GHSA-gh6x-4whr-2qv4 Null pointer dereference and heap OOB read in operations restoring tensors

• PyPI tensorflow: < 2.3.4 → fixed in 2.3.4 • PyPI tensorflow: >= 2.4.0, < 2.4.3 → fixed in 2.4.3 • PyPI tensorflow: >= 2.5.0, < 2.5.1 → fixed in 2.5.1 • PyPI tensorflow-cpu: < 2.3.4 → fixed in 2.3.4 • PyPI tensorflow-cpu: >= 2.4.0, < 2.4.3 → fixed in 2.4.3 • PyPI tensorflow-cpu: >= 2.5.0, < 2.5.1 → fixed in 2.5.1 • PyPI tensorflow-gpu: < 2.3.4 → fixed in 2.3.4 • PyPI tensorflow-gpu: >= 2.4.0, < 2.4.3 → fixed in 2.4.3 • PyPI tensorflow-gpu: >= 2.5.0, < 2.5.1 → fixed in 2.5.1

Impact When restoring tensors via raw APIs, if the tensor name is not provided, TensorFlow can be tricked into dereferencing a null pointer: ```python import tensorflow as tf tf.raw_ops.Restore( file_pattern=['/tmp'], tensor_name=[], default_value=21, dt=tf.int, preferred_shard=1) ``` The same undefined behavior can be triggered by `tf.raw_ops.RestoreSlice`: ```python import tensorflow as tf tf.raw_ops.RestoreSlice( file_pattern=['/tmp'], tensor_name=[], shape_and_slice='2', dt=inp.array([tf.int]), preferred_shard=1) ``` Alternatively, attackers can read memory outside the bounds of heap allocated data by providing some tensor names but not enough for a successful restoration: ```python import tensorflow as tf tf.raw_ops.Restore( file_pattern=['/tmp'], tensor_name=['x'], default_value=21, dt=tf.int, preferred_shard=42) ``` The implementation retrieves the tensor list corresponding to the `tensor_name` user controlled input and immediately retrieves the tensor at the restoration index (controlled via `preferred_shard` argument). This occurs without validating that the provided list has enough values. If the list is empty this results in dereferencing a null pointer (undefined behavior). If, however, the list has some elements, if the restoration index is outside the bounds this results in heap OOB read. Patches We have patched the issue in GitHub commit 9e82dce6e6bd1f36a57e08fa85af213e2b2f2622. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, 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 members of the Aivul Team from Qihoo 360.