CVE-2022-21725: Division by zero in Tensorflow

Impact The estimator for the cost of some convolution operations can be made to execute a division by 0:

python import tensorflow as tf

@tf.function def test(): y=tf.rawops.AvgPoolGrad( originputshape=[1,1,1,1], grad=[[[[1.0],[1.0],[1.0]]],[[[2.0],[2.0],[2.0]]],[[[3.0],[3.0],[3.0]]]], ksize=[1,1,1,1], strides=[1,1,1,0], padding='VALID', dataformat='NCHW') return y

test()

The function fails to check that the stride argument is stricly positive:

cc int64t GetOutputSize(const int64t input, const int64t filter, const int64t stride, const Padding& padding) { // Logic for calculating output shape is from GetWindowedOutputSizeVerbose() // function in thirdparty/tensorflow/core/framework/commonshapefns.cc. if (padding == Padding::VALID) { return (input - filter + stride) / stride; } else { // SAME. return (input + stride - 1) / stride; } }

Hence, the fix is to add a check for the stride argument to ensure it is valid.

Patches We have patched the issue in GitHub commit 3218043d6d3a019756607643cf65574fbfef5d7a.

The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

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Attribution This vulnerability has been reported by Yu Tian of Qihoo 360 AIVul Team.