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Security Advisories: MDE2OlNlY3VyaXR5QWR2aXNvcnlHSFNBLTl2cG0tcmNmNC05d3F3

Division by 0 in `MaxPoolGradWithArgmax`

Impact

The implementation of tf.raw_ops.MaxPoolGradWithArgmax is vulnerable to a division by 0:

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 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.

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 Ying Wang and Yakun Zhang of Baidu X-Team.

Permalink: https://github.com/advisories/GHSA-9vpm-rcf4-9wqw
JSON: https://advisories.ecosyste.ms/api/v1/advisories/MDE2OlNlY3VyaXR5QWR2aXNvcnlHSFNBLTl2cG0tcmNmNC05d3F3
Source: GitHub Advisory Database
Origin: Unspecified
Severity: Low
Classification: General
Published: almost 3 years ago
Updated: about 1 year 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-9vpm-rcf4-9wqw, CVE-2021-29573
References: Repository: https://github.com/tensorflow/tensorflow
Blast Radius: 12.2

Affected Packages

pypi:tensorflow-gpu
Dependent packages: 146
Dependent repositories: 11,499
Downloads: 354,712 last month
Affected Version Ranges: >= 2.4.0, < 2.4.2, >= 2.3.0, < 2.3.3, >= 2.2.0, < 2.2.3, < 2.1.4
Fixed in: 2.4.2, 2.3.3, 2.2.3, 2.1.4
All affected versions: 0.12.0, 0.12.1, 1.0.0, 1.0.1, 1.1.0, 1.2.0, 1.2.1, 1.3.0, 1.4.0, 1.4.1, 1.5.0, 1.5.1, 1.6.0, 1.7.0, 1.7.1, 1.8.0, 1.9.0, 1.10.0, 1.10.1, 1.11.0, 1.12.0, 1.12.2, 1.12.3, 1.13.1, 1.13.2, 1.14.0, 1.15.0, 1.15.2, 1.15.3, 1.15.4, 1.15.5, 2.0.0, 2.0.1, 2.0.2, 2.0.3, 2.0.4, 2.1.0, 2.1.1, 2.1.2, 2.1.3, 2.2.0, 2.2.1, 2.2.2, 2.3.0, 2.3.1, 2.3.2, 2.4.0, 2.4.1
All unaffected versions: 2.1.4, 2.2.3, 2.3.3, 2.3.4, 2.4.2, 2.4.3, 2.4.4, 2.5.0, 2.5.1, 2.5.2, 2.5.3, 2.6.0, 2.6.1, 2.6.2, 2.6.3, 2.6.4, 2.6.5, 2.7.0, 2.7.1, 2.7.2, 2.7.3, 2.7.4, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.0, 2.9.1, 2.9.2, 2.9.3, 2.10.0, 2.10.1, 2.11.0, 2.12.0
pypi:tensorflow-cpu
Dependent packages: 71
Dependent repositories: 2,483
Downloads: 942,065 last month
Affected Version Ranges: >= 2.4.0, < 2.4.2, >= 2.3.0, < 2.3.3, >= 2.2.0, < 2.2.3, < 2.1.4
Fixed in: 2.4.2, 2.3.3, 2.2.3, 2.1.4
All affected versions: 1.15.0, 2.1.0, 2.1.1, 2.1.2, 2.1.3, 2.2.0, 2.2.1, 2.2.2, 2.3.0, 2.3.1, 2.3.2, 2.4.0, 2.4.1
All unaffected versions: 2.1.4, 2.2.3, 2.3.3, 2.3.4, 2.4.2, 2.4.3, 2.4.4, 2.5.0, 2.5.1, 2.5.2, 2.5.3, 2.6.0, 2.6.1, 2.6.2, 2.6.3, 2.6.4, 2.6.5, 2.7.0, 2.7.1, 2.7.2, 2.7.3, 2.7.4, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.0, 2.9.1, 2.9.2, 2.9.3, 2.10.0, 2.10.1, 2.11.0, 2.11.1, 2.12.0, 2.12.1, 2.13.0, 2.13.1, 2.14.0, 2.14.1, 2.15.0, 2.15.1, 2.16.1
pypi:tensorflow
Dependent packages: 1,733
Dependent repositories: 73,755
Downloads: 22,560,575 last month
Affected Version Ranges: >= 2.4.0, < 2.4.2, >= 2.3.0, < 2.3.3, >= 2.2.0, < 2.2.3, < 2.1.4
Fixed in: 2.4.2, 2.3.3, 2.2.3, 2.1.4
All affected versions: 0.12.0, 0.12.1, 1.0.0, 1.0.1, 1.1.0, 1.2.0, 1.2.1, 1.3.0, 1.4.0, 1.4.1, 1.5.0, 1.5.1, 1.6.0, 1.7.0, 1.7.1, 1.8.0, 1.9.0, 1.10.0, 1.10.1, 1.11.0, 1.12.0, 1.12.2, 1.12.3, 1.13.1, 1.13.2, 1.14.0, 1.15.0, 1.15.2, 1.15.3, 1.15.4, 1.15.5, 2.0.0, 2.0.1, 2.0.2, 2.0.3, 2.0.4, 2.1.0, 2.1.1, 2.1.2, 2.1.3, 2.2.0, 2.2.1, 2.2.2, 2.3.0, 2.3.1, 2.3.2, 2.4.0, 2.4.1
All unaffected versions: 2.1.4, 2.2.3, 2.3.3, 2.3.4, 2.4.2, 2.4.3, 2.4.4, 2.5.0, 2.5.1, 2.5.2, 2.5.3, 2.6.0, 2.6.1, 2.6.2, 2.6.3, 2.6.4, 2.6.5, 2.7.0, 2.7.1, 2.7.2, 2.7.3, 2.7.4, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.0, 2.9.1, 2.9.2, 2.9.3, 2.10.0, 2.10.1, 2.11.0, 2.11.1, 2.12.0, 2.12.1, 2.13.0, 2.13.1, 2.14.0, 2.14.1, 2.15.0, 2.15.1, 2.16.1