CVE-2021-29535: Heap buffer overflow in `QuantizedMul`

Impact An attacker can cause a heap buffer overflow in QuantizedMul by passing in invalid thresholds for the quantization:

python import tensorflow as tf

x = tf.constant([256, 328], shape=[1, 2], dtype=tf.quint8) y = tf.constant([256, 328], shape=[1, 2], dtype=tf.quint8) minx = tf.constant([], dtype=tf.float32) maxx = tf.constant([], dtype=tf.float32) miny = tf.constant([], dtype=tf.float32) maxy = tf.constant([], dtype=tf.float32)

tf.rawops.QuantizedMul(x=x, y=y, minx=minx, maxx=maxx, miny=miny, maxy=maxy)

This is because the implementation assumes that the 4 arguments are always valid scalars and tries to access the numeric value directly:

cc const float minx = context->input(2).flat<float>()(0); const float maxx = context->input(3).flat<float>()(0); const float miny = context->input(4).flat<float>()(0); const float maxy = context->input(5).flat<float>()(0);

However, if any of these tensors is empty, then .flat<T>() is an empty buffer and accessing the element at position 0 results in overflow.

Patches We have patched the issue in GitHub commit efea03b38fb8d3b81762237dc85e579cc5fc6e87.

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 Ying Wang and Yakun Zhang of Baidu X-Team.