CVE-2022-35996

Floating point exception in `Conv2D` in TensorFlow

5.9 MediumFix available

TensorFlow is an open source platform for machine learning. If Conv2D is given empty input and the filter and padding sizes are valid, the output is all-zeros. This causes division-by-zero floating point exceptions that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 611d80db29dd7b0cfb755772c69d60ae5bca05f9. 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

What each source reports

CVE List Score used 5.9

Floating point exception in `Conv2D` in TensorFlow

Record: CVE-2022-35996
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-369
Updated: 2026-11-02

EUVDEUVD-2022-6908 5.9

Record: EUVD-2022-6908
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-q5jv-m6qw-5g37 5.9

TensorFlow vulnerable to floating point exception in `Conv2D`

Impact

If Conv2D is given empty input and the filter and padding sizes are valid, the output is all-zeros. This causes division-by-zero floating point exceptions that can be used to trigger a denial of service attack.

import tensorflow as tf
import numpy as np
with tf.device("CPU"): # also can be triggerred on GPU
   input = np.ones([1, 0, 2, 1])
   filter = np.ones([1, 1, 1, 1])
   strides = ([1, 1, 1, 1])
   padding = "EXPLICIT"
   explicit_paddings = [0 , 0, 1, 1, 1, 1, 0, 0]
   data_format = "NHWC"
   res = tf.raw_ops.Conv2D(
       input=input,
       filter=filter,
       strides=strides,
       padding=padding,
        explicit_paddings=explicit_paddings,
       data_format=data_format,
  )

Patches

We have patched the issue in GitHub commit 611d80db29dd7b0cfb755772c69d60ae5bca05f9.

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 Jingyi Shi.

Record: GHSA-q5jv-m6qw-5g37
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-369
Updated: 2026-11-02

NVD 5.9

Record: CVE-2022-35996
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-369
Updated: 2026-11-02

OSV 5.9

Floating point exception in `Conv2D` in TensorFlow

Record: CVE-2022-35996
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-369
Updated: 2026-11-02

OSVGHSA-q5jv-m6qw-5g37 5.9

TensorFlow vulnerable to floating point exception in `Conv2D`

Impact

import tensorflow as tf
import numpy as np
with tf.device("CPU"): # also can be triggerred on GPU
   input = np.ones([1, 0, 2, 1])
   filter = np.ones([1, 1, 1, 1])
   strides = ([1, 1, 1, 1])
   padding = "EXPLICIT"
   explicit_paddings = [0 , 0, 1, 1, 1, 1, 0, 0]
   data_format = "NHWC"
   res = tf.raw_ops.Conv2D(
       input=input,
       filter=filter,
       strides=strides,
       padding=padding,
        explicit_paddings=explicit_paddings,
       data_format=data_format,
  )

Patches

We have patched the issue in GitHub commit 611d80db29dd7b0cfb755772c69d60ae5bca05f9.

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 Jingyi Shi.

Record: GHSA-q5jv-m6qw-5g37
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-369
Updated: 2026-11-02

Show 5 moreShow less

OSVPYSEC-2026-1029 5.9

TensorFlow vulnerable to floating point exception in `Conv2D`

Impact

import tensorflow as tf
import numpy as np
with tf.device("CPU"): # also can be triggerred on GPU
   input = np.ones([1, 0, 2, 1])
   filter = np.ones([1, 1, 1, 1])
   strides = ([1, 1, 1, 1])
   padding = "EXPLICIT"
   explicit_paddings = [0 , 0, 1, 1, 1, 1, 0, 0]
   data_format = "NHWC"
   res = tf.raw_ops.Conv2D(
       input=input,
       filter=filter,
       strides=strides,
       padding=padding,
        explicit_paddings=explicit_paddings,
       data_format=data_format,
  )

Patches

We have patched the issue in GitHub commit 611d80db29dd7b0cfb755772c69d60ae5bca05f9.

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 Jingyi Shi.

Record: PYSEC-2026-1029
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-2026-3228 5.9

TensorFlow vulnerable to floating point exception in `Conv2D`

Impact

import tensorflow as tf
import numpy as np
with tf.device("CPU"): # also can be triggerred on GPU
   input = np.ones([1, 0, 2, 1])
   filter = np.ones([1, 1, 1, 1])
   strides = ([1, 1, 1, 1])
   padding = "EXPLICIT"
   explicit_paddings = [0 , 0, 1, 1, 1, 1, 0, 0]
   data_format = "NHWC"
   res = tf.raw_ops.Conv2D(
       input=input,
       filter=filter,
       strides=strides,
       padding=padding,
        explicit_paddings=explicit_paddings,
       data_format=data_format,
  )

Patches

We have patched the issue in GitHub commit 611d80db29dd7b0cfb755772c69d60ae5bca05f9.

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 Jingyi Shi.

Record: PYSEC-2026-3228
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-2026-3361 5.9

TensorFlow vulnerable to floating point exception in `Conv2D`

Impact

import tensorflow as tf
import numpy as np
with tf.device("CPU"): # also can be triggerred on GPU
   input = np.ones([1, 0, 2, 1])
   filter = np.ones([1, 1, 1, 1])
   strides = ([1, 1, 1, 1])
   padding = "EXPLICIT"
   explicit_paddings = [0 , 0, 1, 1, 1, 1, 0, 0]
   data_format = "NHWC"
   res = tf.raw_ops.Conv2D(
       input=input,
       filter=filter,
       strides=strides,
       padding=padding,
        explicit_paddings=explicit_paddings,
       data_format=data_format,
  )

Patches

We have patched the issue in GitHub commit 611d80db29dd7b0cfb755772c69d60ae5bca05f9.

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 Jingyi Shi.

Record: PYSEC-2026-3361
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-2022-35996 No score

Floating point exception in `Conv2D` in TensorFlow

Record: BIT-tensorflow-2022-35996
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
Package	Affected	Fixed
`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
Package	Affected	Fixed
`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
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-2022-35996 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

Show 63 moreShow less

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Left out here: 82 versions. The JSON record has them.