CVE-2021-29546: Division by 0 in `QuantizedBiasAdd`

Impact An attacker can trigger an integer division by zero undefined behavior in tf.rawops.QuantizedBiasAdd:

python import tensorflow as tf

inputtensor = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.quint8) bias = tf.constant([], shape=[0], dtype=tf.quint8) mininput = tf.constant(-10.0, dtype=tf.float32) maxinput = tf.constant(-10.0, dtype=tf.float32) minbias = tf.constant(-10.0, dtype=tf.float32) maxbias = tf.constant(-10.0, dtype=tf.float32)

tf.rawops.QuantizedBiasAdd(input=inputtensor, bias=bias, mininput=mininput, maxinput=maxinput, minbias=minbias, maxbias=maxbias, outtype=tf.qint32)

This is because the implementation of the Eigen kernel does a division by the number of elements of the smaller input (based on shape) without checking that this is not zero:

cc template <typename T1, typename T2, typename T3> void QuantizedAddUsingEigen(const Eigen::ThreadPoolDevice& device, const Tensor& input, float inputmin, float inputmax, const Tensor& smallerinput, float smallerinputmin, float smallerinputmax, Tensor output, float outputmin, float outputmax) { ... const int64 inputelementcount = input.NumElements(); const int64 smallerinputelementcount = smallerinput.NumElements(); ... bcast[0] = inputelementcount / smallerinputelementcount; ... }

This integral division by 0 is undefined behavior.

Patches We have patched the issue in GitHub commit 67784700869470d65d5f2ef20aeb5e97c31673cb.

The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, 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 Yakun Zhang and Ying Wang of Baidu X-Team.