CVE-2022-23583: TensorFlow: SavedModel type confusion triggers DoS crash

Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a `SavedModel` such that any binary op would trigger `CHECK` failures. This occurs when the protobuf part corresponding to the tensor arguments is modified such that the `dtype` no longer matches the `dtype` expected by the op. In that case, calling the templated binary operator for the binary op would receive corrupted data, due to the type confusion involved. If `Tin` and `Tout` don't match the type of data in `out` and `input_*` tensors then `flat<*>` would interpret it wrongly. In most cases, this would be a silent failure, but we have noticed scenarios where this results in a `CHECK` crash, hence a denial of service. 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.

An adversary with a low-privilege account on a shared ML platform (e.g., a researcher account on an internal Kubeflow or MLflow deployment) crafts a SavedModel where the protobuf tensor descriptors for a binary op have a deliberately mismatched dtype — e.g., declaring float32 but providing int32 data. They upload this model to the model registry. When the inference service loads and executes the model, the templated binary operator in cwise_ops_common.h receives corrupted type data, the flat<> accessor interprets memory with the wrong type, and TensorFlow triggers a CHECK failure that crashes the serving process. If the service lacks process isolation or auto-restart, all tenants on that inference endpoint experience a complete outage until an operator intervenes.