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CVE-2022-21735

Division by zero in Tensorflow

6.5 MediumFix available

Tensorflow is an Open Source Machine Learning Framework. The implementation of FractionalMaxPool can be made to crash a TensorFlow process via a division by 0. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Attack profile

How easy it is to exploit

Attack vector
Network
Complexity
Low
Privileges needed
Low
User interaction
None

What an attacker can affect

Confidentiality
None
Integrity
None
Availability
High

Exploit likelihood

0.79%Down 0.1 points since Feb 8, 2022
90%50%10%1%

What each source reports

CVE List Score used 6.5

Division by zero in Tensorflow

Tensorflow is an Open Source Machine Learning Framework. The implementation of FractionalMaxPool can be made to crash a TensorFlow process via a division by 0. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Record
CVE-2022-21735
CVSS 3.1
6.5 MediumCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
Weakness
CWE-369
Updated
2026-11-02
EUVDEUVD-2022-0298 6.5

Tensorflow is an Open Source Machine Learning Framework. The implementation of FractionalMaxPool can be made to crash a TensorFlow process via a division by 0. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Record
EUVD-2022-0298
CVSS 3.1
6.5 MediumCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
Updated
2026-11-02
GitHub AdvisoriesGHSA-87v6-crgm-2gfj 7.1

Division by zero in Tensorflow

Impact

The implementation of FractionalMaxPool can be made to crash a TensorFlow process via a division by 0:

import tensorflow as tf
import numpy as np

tf.raw_ops.FractionalMaxPool(
  value=tf.constant(value=[[[[1, 4, 2, 3]]]], dtype=tf.int64),
  pooling_ratio=[1.0, 1.44, 1.73, 1.0],
  pseudo_random=False,
  overlapping=False,
  deterministic=False,
  seed=0,
  seed2=0,
  name=None)

Patches

We have patched the issue in GitHub commit ba4e8ac4dc2991e350d5cc407f8598c8d4ee70fb.

The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Faysal Hossain Shezan from University of Virginia.

Record
GHSA-87v6-crgm-2gfj
CVSS 4.0
7.1 HighCVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N
CVSS 3.1
6.5 MediumCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
Weakness
CWE-369
Updated
2026-11-02
NVD 6.5

Tensorflow is an Open Source Machine Learning Framework. The implementation of FractionalMaxPool can be made to crash a TensorFlow process via a division by 0. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Record
CVE-2022-21735
CVSS 3.1
6.5 MediumCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
CVSS 2.0
4.0 MediumAV:N/AC:L/Au:S/C:N/I:N/A:P
Weakness
CWE-369
Updated
2026-11-02
OSV 6.5

Division by zero in Tensorflow

Tensorflow is an Open Source Machine Learning Framework. The implementation of FractionalMaxPool can be made to crash a TensorFlow process via a division by 0. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Record
CVE-2022-21735
CVSS 3.1
6.5 MediumCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
Updated
2026-11-02
OSVGHSA-87v6-crgm-2gfj 7.1

Division by zero in Tensorflow

Impact

The implementation of FractionalMaxPool can be made to crash a TensorFlow process via a division by 0:

import tensorflow as tf
import numpy as np

tf.raw_ops.FractionalMaxPool(
  value=tf.constant(value=[[[[1, 4, 2, 3]]]], dtype=tf.int64),
  pooling_ratio=[1.0, 1.44, 1.73, 1.0],
  pseudo_random=False,
  overlapping=False,
  deterministic=False,
  seed=0,
  seed2=0,
  name=None)

Patches

We have patched the issue in GitHub commit ba4e8ac4dc2991e350d5cc407f8598c8d4ee70fb.

The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Faysal Hossain Shezan from University of Virginia.

Record
GHSA-87v6-crgm-2gfj
CVSS 4.0
7.1 HighCVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N
CVSS 3.1
6.5 MediumCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
Weakness
CWE-369
Updated
2026-11-02
Show 5 moreShow less
OSVPYSEC-2026-3134 7.1

Division by zero in Tensorflow

Impact

The implementation of FractionalMaxPool can be made to crash a TensorFlow process via a division by 0:

import tensorflow as tf
import numpy as np

tf.raw_ops.FractionalMaxPool(
  value=tf.constant(value=[[[[1, 4, 2, 3]]]], dtype=tf.int64),
  pooling_ratio=[1.0, 1.44, 1.73, 1.0],
  pseudo_random=False,
  overlapping=False,
  deterministic=False,
  seed=0,
  seed2=0,
  name=None)

Patches

We have patched the issue in GitHub commit ba4e8ac4dc2991e350d5cc407f8598c8d4ee70fb.

The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Faysal Hossain Shezan from University of Virginia.

Record
PYSEC-2026-3134
CVSS 4.0
7.1 HighCVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N
CVSS 3.1
6.5 MediumCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
Updated
2026-11-02
OSVBIT-tensorflow-2022-21735 No score

Division by zero in Tensorflow

Tensorflow is an Open Source Machine Learning Framework. The implementation of FractionalMaxPool can be made to crash a TensorFlow process via a division by 0. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Record
BIT-tensorflow-2022-21735
Updated
2026-11-02
OSVopenSUSE-SU-2024:12116-1 No score

tensorflow-lite-2.9.1-1.1 on GA media

These are all security issues fixed in the tensorflow-lite-2.9.1-1.1 package on the GA media of openSUSE Tumbleweed.

Record
openSUSE-SU-2024:12116-1
Updated
2026-11-02
OSVPYSEC-2022-114 No score

Tensorflow is an Open Source Machine Learning Framework. The implementation of FractionalMaxPool can be made to crash a TensorFlow process via a division by 0. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Record
PYSEC-2022-114
Updated
2026-11-02
OSVPYSEC-2022-59 No score

Tensorflow is an Open Source Machine Learning Framework. The implementation of FractionalMaxPool can be made to crash a TensorFlow process via a division by 0. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Record
PYSEC-2022-59
Updated
2026-11-02

Affected products

Affected packages

Affected packages
PackageAffectedFixed
tensorflow
PyPI
 before 2.5.3
2.5.3
tensorflow
PyPI
 2.6.0 up to 2.6.3
2.6.3
tensorflow
PyPI
 2.7.0 to 2.7.0
2.7.1
tensorflow-cpu
PyPI
 before 2.5.3
2.5.3
tensorflow-cpu
PyPI
 2.6.0 up to 2.6.3
2.6.3
tensorflow-cpu
PyPI
 2.7.0 to 2.7.0
2.7.1
tensorflow-gpu
PyPI
 before 2.5.3
2.5.3
tensorflow-gpu
PyPI
 2.6.0 up to 2.6.3
2.6.3
tensorflow-gpu
PyPI
 2.7.0 to 2.7.0
2.7.1
tensorflow
Bitnami
 before 2.5.3, 2.6.0 up to 2.6.3, 2.7.0 up to 2.7.1
2.5.32.6.32.7.1
Show 12 moreShow less
Affected packages
PackageAffectedFixed
tensorflow
PyPI
 before 2.5.3, plus listed versions
0.12.00.12.11.0.01.0.11.1.0
Show 45 moreShow less
1.10.01.10.11.11.01.12.01.12.21.12.31.13.11.13.21.14.01.15.01.15.21.15.31.15.41.15.51.2.01.2.11.3.01.4.01.4.11.5.01.5.11.6.01.7.01.7.11.8.01.9.02.0.02.0.12.0.22.0.32.0.42.1.02.1.12.1.22.1.32.1.42.2.02.2.12.2.22.2.32.3.02.3.12.3.22.3.32.3.4
2.5.3
tensorflow
PyPI
 2.6.0 up to 2.6.3, plus listed versions
2.6.12.6.2
2.6.3
tensorflow
PyPI
 2.7.0 up to 2.7.1
2.7.1
tensorflow-cpu
PyPI
 before 2.5.3, plus listed versions
1.15.02.1.02.1.12.1.22.1.3
Show 18 moreShow less
2.1.42.2.02.2.12.2.22.2.32.3.02.3.12.3.22.3.32.3.42.4.02.4.12.4.22.4.32.4.42.5.02.5.12.5.2
2.5.3
tensorflow-cpu
PyPI
 2.6.0 up to 2.6.3, plus listed versions
2.6.12.6.2
2.6.3
tensorflow-cpu
PyPI
 2.7.0 up to 2.7.1
2.7.1
tensorflow-gpu
PyPI
 before 2.5.3, plus listed versions
0.12.00.12.11.0.01.0.11.1.0
Show 45 moreShow less
1.10.01.10.11.11.01.12.01.12.21.12.31.13.11.13.21.14.01.15.01.15.21.15.31.15.41.15.51.2.01.2.11.3.01.4.01.4.11.5.01.5.11.6.01.7.01.7.11.8.01.9.02.0.02.0.12.0.22.0.32.0.42.1.02.1.12.1.22.1.32.1.42.2.02.2.12.2.22.2.32.3.02.3.12.3.22.3.32.3.4
2.5.3
tensorflow-gpu
PyPI
 2.6.0 up to 2.6.3, plus listed versions
2.6.12.6.2
2.6.3
tensorflow-gpu
PyPI
 2.7.0 up to 2.7.1
2.7.1
tensorflow-gpu
PyPI
 before 2.5.3, 2.6.0 up to 2.6.3, plus listed versions
0.12.00.12.11.0.01.0.11.1.0
Show 45 moreShow less
1.10.01.10.11.11.01.12.01.12.21.12.31.13.11.13.21.14.01.15.01.15.21.15.31.15.41.15.51.2.01.2.11.3.01.4.01.4.11.5.01.5.11.6.01.7.01.7.11.8.01.9.02.0.02.0.12.0.22.0.32.0.42.1.02.1.12.1.22.1.32.1.42.2.02.2.12.2.22.2.32.3.02.3.12.3.22.3.32.3.4
2.5.32.6.3
tensorflow-cpu
PyPI
 before 2.5.3, 2.6.0 up to 2.6.3, plus listed versions
1.15.02.1.02.1.12.1.22.1.3
Show 20 moreShow less
2.1.42.2.02.2.12.2.22.2.32.3.02.3.12.3.22.3.32.3.42.4.02.4.12.4.22.4.32.4.42.5.02.5.12.5.22.6.12.6.2
2.5.32.6.3
tensorflow
PyPI
 before 2.5.3, 2.6.0 up to 2.6.3, 2.7.0 up to 2.7.1, plus listed versions
0.12.00.12.11.0.01.0.11.1.0
Show 45 moreShow less
1.10.01.10.11.11.01.12.01.12.21.12.31.13.11.13.21.14.01.15.01.15.21.15.31.15.41.15.51.2.01.2.11.3.01.4.01.4.11.5.01.5.11.6.01.7.01.7.11.8.01.9.02.0.02.0.12.0.22.0.32.0.42.1.02.1.12.1.22.1.32.1.42.2.02.2.12.2.22.2.32.3.02.3.12.3.22.3.32.3.4
2.5.32.6.32.7.1

History

History
RecordedChangedSources
facts
first indexedCVE List, EUVD, GitHub Advisories, NVD, OSV

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

In short

CVE-2022-21735 is a medium vulnerability (CVSS 6.5) in Google Tensorflow, published February 3, 2022. No source lists public exploit code. EPSS puts the chance of exploitation in the next 30 days at 0.79%, the 56th percentile. Fixed in tensorflow 2.5.3.

References

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Left out here: 36 versions. The JSON record has them.

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