CVE-2022-35967

Segfault in `QuantizedAdd` in TensorFlow

5.9 MediumFix available

TensorFlow is an open source platform for machine learning. If QuantizedAdd is given min_input or max_input tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 49b3824d83af706df0ad07e4e677d88659756d89. 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

0.49%Down 0.4 points since Sep 17, 2022

What each source reports

CVE List Score used 5.9

Segfault in `QuantizedAdd` in TensorFlow

Record: CVE-2022-35967
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-20
Updated: 2026-11-02

EUVDEUVD-2022-6943 5.9

Record: EUVD-2022-6943
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-v6h3-348g-6h5x 5.9

TensorFlow vulnerable to segfault in `QuantizedAdd`

Impact

If QuantizedAdd is given min_input or max_input tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack.

import tensorflow as tf

Toutput = tf.qint32
x = tf.constant(140, shape=[1], dtype=tf.quint8)
y = tf.constant(26, shape=[10], dtype=tf.quint8)
min_x = tf.constant([], shape=[0], dtype=tf.float32)
max_x = tf.constant(0, shape=[], dtype=tf.float32)
min_y = tf.constant(0, shape=[], dtype=tf.float32)
max_y = tf.constant(0, shape=[], dtype=tf.float32)
tf.raw_ops.QuantizedAdd(x=x, y=y, min_x=min_x, max_x=max_x, min_y=min_y, max_y=max_y, Toutput=Toutput)

Patches

We have patched the issue in GitHub commit 49b3824d83af706df0ad07e4e677d88659756d89.

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 Neophytos Christou, Secure Systems Labs, Brown University.

Record: GHSA-v6h3-348g-6h5x
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-20
Updated: 2026-11-02

NVD 5.9

Record: CVE-2022-35967
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-20
Updated: 2026-11-02

OSV 5.9

Segfault in `QuantizedAdd` in TensorFlow

Record: CVE-2022-35967
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-20
Updated: 2026-11-02

OSVGHSA-v6h3-348g-6h5x 5.9

TensorFlow vulnerable to segfault in `QuantizedAdd`

Impact

If QuantizedAdd is given min_input or max_input tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack.

import tensorflow as tf

Toutput = tf.qint32
x = tf.constant(140, shape=[1], dtype=tf.quint8)
y = tf.constant(26, shape=[10], dtype=tf.quint8)
min_x = tf.constant([], shape=[0], dtype=tf.float32)
max_x = tf.constant(0, shape=[], dtype=tf.float32)
min_y = tf.constant(0, shape=[], dtype=tf.float32)
max_y = tf.constant(0, shape=[], dtype=tf.float32)
tf.raw_ops.QuantizedAdd(x=x, y=y, min_x=min_x, max_x=max_x, min_y=min_y, max_y=max_y, Toutput=Toutput)

Patches

We have patched the issue in GitHub commit 49b3824d83af706df0ad07e4e677d88659756d89.

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 Neophytos Christou, Secure Systems Labs, Brown University.

Record: GHSA-v6h3-348g-6h5x
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-20
Updated: 2026-11-02

Show 5 moreShow less

OSVPYSEC-2026-1037 5.9

TensorFlow vulnerable to segfault in `QuantizedAdd`

Impact

If QuantizedAdd is given min_input or max_input tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack.

import tensorflow as tf

Toutput = tf.qint32
x = tf.constant(140, shape=[1], dtype=tf.quint8)
y = tf.constant(26, shape=[10], dtype=tf.quint8)
min_x = tf.constant([], shape=[0], dtype=tf.float32)
max_x = tf.constant(0, shape=[], dtype=tf.float32)
min_y = tf.constant(0, shape=[], dtype=tf.float32)
max_y = tf.constant(0, shape=[], dtype=tf.float32)
tf.raw_ops.QuantizedAdd(x=x, y=y, min_x=min_x, max_x=max_x, min_y=min_y, max_y=max_y, Toutput=Toutput)

Patches

We have patched the issue in GitHub commit 49b3824d83af706df0ad07e4e677d88659756d89.

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 Neophytos Christou, Secure Systems Labs, Brown University.

Record: PYSEC-2026-1037
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-2026-3244 5.9

TensorFlow vulnerable to segfault in `QuantizedAdd`

Impact

If QuantizedAdd is given min_input or max_input tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack.

import tensorflow as tf

Toutput = tf.qint32
x = tf.constant(140, shape=[1], dtype=tf.quint8)
y = tf.constant(26, shape=[10], dtype=tf.quint8)
min_x = tf.constant([], shape=[0], dtype=tf.float32)
max_x = tf.constant(0, shape=[], dtype=tf.float32)
min_y = tf.constant(0, shape=[], dtype=tf.float32)
max_y = tf.constant(0, shape=[], dtype=tf.float32)
tf.raw_ops.QuantizedAdd(x=x, y=y, min_x=min_x, max_x=max_x, min_y=min_y, max_y=max_y, Toutput=Toutput)

Patches

We have patched the issue in GitHub commit 49b3824d83af706df0ad07e4e677d88659756d89.

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 Neophytos Christou, Secure Systems Labs, Brown University.

Record: PYSEC-2026-3244
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-2026-3371 5.9

TensorFlow vulnerable to segfault in `QuantizedAdd`

Impact

If QuantizedAdd is given min_input or max_input tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack.

import tensorflow as tf

Toutput = tf.qint32
x = tf.constant(140, shape=[1], dtype=tf.quint8)
y = tf.constant(26, shape=[10], dtype=tf.quint8)
min_x = tf.constant([], shape=[0], dtype=tf.float32)
max_x = tf.constant(0, shape=[], dtype=tf.float32)
min_y = tf.constant(0, shape=[], dtype=tf.float32)
max_y = tf.constant(0, shape=[], dtype=tf.float32)
tf.raw_ops.QuantizedAdd(x=x, y=y, min_x=min_x, max_x=max_x, min_y=min_y, max_y=max_y, Toutput=Toutput)

Patches

We have patched the issue in GitHub commit 49b3824d83af706df0ad07e4e677d88659756d89.

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 Neophytos Christou, Secure Systems Labs, Brown University.

Record: PYSEC-2026-3371
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-2022-35967 No score

Segfault in `QuantizedAdd` in TensorFlow

Record: BIT-tensorflow-2022-35967
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	2.7.0 up to 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-2022-35967 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 0.49%, the 42nd 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.