A use of uninitialized value vulnerability in Tensorflow

Impact

TensorFlow's Grappler optimizer has a [use of unitialized variable](https://github.com/tensorflow/tensorflow/blob/3457a2b122e50b4d44ceaaed5a663d635e5c22df/tensorflow/core/grappler/optimizers/auto_parallel.cc#L155-L164):

```cc

const NodeDef* dequeue_node;

for (const auto& train_node : train_nodes) {

if (IsDequeueOp(*train_node)) {

dequeue_node = train_node;

break;

}

}

if (dequeue_node) {

...

}

`

If the train_nodes vector (obtained from the saved model that gets optimized) does not contain a Dequeue node, then dequeue_node is left unitialized.

Patches

We have patched the issue in GitHub commit [68867bf01239d9e1048f98cbad185bf4761bedd3](https://github.com/tensorflow/tensorflow/commit/68867bf01239d9e1048f98cbad185bf4761bedd3).

The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, 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 Qian Feng from Baidu Security Team.