CVE-2022-21736

Undefined behavior in Tensorflow

7.6 HighFix available

Tensorflow is an Open Source Machine Learning Framework. The implementation of SparseTensorSliceDataset has an undefined behavior: under certain condition it can be made to dereference a nullptr value. The 3 input arguments to SparseTensorSliceDataset represent a sparse tensor. However, there are some preconditions that these arguments must satisfy but these are not validated in the implementation. 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: Low
Integrity: Low
Availability: High

Exploit likelihood

0.75%Down 0.1 points since Feb 8, 2022

What each source reports

CVE List Score used 7.6

Undefined behavior in Tensorflow

Record: CVE-2022-21736
CVSS 3.1: 7.6 HighCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:H
Weakness: CWE-476
Updated: 2026-11-02

EUVDEUVD-2022-0299 7.6

Record: EUVD-2022-0299
CVSS 3.1: 7.6 HighCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:H
Updated: 2026-11-02

GitHub AdvisoriesGHSA-pfjj-m3jj-9jc9 7.2

Undefined behavior in `SparseTensorSliceDataset`

Impact

The implementation of SparseTensorSliceDataset has an undefined behavior: under certain condition it can be made to dereference a nullptr value:

import tensorflow as tf
import numpy as np

tf.raw_ops.SparseTensorSliceDataset(
  indices=[[]],
  values=[],
  dense_shape=[1,1])

The 3 input arguments represent a sparse tensor. However, there are some preconditions that these arguments must satisfy but these are not validated in the implementation.

Patches

We have patched the issue in GitHub commit 965b97e4a9650495cda5a8c210ef6684b4b9eceb.

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-pfjj-m3jj-9jc9
CVSS 4.0: 7.2 HighCVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:L/VI:L/VA:H/SC:N/SI:N/SA:N
CVSS 3.1: 7.6 HighCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:H
Weakness: CWE-476
Updated: 2026-11-02

NVD 7.6

Record: CVE-2022-21736
CVSS 3.1: 7.6 HighCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:H
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-476
Updated: 2026-11-02

OSV 7.6

Undefined behavior in Tensorflow

Record: CVE-2022-21736
CVSS 3.1: 7.6 HighCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:H
Updated: 2026-11-02

OSVGHSA-pfjj-m3jj-9jc9 7.2

Undefined behavior in `SparseTensorSliceDataset`

Impact

The implementation of SparseTensorSliceDataset has an undefined behavior: under certain condition it can be made to dereference a nullptr value:

import tensorflow as tf
import numpy as np

tf.raw_ops.SparseTensorSliceDataset(
  indices=[[]],
  values=[],
  dense_shape=[1,1])

The 3 input arguments represent a sparse tensor. However, there are some preconditions that these arguments must satisfy but these are not validated in the implementation.

Patches

We have patched the issue in GitHub commit 965b97e4a9650495cda5a8c210ef6684b4b9eceb.

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-pfjj-m3jj-9jc9
CVSS 4.0: 7.2 HighCVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:L/VI:L/VA:H/SC:N/SI:N/SA:N
CVSS 3.1: 7.6 HighCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:H
Weakness: CWE-476
Updated: 2026-11-02

Show 5 moreShow less

OSVPYSEC-2026-3223 7.2

Undefined behavior in `SparseTensorSliceDataset`

Impact

The implementation of SparseTensorSliceDataset has an undefined behavior: under certain condition it can be made to dereference a nullptr value:

import tensorflow as tf
import numpy as np

tf.raw_ops.SparseTensorSliceDataset(
  indices=[[]],
  values=[],
  dense_shape=[1,1])

The 3 input arguments represent a sparse tensor. However, there are some preconditions that these arguments must satisfy but these are not validated in the implementation.

Patches

We have patched the issue in GitHub commit 965b97e4a9650495cda5a8c210ef6684b4b9eceb.

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-2026-3223
CVSS 4.0: 7.2 HighCVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:L/VI:L/VA:H/SC:N/SI:N/SA:N
CVSS 3.1: 7.6 HighCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:H
Updated: 2026-11-02

OSVBIT-tensorflow-2022-21736 No score

Undefined behavior in Tensorflow

Record: BIT-tensorflow-2022-21736
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-2022-115 No score

Record: PYSEC-2022-115
Updated: 2026-11-02

OSVPYSEC-2022-60 No score

Record: PYSEC-2022-60
Updated: 2026-11-02

Affected products

google tensorflow

Affected packages

Affected packages
Package	Affected	Fixed
`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
Package	Affected	Fixed
`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
Recorded	Changed	Sources
Nov 6, 2026	facts
Nov 2, 2026	first indexed	CVE List, EUVD, GitHub Advisories, NVD, OSV

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

In short

CVE-2022-21736 is a high vulnerability (CVSS 7.6) 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 0.75%, the 55th percentile. Fixed in tensorflow 2.5.3.

References

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This page doesn't list everything

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