TensorFlow is an end-to-end open source platform for machine learning. In affected versions due to incomplete validation in tf.raw_ops.QuantizeV2
, 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. The implementation has some validation but does not check that min_range
and max_range
both have the same non-zero number of elements. If axis
is provided (i.e., not -1
), then validation should check that it is a value in range for the rank of input
tensor and then the lengths of min_range
and max_range
inputs match the axis
dimension of the input
tensor. We have patched the issue in GitHub commit 6da6620efad397c85493b8f8667b821403516708. 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.