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CVE-2022-21725

Division by zero in Tensorflow

6.5 MediumFix available

Tensorflow is an Open Source Machine Learning Framework. The estimator for the cost of some convolution operations can be made to execute a division by 0. The function fails to check that the stride argument is strictly positive. Hence, the fix is to add a check for the stride argument to ensure it is valid. 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 estimator for the cost of some convolution operations can be made to execute a division by 0. The function fails to check that the stride argument is strictly positive. Hence, the fix is to add a check for the stride argument to ensure it is valid. 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-2022-21725
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-2022-0288 6.5

Tensorflow is an Open Source Machine Learning Framework. The estimator for the cost of some convolution operations can be made to execute a division by 0. The function fails to check that the stride argument is strictly positive. Hence, the fix is to add a check for the stride argument to ensure it is valid. 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-2022-0288
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-v3f7-j968-4h5f 6.8

Division by zero in Tensorflow

Impact

The estimator for the cost of some convolution operations can be made to execute a division by 0:

import tensorflow as tf

@tf.function
def test():
  y=tf.raw_ops.AvgPoolGrad(
    orig_input_shape=[1,1,1,1],
    grad=[[[[1.0],[1.0],[1.0]]],[[[2.0],[2.0],[2.0]]],[[[3.0],[3.0],[3.0]]]],
    ksize=[1,1,1,1],
    strides=[1,1,1,0],
    padding='VALID',
    data_format='NCHW')
  return y

test()

The function fails to check that the stride argument is stricly positive:

int64_t GetOutputSize(const int64_t input, const int64_t filter,
                      const int64_t stride, const Padding& padding) {
  // Logic for calculating output shape is from GetWindowedOutputSizeVerbose() 
  // function in third_party/tensorflow/core/framework/common_shape_fns.cc.
  if (padding == Padding::VALID) {
    return (input - filter + stride) / stride;
  } else {  // SAME.
    return (input + stride - 1) / stride;
  }
} 

Hence, the fix is to add a check for the stride argument to ensure it is valid.

Patches

We have patched the issue in GitHub commit 3218043d6d3a019756607643cf65574fbfef5d7a.

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 Yu Tian of Qihoo 360 AIVul Team.

Record
GHSA-v3f7-j968-4h5f
CVSS 4.0
6.8 MediumCVSS:4.0/AV:L/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 estimator for the cost of some convolution operations can be made to execute a division by 0. The function fails to check that the stride argument is strictly positive. Hence, the fix is to add a check for the stride argument to ensure it is valid. 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-2022-21725
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 estimator for the cost of some convolution operations can be made to execute a division by 0. The function fails to check that the stride argument is strictly positive. Hence, the fix is to add a check for the stride argument to ensure it is valid. 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-2022-21725
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-v3f7-j968-4h5f 6.8

Division by zero in Tensorflow

Impact

The estimator for the cost of some convolution operations can be made to execute a division by 0:

import tensorflow as tf

@tf.function
def test():
  y=tf.raw_ops.AvgPoolGrad(
    orig_input_shape=[1,1,1,1],
    grad=[[[[1.0],[1.0],[1.0]]],[[[2.0],[2.0],[2.0]]],[[[3.0],[3.0],[3.0]]]],
    ksize=[1,1,1,1],
    strides=[1,1,1,0],
    padding='VALID',
    data_format='NCHW')
  return y

test()

The function fails to check that the stride argument is stricly positive:

int64_t GetOutputSize(const int64_t input, const int64_t filter,
                      const int64_t stride, const Padding& padding) {
  // Logic for calculating output shape is from GetWindowedOutputSizeVerbose() 
  // function in third_party/tensorflow/core/framework/common_shape_fns.cc.
  if (padding == Padding::VALID) {
    return (input - filter + stride) / stride;
  } else {  // SAME.
    return (input + stride - 1) / stride;
  }
} 

Hence, the fix is to add a check for the stride argument to ensure it is valid.

Patches

We have patched the issue in GitHub commit 3218043d6d3a019756607643cf65574fbfef5d7a.

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 Yu Tian of Qihoo 360 AIVul Team.

Record
GHSA-v3f7-j968-4h5f
CVSS 4.0
6.8 MediumCVSS:4.0/AV:L/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-2026-3242 6.8

Division by zero in Tensorflow

Impact

The estimator for the cost of some convolution operations can be made to execute a division by 0:

import tensorflow as tf

@tf.function
def test():
  y=tf.raw_ops.AvgPoolGrad(
    orig_input_shape=[1,1,1,1],
    grad=[[[[1.0],[1.0],[1.0]]],[[[2.0],[2.0],[2.0]]],[[[3.0],[3.0],[3.0]]]],
    ksize=[1,1,1,1],
    strides=[1,1,1,0],
    padding='VALID',
    data_format='NCHW')
  return y

test()

The function fails to check that the stride argument is stricly positive:

int64_t GetOutputSize(const int64_t input, const int64_t filter,
                      const int64_t stride, const Padding& padding) {
  // Logic for calculating output shape is from GetWindowedOutputSizeVerbose() 
  // function in third_party/tensorflow/core/framework/common_shape_fns.cc.
  if (padding == Padding::VALID) {
    return (input - filter + stride) / stride;
  } else {  // SAME.
    return (input + stride - 1) / stride;
  }
} 

Hence, the fix is to add a check for the stride argument to ensure it is valid.

Patches

We have patched the issue in GitHub commit 3218043d6d3a019756607643cf65574fbfef5d7a.

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 Yu Tian of Qihoo 360 AIVul Team.

Record
PYSEC-2026-3242
CVSS 4.0
6.8 MediumCVSS:4.0/AV:L/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-2022-21725 No score

Division by zero in Tensorflow

Tensorflow is an Open Source Machine Learning Framework. The estimator for the cost of some convolution operations can be made to execute a division by 0. The function fails to check that the stride argument is strictly positive. Hence, the fix is to add a check for the stride argument to ensure it is valid. 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-2022-21725
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-2022-104 No score

Tensorflow is an Open Source Machine Learning Framework. The estimator for the cost of some convolution operations can be made to execute a division by 0. The function fails to check that the stride argument is strictly positive. Hence, the fix is to add a check for the stride argument to ensure it is valid. 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-2022-104
Updated
2026-11-02
OSVPYSEC-2022-49 No score

Tensorflow is an Open Source Machine Learning Framework. The estimator for the cost of some convolution operations can be made to execute a division by 0. The function fails to check that the stride argument is strictly positive. Hence, the fix is to add a check for the stride argument to ensure it is valid. 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-2022-49
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-2022-21725 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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