GHSA-3ff2-r28g-w7h9 Heap buffer overflow in `Transpose`

• 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 The shape inference function for `Transpose` is vulnerable to a heap buffer overflow: ```python import tensorflow as tf @tf.function def test(): y = tf.raw_ops.Transpose(x=[1,2,3,4],perm=[-10]) return y test() ``` This occurs whenever `perm` contains negative elements. The shape inference function does not validate that the indices in `perm` are all valid: ```cc for (int32_t i = 0; i = rank) { return errors::InvalidArgument("perm dim ", in_idx, " is out of range of input rank ", rank); } dims[i] = c->Dim(input, in_idx); } ``` where `Dim(tensor, index)` accepts either a positive index less than the rank of the tensor or the special value `-1` for unknown dimensions. Patches We have patched the issue in GitHub commit c79ba87153ee343401dbe9d1954d7f79e521eb14. 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 members of the Aivul Team from Qihoo 360.