GHSA-v82p-hv3v-p6qp Incomplete validation in MKL requantization

• PyPI tensorflow: < 2.3.4 → fixed in 2.3.4 • PyPI tensorflow: >= 2.4.0, < 2.4.3 → fixed in 2.4.3 • PyPI tensorflow: >= 2.5.0, < 2.5.1 → fixed in 2.5.1 • PyPI tensorflow-cpu: < 2.3.4 → fixed in 2.3.4 • PyPI tensorflow-cpu: >= 2.4.0, < 2.4.3 → fixed in 2.4.3 • PyPI tensorflow-cpu: >= 2.5.0, < 2.5.1 → fixed in 2.5.1 • PyPI tensorflow-gpu: < 2.3.4 → fixed in 2.3.4 • PyPI tensorflow-gpu: >= 2.4.0, < 2.4.3 → fixed in 2.4.3 • PyPI tensorflow-gpu: >= 2.5.0, < 2.5.1 → fixed in 2.5.1

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: ```python 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`: ```python 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.