GHSA-v82p-hv3v-p6qp

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GHSA-v82p-hv3v-p6qp

Summary: Incomplete validation in MKL requantization

Details: ### 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](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/mkl/mkl_requantization_range_per_channel_op.cc) 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](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/mkl/mkl_requantize_per_channel_op.cc) does not perform full validation for all the input arguments. ### Patches We have patched the issue in GitHub commit [9e62869465573cb2d9b5053f1fa02a81fce21d69](https://github.com/tensorflow/tensorflow/commit/9e62869465573cb2d9b5053f1fa02a81fce21d69) and in the Github commit [203214568f5bc237603dbab6e1fd389f1572f5c9](https://github.com/tensorflow/tensorflow/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](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) 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.

References: https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v82p-hv3v-p6qp, https://nvd.nist.gov/vuln/detail/CVE-2021-37665, https://github.com/tensorflow/tensorflow/commit/203214568f5bc237603dbab6e1fd389f1572f5c9, https://github.com/tensorflow/tensorflow/commit/9e62869465573cb2d9b5053f1fa02a81fce21d69, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-578.yaml, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-776.yaml, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-287.yaml, https://github.com/tensorflow/tensorflow