CVE-2021-41221: Access to invalid memory during shape inference in `Cudnn*` ops

Impact The shape inference code for the Cudnn operations in TensorFlow can be tricked into accessing invalid memory, via a heap buffer overflow:

python import tensorflow as tf

@tf.function def func(): return tf.rawops.CudnnRNNV3( input=[0.1, 0.1], inputh=[0.5], inputc=[0.1, 0.1, 0.1], params=[0.5, 0.5], sequencelengths=[-1, 0, 1]) func() This occurs because the ranks of the input, inputh and inputc parameters are not validated, but code assumes they have certain values:

cc auto inputshape = c->input(0); auto inputhshape = c->input(1); auto seqlength = c->Dim(inputshape, 0); auto batchsize = c->Dim(inputshape, 1); // assumes rank >= 2 auto numunits = c->Dim(inputhshape, 2); // assumes rank >= 3

Patches We have patched the issue in GitHub commit af5fcebb37c8b5d71c237f4e59c6477015c78ce6.

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.

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Attribution This vulnerability has been reported by members of the Aivul Team from Qihoo 360.