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CVE-2022-36005

`CHECK` fail in `FakeQuantWithMinMaxVarsGradient` in TensorFlow

5.9 MediumFix available

TensorFlow is an open source platform for machine learning. When tf.quantization.fake_quant_with_min_max_vars_gradient receives input min or max that is nonscalar, it gives a CHECK fail that can trigger a denial of service attack. We have patched the issue in GitHub commit f3cf67ac5705f4f04721d15e485e192bb319feed. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

Attack profile

How easy it is to exploit

Attack vector
Network
Complexity
High
Privileges needed
None
User interaction
None

What an attacker can affect

Confidentiality
None
Integrity
None
Availability
High

Exploit likelihood

0.48%Down 0.4 points since Sep 17, 2022
90%50%10%1%

What each source reports

CVE List Score used 5.9

`CHECK` fail in `FakeQuantWithMinMaxVarsGradient` in TensorFlow

TensorFlow is an open source platform for machine learning. When tf.quantization.fake_quant_with_min_max_vars_gradient receives input min or max that is nonscalar, it gives a CHECK fail that can trigger a denial of service attack. We have patched the issue in GitHub commit f3cf67ac5705f4f04721d15e485e192bb319feed. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

Record
CVE-2022-36005
CVSS 3.1
5.9 MediumCVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H
Weakness
CWE-617
Updated
2026-11-02
EUVDEUVD-2022-6926 5.9

TensorFlow is an open source platform for machine learning. When tf.quantization.fake_quant_with_min_max_vars_gradient receives input min or max that is nonscalar, it gives a CHECK fail that can trigger a denial of service attack. We have patched the issue in GitHub commit f3cf67ac5705f4f04721d15e485e192bb319feed. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

Record
EUVD-2022-6926
CVSS 3.1
5.9 MediumCVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H
Updated
2026-11-02
GitHub AdvisoriesGHSA-r26c-679w-mrjm 5.9

TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsGradient`

Impact

When tf.quantization.fake_quant_with_min_max_vars_gradient receives input min or max that is nonscalar, it gives a CHECK fail that can trigger a denial of service attack.

import tensorflow as tf
import numpy as np 
arg_0=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_1=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_2=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_3=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_4=8
arg_5=False
arg_6=''
tf.quantization.fake_quant_with_min_max_vars_gradient(gradients=arg_0, inputs=arg_1,
min=arg_2, max=arg_3, num_bits=arg_4, narrow_range=arg_5, name=arg_6)

Patches

We have patched the issue in GitHub commit f3cf67ac5705f4f04721d15e485e192bb319feed.

The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, 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

  • 刘力源, Information System & Security and Countermeasures Experiments Center, Beijing Institute of Technology
  • Neophytos Christou, Secure Systems Labs, Brown University
Record
GHSA-r26c-679w-mrjm
CVSS 3.1
5.9 MediumCVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H
Weakness
CWE-617
Updated
2026-11-02
NVD 5.9

TensorFlow is an open source platform for machine learning. When tf.quantization.fake_quant_with_min_max_vars_gradient receives input min or max that is nonscalar, it gives a CHECK fail that can trigger a denial of service attack. We have patched the issue in GitHub commit f3cf67ac5705f4f04721d15e485e192bb319feed. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

Record
CVE-2022-36005
CVSS 3.1
5.9 MediumCVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H
CVSS 3.1
7.5 HighCVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
Weakness
CWE-617
Updated
2026-11-02
OSV 5.9

`CHECK` fail in `FakeQuantWithMinMaxVarsGradient` in TensorFlow

TensorFlow is an open source platform for machine learning. When tf.quantization.fake_quant_with_min_max_vars_gradient receives input min or max that is nonscalar, it gives a CHECK fail that can trigger a denial of service attack. We have patched the issue in GitHub commit f3cf67ac5705f4f04721d15e485e192bb319feed. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

Record
CVE-2022-36005
CVSS 3.1
5.9 MediumCVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H
Weakness
CWE-617
Updated
2026-11-02
OSVGHSA-r26c-679w-mrjm 5.9

TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsGradient`

Impact

When tf.quantization.fake_quant_with_min_max_vars_gradient receives input min or max that is nonscalar, it gives a CHECK fail that can trigger a denial of service attack.

import tensorflow as tf
import numpy as np 
arg_0=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_1=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_2=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_3=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_4=8
arg_5=False
arg_6=''
tf.quantization.fake_quant_with_min_max_vars_gradient(gradients=arg_0, inputs=arg_1,
min=arg_2, max=arg_3, num_bits=arg_4, narrow_range=arg_5, name=arg_6)

Patches

We have patched the issue in GitHub commit f3cf67ac5705f4f04721d15e485e192bb319feed.

The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, 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

  • 刘力源, Information System & Security and Countermeasures Experiments Center, Beijing Institute of Technology
  • Neophytos Christou, Secure Systems Labs, Brown University
Record
GHSA-r26c-679w-mrjm
CVSS 3.1
5.9 MediumCVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H
Weakness
CWE-617
Updated
2026-11-02
Show 5 moreShow less
OSVPYSEC-2026-1031 5.9

TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsGradient`

Impact

When tf.quantization.fake_quant_with_min_max_vars_gradient receives input min or max that is nonscalar, it gives a CHECK fail that can trigger a denial of service attack.

import tensorflow as tf
import numpy as np 
arg_0=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_1=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_2=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_3=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_4=8
arg_5=False
arg_6=''
tf.quantization.fake_quant_with_min_max_vars_gradient(gradients=arg_0, inputs=arg_1,
min=arg_2, max=arg_3, num_bits=arg_4, narrow_range=arg_5, name=arg_6)

Patches

We have patched the issue in GitHub commit f3cf67ac5705f4f04721d15e485e192bb319feed.

The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, 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

  • 刘力源, Information System & Security and Countermeasures Experiments Center, Beijing Institute of Technology
  • Neophytos Christou, Secure Systems Labs, Brown University
Record
PYSEC-2026-1031
CVSS 3.1
5.9 MediumCVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H
Updated
2026-11-02
OSVPYSEC-2026-3234 5.9

TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsGradient`

Impact

When tf.quantization.fake_quant_with_min_max_vars_gradient receives input min or max that is nonscalar, it gives a CHECK fail that can trigger a denial of service attack.

import tensorflow as tf
import numpy as np 
arg_0=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_1=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_2=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_3=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_4=8
arg_5=False
arg_6=''
tf.quantization.fake_quant_with_min_max_vars_gradient(gradients=arg_0, inputs=arg_1,
min=arg_2, max=arg_3, num_bits=arg_4, narrow_range=arg_5, name=arg_6)

Patches

We have patched the issue in GitHub commit f3cf67ac5705f4f04721d15e485e192bb319feed.

The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, 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

  • 刘力源, Information System & Security and Countermeasures Experiments Center, Beijing Institute of Technology
  • Neophytos Christou, Secure Systems Labs, Brown University
Record
PYSEC-2026-3234
CVSS 3.1
5.9 MediumCVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H
Updated
2026-11-02
OSVPYSEC-2026-3364 5.9

TensorFlow vulnerable to `CHECK` fail in `FakeQuantWithMinMaxVarsGradient`

Impact

When tf.quantization.fake_quant_with_min_max_vars_gradient receives input min or max that is nonscalar, it gives a CHECK fail that can trigger a denial of service attack.

import tensorflow as tf
import numpy as np 
arg_0=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_1=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_2=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_3=tf.constant(value=np.random.random(size=(2, 2)), shape=(2, 2), dtype=tf.float32)
arg_4=8
arg_5=False
arg_6=''
tf.quantization.fake_quant_with_min_max_vars_gradient(gradients=arg_0, inputs=arg_1,
min=arg_2, max=arg_3, num_bits=arg_4, narrow_range=arg_5, name=arg_6)

Patches

We have patched the issue in GitHub commit f3cf67ac5705f4f04721d15e485e192bb319feed.

The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, 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

  • 刘力源, Information System & Security and Countermeasures Experiments Center, Beijing Institute of Technology
  • Neophytos Christou, Secure Systems Labs, Brown University
Record
PYSEC-2026-3364
CVSS 3.1
5.9 MediumCVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H
Updated
2026-11-02
OSVBIT-tensorflow-2022-36005 No score

`CHECK` fail in `FakeQuantWithMinMaxVarsGradient` in TensorFlow

TensorFlow is an open source platform for machine learning. When tf.quantization.fake_quant_with_min_max_vars_gradient receives input min or max that is nonscalar, it gives a CHECK fail that can trigger a denial of service attack. We have patched the issue in GitHub commit f3cf67ac5705f4f04721d15e485e192bb319feed. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.

Record
BIT-tensorflow-2022-36005
Updated
2026-11-02
OSVopenSUSE-SU-2024:12355-1 No score

tensorflow-lite-2.10.0-1.1 on GA media

These are all security issues fixed in the tensorflow-lite-2.10.0-1.1 package on the GA media of openSUSE Tumbleweed.

Record
openSUSE-SU-2024:12355-1
Updated
2026-11-02

Affected products

Affected packages

Affected packages
PackageAffectedFixed
tensorflow
PyPI
 before 2.7.2
2.7.2
tensorflow
PyPI
 2.8.0 up to 2.8.1
2.8.1
tensorflow
PyPI
 2.9.0 up to 2.9.1
2.9.1
tensorflow-cpu
PyPI
 before 2.7.2
2.7.2
tensorflow-cpu
PyPI
 2.8.0 up to 2.8.1
2.8.1
tensorflow-cpu
PyPI
 2.9.0 up to 2.9.1
2.9.1
tensorflow-gpu
PyPI
 before 2.7.2
2.7.2
tensorflow-gpu
PyPI
 2.8.0 up to 2.8.1
2.8.1
tensorflow-gpu
PyPI
 2.9.0 up to 2.9.1
2.9.1
tensorflow
Bitnami
 before 2.7.2, 2.8.0 up to 2.8.1, 2.9.0 up to 2.9.1
2.7.22.8.12.9.1
Show 6 moreShow less
Affected packages
PackageAffectedFixed
tensorflow
PyPI
 before 2.7.2, 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
2.7.2
tensorflow-cpu
PyPI
 before 2.7.2, plus listed versions
1.15.02.1.02.1.12.1.22.1.3
Show 27 moreShow less
2.1.42.2.02.2.12.2.22.2.32.3.02.3.12.3.22.3.32.3.42.4.02.4.12.4.22.4.32.4.42.5.02.5.12.5.22.5.32.6.02.6.12.6.22.6.32.6.42.6.52.7.02.7.1
2.7.2
tensorflow-gpu
PyPI
 before 2.7.2, 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
2.7.2
tensorflow-gpu
PyPI
 before 2.7.2, 2.8.0 up to 2.8.1, 2.9.0 up to 2.9.1, 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
2.7.22.8.12.9.1
tensorflow
PyPI
 before 2.7.2, 2.8.0 up to 2.8.1, 2.9.0 up to 2.9.1, 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
2.7.22.8.12.9.1
tensorflow-cpu
PyPI
 before 2.7.2, 2.8.0 up to 2.8.1, 2.9.0 up to 2.9.1, plus listed versions
1.15.02.1.02.1.12.1.22.1.3
Show 27 moreShow less
2.1.42.2.02.2.12.2.22.2.32.3.02.3.12.3.22.3.32.3.42.4.02.4.12.4.22.4.32.4.42.5.02.5.12.5.22.5.32.6.02.6.12.6.22.6.32.6.42.6.52.7.02.7.1
2.7.22.8.12.9.1

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-2022-36005 is a medium vulnerability (CVSS 5.9) in Google Tensorflow, published September 16, 2022. No source lists public exploit code. EPSS puts the chance of exploitation in the next 30 days at 0.48%, the 41st percentile. Fixed in tensorflow 2.7.2.

References

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