zhang of Baidu X-Team.
Vulnerability ID: GHSA-3h8m-483j-7xxm
CWE: CWE-125
Severity score: 2.5
Project: https://github.com/tensorflow/tensorflow
Published: 2021-05-21
File: tensorflow/core/kernels/requantization_range_op.cc
Commit message: Fix out of bound read in requantization_range_op.cc

PiperOrigin-RevId: 372129031
Change-Id: Ie684ab98a3840c5186ead3eafffc0e0ed0e8030d
Commit author: Laura Pak

Vulnerable version (before fix):
 class RequantizationRangeOp : public OpKernel {    void Compute(OpKernelContext* ctx) override {     const Tensor& input = ctx->input(0);     const float input_min_float = ctx->input(1).flat<float>()(0);     const float input_max_float = ctx->input(2).flat<float>()(0);     Tensor* output_min = nullptr;

Fixed version (after fix):
 class RequantizationRangeOp : public OpKernel {    void Compute(OpKernelContext* ctx) override {     const Tensor& input = ctx->input(0);     OP_REQUIRES(ctx, ctx->input(1).NumElements() > 0,                 errors::InvalidArgument("Input min must not be empty."));     OP_REQUIRES(ctx, ctx->input(2).NumElements() > 0,                 errors::InvalidArgument("Input max must not be empty."));     const float input_min_float = ctx->input(1).flat<float>()(0);     const float input_max_float = ctx->input(2).flat<float>()(0);     Tensor* output_min = nullptr;

Patch:
patch: @@ -46,6 +46,10 @@ class RequantizationRangeOp : public OpKernel {
 
   void Compute(OpKernelContext* ctx) override {
     const Tensor& input = ctx->input(0);
+    OP_REQUIRES(ctx, ctx->input(1).NumElements() > 0,
+                errors::InvalidArgument("Input min must not be empty."));
+    OP_REQUIRES(ctx, ctx->input(2).NumElements() > 0,
+                errors::InvalidArgument("Input max must not be empty."));
     const float input_min_float = ctx->input(1).flat<float>()(0);
     const float input_max_float = ctx->input(2).flat<float>()(0);
     Tensor* output_min = nullptr;
path: tensorflow/core/kernels/requantization_range_op.cc