GHSA-9vpm-rcf4-9wqw

Dashboard / Vulnerabilities / GHSA-9vpm-rcf4-9wqw

GHSA-9vpm-rcf4-9wqw

Summary: Division by 0 in `MaxPoolGradWithArgmax`

Details: ### Impact The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` is vulnerable to a division by 0: ```python import tensorflow as tf input = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32) grad = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32) argmax = tf.constant([], shape=[0], dtype=tf.int64) ksize = [1, 1, 1, 1] strides = [1, 1, 1, 1] tf.raw_ops.MaxPoolGradWithArgmax( input=input, grad=grad, argmax=argmax, ksize=ksize, strides=strides, padding='SAME', include_batch_in_index=False) ``` The [implementation](https://github.com/tensorflow/tensorflow/blob/279bab6efa22752a2827621b7edb56a730233bd8/tensorflow/core/kernels/maxpooling_op.cc#L1033-L1034) fails to validate that the batch dimension of the tensor is non-zero, before dividing by this quantity. ### Patches We have patched the issue in GitHub commit [376c352a37ce5a68b721406dc7e77ac4b6cf483d](https://github.com/tensorflow/tensorflow/commit/376c352a37ce5a68b721406dc7e77ac4b6cf483d). 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.

References: https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9vpm-rcf4-9wqw, https://nvd.nist.gov/vuln/detail/CVE-2021-29573, https://github.com/tensorflow/tensorflow/commit/376c352a37ce5a68b721406dc7e77ac4b6cf483d, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-501.yaml, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-699.yaml, https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-210.yaml, https://github.com/tensorflow/tensorflow