Vulnerability: Heap OOB in `ResourceScatterUpdate` ### Impact
An attacker can trigger a read from outside of bounds of heap allocated data by sending invalid arguments to `tf.raw_ops.ResourceScatterUpdate`:

```python
import tensorflow as tf

v = tf.Variable([b'vvv'])
tf.raw_ops.ResourceScatterUpdate(
  resource=v.handle,
  indices=[0],
  updates=['1', '2', '3', '4', '5'])
```
  
The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L919-L923) has an incomplete validation of the relationship between the shapes of `indices` and `updates`: instead of checking that the shape of `indices` is a prefix of the shape of `updates` (so that broadcasting can happen), code only checks that the number of elements in these two tensors are in a divisibility relationship.

### Patches 
We have patched the issue in GitHub commit [01cff3f986259d661103412a20745928c727326f](https://github.com/tensorflow/tensorflow/commit/01cff3f986259d661103412a20745928c727326f).

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-7fvx-3jfc-2cpc
CWE: CWE-125
Severity score: 7.3
Project: https://github.com/tensorflow/tensorflow
Published: 2021-08-25
File: tensorflow/core/kernels/resource_variable_ops.cc
Commit message: Fix heap OOB due to dimension mismatch in `ResourceScatterUpdate`

PiperOrigin-RevId: 388292801
Change-Id: Id9bd7244d98d41b1517d4771850b32782c0cc949
Commit author: Mihai Maruseac

Vulnerable version (before fix):
 class ResourceScatterUpdateOp : public OpKernel {                         params->dim_size(0), ")"));       } else {         int64_t num_updates = updates.NumElements();         OP_REQUIRES(c, num_updates % N == 0,                     errors::InvalidArgument(                         "shape of indices (", indices.shape().DebugString(),                         ") is not compatible with the shape of updates (",                         updates.shape().DebugString(), ")"));         auto updates_flat = updates.shaped<T, 2>({N, num_updates / N});          functor::ScatterFunctor<Device, T, Index, op> functor;

Fixed version (after fix):
 class ResourceScatterUpdateOp : public OpKernel {                         params->dim_size(0), ")"));       } else {         int64_t num_updates = updates.NumElements();         OP_REQUIRES(             c, TensorShapeUtils::StartsWith(updates.shape(), indices.shape()),             errors::InvalidArgument(                 "The shape of indices (", indices.shape().DebugString(),                 ") must be a prefix of the shape of updates (",                 updates.shape().DebugString(), ")"));         auto updates_flat = updates.shaped<T, 2>({N, num_updates / N});          functor::ScatterFunctor<Device, T, Index, op> functor;

Patch:
patch: @@ -955,11 +955,12 @@ class ResourceScatterUpdateOp : public OpKernel {
                         params->dim_size(0), ")"));
       } else {
         int64_t num_updates = updates.NumElements();
-        OP_REQUIRES(c, num_updates % N == 0,
-                    errors::InvalidArgument(
-                        "shape of indices (", indices.shape().DebugString(),
-                        ") is not compatible with the shape of updates (",
-                        updates.shape().DebugString(), ")"));
+        OP_REQUIRES(
+            c, TensorShapeUtils::StartsWith(updates.shape(), indices.shape()),
+            errors::InvalidArgument(
+                "The shape of indices (", indices.shape().DebugString(),
+                ") must be a prefix of the shape of updates (",
+                updates.shape().DebugString(), ")"));
         auto updates_flat = updates.shaped<T, 2>({N, num_updates / N});
 
         functor::ScatterFunctor<Device, T, Index, op> functor;
path: tensorflow/core/kernels/resource_variable_ops.cc