Vulnerability: Integer overflow in Tensorflow ### Impact
The [implementation of `OpLevelCostEstimator::CalculateTensorSize`](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L1552-L1558) is vulnerable to an integer overflow if an attacker can create an operation which would involve a tensor with large enough number of elements:
```cc
int64_t OpLevelCostEstimator::CalculateTensorSize(
    const OpInfo::TensorProperties& tensor, bool* found_unknown_shapes) {
  int64_t count = CalculateTensorElementCount(tensor, found_unknown_shapes);
  int size = DataTypeSize(BaseType(tensor.dtype()));
  VLOG(2) << "Count: " << count << " DataTypeSize: " << size;
  return count * size;
}
```
Here, `count` and `size` can be large enough to cause `count * size` to overflow.

### Patches
We have patched the issue in GitHub commit [fcd18ce3101f245b083b30655c27b239dc72221e](https://github.com/tensorflow/tensorflow/commit/fcd18ce3101f245b083b30655c27b239dc72221e).

The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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.
Vulnerability ID: GHSA-c94w-c95p-phf8
CWE: CWE-190
Severity score: 6.5
Project: https://github.com/tensorflow/tensorflow
Published: 2022-02-10
File: tensorflow/core/grappler/costs/op_level_cost_estimator.cc
Commit message: Prevent integer overflow in `OpLevelCostEstimator::CalculateTensorSize`.

In order to not change the API, we return a negative value in case of overflow. A better fix is to change the API to return a status instead.

PiperOrigin-RevId: 408713061
Change-Id: I3771475b0c72a2844a3854086966562fd33f2da5
Commit author: Mihai Maruseac

Vulnerable version (before fix):
 int64_t OpLevelCostEstimator::CalculateTensorSize(   int64_t count = CalculateTensorElementCount(tensor, found_unknown_shapes);   int size = DataTypeSize(BaseType(tensor.dtype()));   VLOG(2) << "Count: " << count << " DataTypeSize: " << size;   return count * size; }  int64_t OpLevelCostEstimator::CalculateInputSize(const OpInfo& op_info,

Fixed version (after fix):
 int64_t OpLevelCostEstimator::CalculateTensorSize(   int64_t count = CalculateTensorElementCount(tensor, found_unknown_shapes);   int size = DataTypeSize(BaseType(tensor.dtype()));   VLOG(2) << "Count: " << count << " DataTypeSize: " << size;   int64_t tensor_size = MultiplyWithoutOverflow(count, size);   if (tensor_size < 0) {     VLOG(1) << "Overflow encountered when computing tensor size, multiplying "             << count << " with " << size;     return -1;   }   return tensor_size; }  int64_t OpLevelCostEstimator::CalculateInputSize(const OpInfo& op_info,

Patch:
patch: @@ -1555,7 +1555,13 @@ int64_t OpLevelCostEstimator::CalculateTensorSize(
   int64_t count = CalculateTensorElementCount(tensor, found_unknown_shapes);
   int size = DataTypeSize(BaseType(tensor.dtype()));
   VLOG(2) << "Count: " << count << " DataTypeSize: " << size;
-  return count * size;
+  int64_t tensor_size = MultiplyWithoutOverflow(count, size);
+  if (tensor_size < 0) {
+    VLOG(1) << "Overflow encountered when computing tensor size, multiplying "
+            << count << " with " << size;
+    return -1;
+  }
+  return tensor_size;
 }
 
 int64_t OpLevelCostEstimator::CalculateInputSize(const OpInfo& op_info,
path: tensorflow/core/grappler/costs/op_level_cost_estimator.cc