First published: Fri Nov 05 2021(Updated: )
### 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.
Credit: security-advisories@github.com security-advisories@github.com
Affected Software | Affected Version | How to fix |
---|---|---|
Google TensorFlow | >=2.4.0<2.4.4 | |
Google TensorFlow | >=2.6.0<2.6.1 | |
Google TensorFlow | =2.7.0-rc0 | |
Google TensorFlow | =2.7.0-rc1 | |
pip/tensorflow-gpu | <2.4.4 | 2.4.4 |
pip/tensorflow-gpu | >=2.5.0<2.5.2 | 2.5.2 |
pip/tensorflow-gpu | >=2.6.0<2.6.1 | 2.6.1 |
pip/tensorflow-cpu | <2.4.4 | 2.4.4 |
pip/tensorflow-cpu | >=2.5.0<2.5.2 | 2.5.2 |
pip/tensorflow-cpu | >=2.6.0<2.6.1 | 2.6.1 |
pip/tensorflow | <2.4.4 | 2.4.4 |
pip/tensorflow | >=2.5.0<2.5.2 | 2.5.2 |
pip/tensorflow | >=2.6.0<2.6.1 | 2.6.1 |
>=2.4.0<2.4.4 | ||
>=2.6.0<2.6.1 | ||
=2.7.0-rc0 | ||
=2.7.0-rc1 |
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