GHSA-7r94-xv9v-63jw A use of uninitialized value vulnerability in Tensorflow

• PyPI tensorflow: >= 2.6.0, < 2.6.1 → fixed in 2.6.1 • PyPI tensorflow: >= 2.5.0, < 2.5.2 → fixed in 2.5.2 • PyPI tensorflow: < 2.4.4 → fixed in 2.4.4 • PyPI tensorflow-cpu: >= 2.6.0, < 2.6.1 → fixed in 2.6.1 • PyPI tensorflow-cpu: >= 2.5.0, < 2.5.2 → fixed in 2.5.2 • PyPI tensorflow-cpu: < 2.4.4 → fixed in 2.4.4 • PyPI tensorflow-gpu: >= 2.6.0, < 2.6.1 → fixed in 2.6.1 • PyPI tensorflow-gpu: >= 2.5.0, < 2.5.2 → fixed in 2.5.2 • PyPI tensorflow-gpu: < 2.4.4 → fixed in 2.4.4

Impact TensorFlow's Grappler optimizer has a use of unitialized variable: ```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. 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 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.