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CVE-2021-29573

Division by 0 in `MaxPoolGradWithArgmax`

2.5 LowFix available

TensorFlow is an end-to-end open source platform for machine learning. The implementation of tf.raw_ops.MaxPoolGradWithArgmax is vulnerable to a division by 0. 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. 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.

Attack profile

How easy it is to exploit

Attack vector
Local
Complexity
High
Privileges needed
Low
User interaction
None

What an attacker can affect

Confidentiality
None
Integrity
None
Availability
Low

Exploit likelihood

0.19%Down 0.4 points since May 15, 2021
90%50%10%1%

What each source reports

CVE List Score used 2.5

Division by 0 in `MaxPoolGradWithArgmax`

TensorFlow is an end-to-end open source platform for machine learning. The implementation of tf.raw_ops.MaxPoolGradWithArgmax is vulnerable to a division by 0. 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. 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.

Record
CVE-2021-29573
CVSS 3.1
2.5 LowCVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
Weakness
CWE-369
Updated
2026-11-02
EUVDEUVD-2021-0314 2.5

TensorFlow is an end-to-end open source platform for machine learning. The implementation of tf.raw_ops.MaxPoolGradWithArgmax is vulnerable to a division by 0. 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. 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.

Record
EUVD-2021-0314
CVSS 3.1
2.5 LowCVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
Updated
2026-11-03
GitHub AdvisoriesGHSA-9vpm-rcf4-9wqw 2.0

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.

Record
GHSA-9vpm-rcf4-9wqw
CVSS 4.0
2.0 LowCVSS:4.0/AV:L/AC:L/AT:P/PR:L/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N
CVSS 3.1
2.5 LowCVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
Weakness
CWE-369
Updated
2026-11-02
NVD 2.5

TensorFlow is an end-to-end open source platform for machine learning. The implementation of tf.raw_ops.MaxPoolGradWithArgmax is vulnerable to a division by 0. 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. 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.

Record
CVE-2021-29573
CVSS 3.1
2.5 LowCVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
CVSS 3.1
5.5 MediumCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
CVSS 2.0
2.1 LowAV:L/AC:L/Au:N/C:N/I:N/A:P
Weakness
CWE-369
Updated
2026-11-02
OSV 5.5

TensorFlow is an end-to-end open source platform for machine learning. The implementation of tf.raw_ops.MaxPoolGradWithArgmax is vulnerable to a division by 0. 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. 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.

Record
CVE-2021-29573
CVSS 3.1
5.5 MediumCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
Updated
2026-11-02
OSVGHSA-9vpm-rcf4-9wqw 2.0

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.

Record
GHSA-9vpm-rcf4-9wqw
CVSS 4.0
2.0 LowCVSS:4.0/AV:L/AC:L/AT:P/PR:L/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N
CVSS 3.1
2.5 LowCVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
Weakness
CWE-369
Updated
2026-11-02
Show 4 moreShow less
OSVBIT-tensorflow-2021-29573 No score

Division by 0 in `MaxPoolGradWithArgmax`

TensorFlow is an end-to-end open source platform for machine learning. The implementation of tf.raw_ops.MaxPoolGradWithArgmax is vulnerable to a division by 0. 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. 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.

Record
BIT-tensorflow-2021-29573
Updated
2026-11-02
OSVPYSEC-2021-210 No score

TensorFlow is an end-to-end open source platform for machine learning. The implementation of tf.raw_ops.MaxPoolGradWithArgmax is vulnerable to a division by 0. 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. 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.

Record
PYSEC-2021-210
Updated
2026-11-02
OSVPYSEC-2021-501 No score

TensorFlow is an end-to-end open source platform for machine learning. The implementation of tf.raw_ops.MaxPoolGradWithArgmax is vulnerable to a division by 0. 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. 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.

Record
PYSEC-2021-501
Updated
2026-11-02
OSVPYSEC-2021-699 No score

TensorFlow is an end-to-end open source platform for machine learning. The implementation of tf.raw_ops.MaxPoolGradWithArgmax is vulnerable to a division by 0. 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. 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.

Record
PYSEC-2021-699
Updated
2026-11-02

Affected products

Affected packages

Affected packages
PackageAffectedFixed
tensorflow
PyPI
 before 2.1.4
2.1.4
tensorflow
PyPI
 2.2.0 up to 2.2.3
2.2.3
tensorflow
PyPI
 2.3.0 up to 2.3.3
2.3.3
tensorflow
PyPI
 2.4.0 up to 2.4.2
2.4.2
tensorflow-cpu
PyPI
 before 2.1.4
2.1.4
tensorflow-cpu
PyPI
 2.2.0 up to 2.2.3
2.2.3
tensorflow-cpu
PyPI
 2.3.0 up to 2.3.3
2.3.3
tensorflow-cpu
PyPI
 2.4.0 up to 2.4.2
2.4.2
tensorflow-gpu
PyPI
 before 2.1.4
2.1.4
tensorflow-gpu
PyPI
 2.2.0 up to 2.2.3
2.2.3
Show 18 moreShow less
Affected packages
PackageAffectedFixed
tensorflow-gpu
PyPI
 2.3.0 up to 2.3.3
2.3.3
tensorflow-gpu
PyPI
 2.4.0 up to 2.4.2
2.4.2
tensorflow
Bitnami
 before 2.1.4, 2.2.0 up to 2.2.3, 2.3.0 up to 2.3.3, 2.4.0 up to 2.4.2
2.1.42.2.32.3.32.4.2
tensorflow
PyPI
 before 2.1.4, plus listed versions
0.12.00.12.11.0.01.0.11.1.0
Show 35 moreShow less
1.10.01.10.11.11.01.12.01.12.21.12.31.13.11.13.21.14.01.15.01.15.21.15.31.15.41.15.51.2.01.2.11.3.01.4.01.4.11.5.01.5.11.6.01.7.01.7.11.8.01.9.02.0.02.0.12.0.22.0.32.0.42.1.02.1.12.1.22.1.3
2.1.4
tensorflow
PyPI
 2.2.0 up to 2.2.3, plus listed versions
2.2.12.2.2
2.2.3
tensorflow
PyPI
 2.3.0 up to 2.3.3, plus listed versions
2.3.12.3.2
2.3.3
tensorflow
PyPI
 2.4.0 up to 2.4.2, plus listed versions
2.4.1
2.4.2
tensorflow-cpu
PyPI
 before 2.1.4, plus listed versions
1.15.02.1.02.1.12.1.22.1.3
2.1.4
tensorflow-cpu
PyPI
 2.2.0 up to 2.2.3, plus listed versions
2.2.12.2.2
2.2.3
tensorflow-cpu
PyPI
 2.3.0 up to 2.3.3, plus listed versions
2.3.12.3.2
2.3.3
tensorflow-cpu
PyPI
 2.4.0 up to 2.4.2, plus listed versions
2.4.1
2.4.2
tensorflow-gpu
PyPI
 before 2.1.4, plus listed versions
0.12.00.12.11.0.01.0.11.1.0
Show 35 moreShow less
1.10.01.10.11.11.01.12.01.12.21.12.31.13.11.13.21.14.01.15.01.15.21.15.31.15.41.15.51.2.01.2.11.3.01.4.01.4.11.5.01.5.11.6.01.7.01.7.11.8.01.9.02.0.02.0.12.0.22.0.32.0.42.1.02.1.12.1.22.1.3
2.1.4
tensorflow-gpu
PyPI
 2.2.0 up to 2.2.3, plus listed versions
2.2.12.2.2
2.2.3
tensorflow-gpu
PyPI
 2.3.0 up to 2.3.3, plus listed versions
2.3.12.3.2
2.3.3
tensorflow-gpu
PyPI
 2.4.0 up to 2.4.2, plus listed versions
2.4.1
2.4.2
tensorflow
PyPI
 before 2.1.4, 2.2.0 up to 2.2.3, 2.3.0 up to 2.3.3, 2.4.0 up to 2.4.2, plus listed versions
0.12.00.12.0rc00.12.0rc10.12.11.0.0
Show 45 moreShow less
1.0.11.1.01.1.0rc01.1.0rc11.1.0rc21.10.01.10.0rc01.10.0rc11.10.11.11.01.11.0rc01.11.0rc11.11.0rc21.12.01.12.0rc01.12.0rc11.12.0rc21.12.21.12.31.13.0rc01.13.0rc11.13.0rc21.13.11.13.21.14.01.14.0rc01.14.0rc11.15.01.15.0rc01.15.0rc11.15.0rc21.15.0rc31.15.21.15.31.15.41.15.51.2.01.2.0rc01.2.0rc11.2.0rc21.2.11.3.01.3.0rc01.3.0rc11.3.0rc2
2.1.42.2.32.3.32.4.2
tensorflow-cpu
PyPI
 before 2.1.4, 2.2.0 up to 2.2.3, 2.3.0 up to 2.3.3, 2.4.0 up to 2.4.2, plus listed versions
1.15.02.1.02.1.12.1.22.1.3
Show 5 moreShow less
2.2.12.2.22.3.12.3.22.4.1
2.1.42.2.32.3.32.4.2
tensorflow-gpu
PyPI
 before 2.1.4, 2.2.0 up to 2.2.3, 2.3.0 up to 2.3.3, 2.4.0 up to 2.4.2, plus listed versions
0.12.00.12.11.0.01.0.11.1.0
Show 40 moreShow less
1.10.01.10.11.11.01.12.01.12.21.12.31.13.11.13.21.14.01.15.01.15.21.15.31.15.41.15.51.2.01.2.11.3.01.4.01.4.11.5.01.5.11.6.01.7.01.7.11.8.01.9.02.0.02.0.12.0.22.0.32.0.42.1.02.1.12.1.22.1.32.2.12.2.22.3.12.3.22.4.1
2.1.42.2.32.3.32.4.2

History

History
RecordedChangedSources
aliasesEUVD
first indexedCVE List, GitHub Advisories, NVD, OSV

Dates are when Every CVE recorded each change. Upstream dates may differ.

In short

CVE-2021-29573 is a low vulnerability (CVSS 2.5) in Google Tensorflow, published May 14, 2021. No source lists public exploit code. EPSS puts the chance of exploitation in the next 30 days at 0.19%, the 8th percentile. Fixed in tensorflow 2.1.4.

References

This page doesn't list everything

Left out here: 45 versions. The JSON record has them.

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