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CVE-2022-29211

Segfault in TensorFlow if `tf.histogram_fixed_width` is called with NaN values

5.5 MediumFix available

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of tf.histogram_fixed_width is vulnerable to a crash when the values array contain Not a Number (NaN) elements. The implementation assumes that all floating point operations are defined and then converts a floating point result to an integer index. If values contains NaN then the result of the division is still NaN and the cast to int32 would result in a crash. This only occurs on the CPU implementation. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

Attack profile

How easy it is to exploit

Attack vector
Local
Complexity
Low
Privileges needed
Low
User interaction
None

What an attacker can affect

Confidentiality
None
Integrity
None
Availability
High

Exploit likelihood

0.32%Down 1.0 points since May 21, 2022
90%50%10%1%

What each source reports

CVE List Score used 5.5

Segfault in TensorFlow if `tf.histogram_fixed_width` is called with NaN values

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of tf.histogram_fixed_width is vulnerable to a crash when the values array contain Not a Number (NaN) elements. The implementation assumes that all floating point operations are defined and then converts a floating point result to an integer index. If values contains NaN then the result of the division is still NaN and the cast to int32 would result in a crash. This only occurs on the CPU implementation. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

Record
CVE-2022-29211
CVSS 3.1
5.5 MediumCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
Weakness
CWE-20
Updated
2026-11-02
EUVDEUVD-2022-5854 5.5

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of tf.histogram_fixed_width is vulnerable to a crash when the values array contain Not a Number (NaN) elements. The implementation assumes that all floating point operations are defined and then converts a floating point result to an integer index. If values contains NaN then the result of the division is still NaN and the cast to int32 would result in a crash. This only occurs on the CPU implementation. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

Record
EUVD-2022-5854
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
GitHub AdvisoriesGHSA-xrp2-fhq4-4q3w 5.5

Segfault if `tf.histogram_fixed_width` is called with NaN values in TensorFlow

Impact

The implementation of tf.histogram_fixed_width is vulnerable to a crash when the values array contain NaN elements:

import tensorflow as tf
import numpy as np

tf.histogram_fixed_width(values=np.nan, value_range=[1,2])

The implementation assumes that all floating point operations are defined and then converts a floating point result to an integer index:

index_to_bin.device(d) =
    ((values.cwiseMax(value_range(0)) - values.constant(value_range(0)))
         .template cast<double>() /
     step)
        .cwiseMin(nbins_minus_1)
        .template cast<int32>();

If values contains NaN then the result of the division is still NaN and the cast to int32 would result in a crash.

This only occurs on the CPU implementation.

Patches

We have patched the issue in GitHub commit e57fd691c7b0fd00ea3bfe43444f30c1969748b5.

The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, 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 externally via a GitHub issue.

Record
GHSA-xrp2-fhq4-4q3w
CVSS 3.1
5.5 MediumCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
Weakness
CWE-20
Updated
2026-11-02
NVD 5.5

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of tf.histogram_fixed_width is vulnerable to a crash when the values array contain Not a Number (NaN) elements. The implementation assumes that all floating point operations are defined and then converts a floating point result to an integer index. If values contains NaN then the result of the division is still NaN and the cast to int32 would result in a crash. This only occurs on the CPU implementation. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

Record
CVE-2022-29211
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-20
Updated
2026-11-02
OSV 5.5

Segfault in TensorFlow if `tf.histogram_fixed_width` is called with NaN values

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of tf.histogram_fixed_width is vulnerable to a crash when the values array contain Not a Number (NaN) elements. The implementation assumes that all floating point operations are defined and then converts a floating point result to an integer index. If values contains NaN then the result of the division is still NaN and the cast to int32 would result in a crash. This only occurs on the CPU implementation. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

Record
CVE-2022-29211
CVSS 3.1
5.5 MediumCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
Weakness
CWE-20
Updated
2026-11-02
OSVGHSA-xrp2-fhq4-4q3w 5.5

Segfault if `tf.histogram_fixed_width` is called with NaN values in TensorFlow

Impact

The implementation of tf.histogram_fixed_width is vulnerable to a crash when the values array contain NaN elements:

import tensorflow as tf
import numpy as np

tf.histogram_fixed_width(values=np.nan, value_range=[1,2])

The implementation assumes that all floating point operations are defined and then converts a floating point result to an integer index:

index_to_bin.device(d) =
    ((values.cwiseMax(value_range(0)) - values.constant(value_range(0)))
         .template cast<double>() /
     step)
        .cwiseMin(nbins_minus_1)
        .template cast<int32>();

If values contains NaN then the result of the division is still NaN and the cast to int32 would result in a crash.

This only occurs on the CPU implementation.

Patches

We have patched the issue in GitHub commit e57fd691c7b0fd00ea3bfe43444f30c1969748b5.

The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, 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 externally via a GitHub issue.

Record
GHSA-xrp2-fhq4-4q3w
CVSS 3.1
5.5 MediumCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
Weakness
CWE-20
Updated
2026-11-02
Show 4 moreShow less
OSVPYSEC-2026-1048 5.5

Segfault if `tf.histogram_fixed_width` is called with NaN values in TensorFlow

Impact

The implementation of tf.histogram_fixed_width is vulnerable to a crash when the values array contain NaN elements:

import tensorflow as tf
import numpy as np

tf.histogram_fixed_width(values=np.nan, value_range=[1,2])

The implementation assumes that all floating point operations are defined and then converts a floating point result to an integer index:

index_to_bin.device(d) =
    ((values.cwiseMax(value_range(0)) - values.constant(value_range(0)))
         .template cast<double>() /
     step)
        .cwiseMin(nbins_minus_1)
        .template cast<int32>();

If values contains NaN then the result of the division is still NaN and the cast to int32 would result in a crash.

This only occurs on the CPU implementation.

Patches

We have patched the issue in GitHub commit e57fd691c7b0fd00ea3bfe43444f30c1969748b5.

The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, 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 externally via a GitHub issue.

Record
PYSEC-2026-1048
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
OSVPYSEC-2026-3260 5.5

Segfault if `tf.histogram_fixed_width` is called with NaN values in TensorFlow

Impact

The implementation of tf.histogram_fixed_width is vulnerable to a crash when the values array contain NaN elements:

import tensorflow as tf
import numpy as np

tf.histogram_fixed_width(values=np.nan, value_range=[1,2])

The implementation assumes that all floating point operations are defined and then converts a floating point result to an integer index:

index_to_bin.device(d) =
    ((values.cwiseMax(value_range(0)) - values.constant(value_range(0)))
         .template cast<double>() /
     step)
        .cwiseMin(nbins_minus_1)
        .template cast<int32>();

If values contains NaN then the result of the division is still NaN and the cast to int32 would result in a crash.

This only occurs on the CPU implementation.

Patches

We have patched the issue in GitHub commit e57fd691c7b0fd00ea3bfe43444f30c1969748b5.

The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, 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 externally via a GitHub issue.

Record
PYSEC-2026-3260
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
OSVPYSEC-2026-3382 5.5

Segfault if `tf.histogram_fixed_width` is called with NaN values in TensorFlow

Impact

The implementation of tf.histogram_fixed_width is vulnerable to a crash when the values array contain NaN elements:

import tensorflow as tf
import numpy as np

tf.histogram_fixed_width(values=np.nan, value_range=[1,2])

The implementation assumes that all floating point operations are defined and then converts a floating point result to an integer index:

index_to_bin.device(d) =
    ((values.cwiseMax(value_range(0)) - values.constant(value_range(0)))
         .template cast<double>() /
     step)
        .cwiseMin(nbins_minus_1)
        .template cast<int32>();

If values contains NaN then the result of the division is still NaN and the cast to int32 would result in a crash.

This only occurs on the CPU implementation.

Patches

We have patched the issue in GitHub commit e57fd691c7b0fd00ea3bfe43444f30c1969748b5.

The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, 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 externally via a GitHub issue.

Record
PYSEC-2026-3382
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
OSVBIT-tensorflow-2022-29211 No score

Segfault in TensorFlow if `tf.histogram_fixed_width` is called with NaN values

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of tf.histogram_fixed_width is vulnerable to a crash when the values array contain Not a Number (NaN) elements. The implementation assumes that all floating point operations are defined and then converts a floating point result to an integer index. If values contains NaN then the result of the division is still NaN and the cast to int32 would result in a crash. This only occurs on the CPU implementation. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

Record
BIT-tensorflow-2022-29211
Updated
2026-11-02

Affected products

Affected packages

Affected packages
PackageAffectedFixed
tensorflow
PyPI
 before 2.6.4
2.6.4
tensorflow
PyPI
 2.7.0 up to 2.7.2
2.7.2
tensorflow
PyPI
 2.8.0 up to 2.8.1
2.8.1
tensorflow-cpu
PyPI
 before 2.6.4
2.6.4
tensorflow-cpu
PyPI
 2.7.0 up to 2.7.2
2.7.2
tensorflow-cpu
PyPI
 2.8.0 up to 2.8.1
2.8.1
tensorflow-gpu
PyPI
 before 2.6.4
2.6.4
tensorflow-gpu
PyPI
 2.7.0 up to 2.7.2
2.7.2
tensorflow-gpu
PyPI
 2.8.0 up to 2.8.1
2.8.1
tensorflow
Bitnami
 before 2.6.4, 2.7.0 up to 2.7.2, 2.8.0 up to 2.8.1
2.6.42.7.22.8.1
Show 9 moreShow less
Affected packages
PackageAffectedFixed
tensorflow
PyPI
 before 2.6.4, 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
2.6.4
tensorflow
PyPI
 2.7.0 up to 2.7.2, plus listed versions
2.7.1
2.7.2
tensorflow-cpu
PyPI
 before 2.6.4, plus listed versions
1.15.02.1.02.1.12.1.22.1.3
Show 23 moreShow less
2.1.42.2.02.2.12.2.22.2.32.3.02.3.12.3.22.3.32.3.42.4.02.4.12.4.22.4.32.4.42.5.02.5.12.5.22.5.32.6.02.6.12.6.22.6.3
2.6.4
tensorflow-cpu
PyPI
 2.7.0 up to 2.7.2, plus listed versions
2.7.1
2.7.2
tensorflow-gpu
PyPI
 before 2.6.4, 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
2.6.4
tensorflow-gpu
PyPI
 2.7.0 up to 2.7.2, plus listed versions
2.7.1
2.7.2
tensorflow-gpu
PyPI
 before 2.6.4, 2.7.0 up to 2.7.2, 2.8.0 up to 2.8.1, 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
2.6.42.7.22.8.1
tensorflow
PyPI
 before 2.6.4, 2.7.0 up to 2.7.2, 2.8.0 up to 2.8.1, 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
2.6.42.7.22.8.1
tensorflow-cpu
PyPI
 before 2.6.4, 2.7.0 up to 2.7.2, 2.8.0 up to 2.8.1, plus listed versions
1.15.02.1.02.1.12.1.22.1.3
Show 24 moreShow less
2.1.42.2.02.2.12.2.22.2.32.3.02.3.12.3.22.3.32.3.42.4.02.4.12.4.22.4.32.4.42.5.02.5.12.5.22.5.32.6.02.6.12.6.22.6.32.7.1
2.6.42.7.22.8.1

History

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

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

In short

CVE-2022-29211 is a medium vulnerability (CVSS 5.5) in Google Tensorflow, published May 20, 2022. No source lists public exploit code. EPSS puts the chance of exploitation in the next 30 days at 0.32%, the 25th percentile. Fixed in tensorflow 2.6.4.

References

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