Division by zero in `Conv3D`

### Impact A malicious user could trigger a division by 0 in `Conv3D` implementation: ```python import tensorflow as tf input_tensor = tf.constant([], shape=[0, 0, 0, 0, 0], dtype=tf.float32) filter_tensor = tf.constant([], shape=[0, 0, 0, 0, 0], dtype=tf.float32) tf.raw_ops.Conv3D(input=input_tensor, filter=filter_tensor, strides=[1, 56, 56, 56, 1], padding='VALID', data_format='NDHWC', dilations=[1, 1, 1, 23, 1]) ``` The [implementation](https://github.com/tensorflow/tensorflow/blob/42033603003965bffac51ae171b51801565e002d/tensorflow/core/kernels/conv_ops_3d.cc#L143-L145) does a modulo operation based on user controlled input: ```cc const int64 out_depth = filter.dim_size(4); OP_REQUIRES(context, in_depth % filter_depth == 0, ...); ``` Thus, when `filter` has a 0 as the fifth element, this results in a division by 0. Additionally, if the shape of the two tensors is not valid, an Eigen assertion can be triggered, resulting in a program crash: ```python import tensorflow as tf input_tensor = tf.constant([], shape=[2, 2, 2, 2, 0], dtype=tf.float32) filter_tensor = tf.constant([], shape=[0, 0, 2, 6, 2], dtype=tf.float32) tf.raw_ops.Conv3D(input=input_tensor, filter=filter_tensor, strides=[1, 56, 39, 34, 1], padding='VALID', data_format='NDHWC', dilations=[1, 1, 1, 1, 1]) ``` The shape of the two tensors must follow the constraints specified in the [op description](https://www.tensorflow.org/api_docs/python/tf/raw_ops/Conv3D). ### Patches We have patched the issue in GitHub commit [799f835a3dfa00a4d852defa29b15841eea9d64f](https://github.com/tensorflow/tensorflow/commit/799f835a3dfa00a4d852defa29b15841eea9d64f). 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 Yakun Zhang and Ying Wang 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-369

Also known as

BIT-tensorflow-2021-29517, CVE-2021-29517, PYSEC-2021-154, PYSEC-2021-445, PYSEC-2021-643

• github.com/tensorflow/tensorflow/security/advisories/GHSA-772p-x54p-hjrv
• nvd.nist.gov/vuln/detail/CVE-2021-29517
• github.com/tensorflow/tensorflow/commit/799f835a3dfa00a4d852defa29b15841eea9d64f
• github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-445.yaml
• github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-643.yaml
• github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-154.yaml