CVE-2022-29201

Missing validation in `QuantizedConv2D` results in undefined behavior in TensorFlow

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.raw_ops.QuantizedConv2D does not fully validate the input arguments. In this case, references get bound to nullptr for each argument that is empty. 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

0.34%Down 0.7 points since May 21, 2022

What each source reports

CVE List Score used 5.5

Missing validation in `QuantizedConv2D` results in undefined behavior in TensorFlow

Record: CVE-2022-29201
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, CWE-476
Updated: 2026-11-02

EUVDEUVD-2022-4806 5.5

Record: EUVD-2022-4806
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-pqhm-4wvf-2jg8 5.5

Missing validation results in undefined behavior in `QuantizedConv2D`

Impact

The implementation of tf.raw_ops.QuantizedConv2D does not fully validate the input arguments:

import tensorflow as tf

input = tf.constant(1, shape=[1, 2, 3, 3], dtype=tf.quint8)
filter = tf.constant(1, shape=[1, 2, 3, 3], dtype=tf.quint8)

# bad args
min_input = tf.constant([], shape=[0], dtype=tf.float32)
max_input = tf.constant(0, shape=[], dtype=tf.float32)
min_filter = tf.constant(0, shape=[], dtype=tf.float32)
max_filter = tf.constant(0, shape=[], dtype=tf.float32)

tf.raw_ops.QuantizedConv2D(
  input=input,
  filter=filter,
  min_input=min_input,
  max_input=max_input,
  min_filter=min_filter,
  max_filter=max_filter, 
  strides=[1, 1, 1, 1],
  padding="SAME")

In this case, references get bound to nullptr for each argument that is empty (in the example, all arguments in the bad args section).

Patches

We have patched the issue in GitHub commit 0f0b080ecde4d3dfec158d6f60da34d5e31693c4.

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 by Neophytos Christou from Secure Systems Lab at Brown University.

Record: GHSA-pqhm-4wvf-2jg8
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, CWE-476
Updated: 2026-11-02

NVD 5.5

Record: CVE-2022-29201
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, CWE-476
Updated: 2026-11-02

OSV 5.5

Missing validation in `QuantizedConv2D` results in undefined behavior in TensorFlow

Record: CVE-2022-29201
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, CWE-476
Updated: 2026-11-11

OSVGHSA-pqhm-4wvf-2jg8 5.5

Missing validation results in undefined behavior in `QuantizedConv2D`

Impact

The implementation of tf.raw_ops.QuantizedConv2D does not fully validate the input arguments:

import tensorflow as tf

input = tf.constant(1, shape=[1, 2, 3, 3], dtype=tf.quint8)
filter = tf.constant(1, shape=[1, 2, 3, 3], dtype=tf.quint8)

# bad args
min_input = tf.constant([], shape=[0], dtype=tf.float32)
max_input = tf.constant(0, shape=[], dtype=tf.float32)
min_filter = tf.constant(0, shape=[], dtype=tf.float32)
max_filter = tf.constant(0, shape=[], dtype=tf.float32)

tf.raw_ops.QuantizedConv2D(
  input=input,
  filter=filter,
  min_input=min_input,
  max_input=max_input,
  min_filter=min_filter,
  max_filter=max_filter, 
  strides=[1, 1, 1, 1],
  padding="SAME")

In this case, references get bound to nullptr for each argument that is empty (in the example, all arguments in the bad args section).

Patches

We have patched the issue in GitHub commit 0f0b080ecde4d3dfec158d6f60da34d5e31693c4.

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 by Neophytos Christou from Secure Systems Lab at Brown University.

Record: GHSA-pqhm-4wvf-2jg8
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, CWE-476
Updated: 2026-11-02

Show 4 moreShow less

OSVPYSEC-2026-1026 5.5

Missing validation results in undefined behavior in `QuantizedConv2D`

Impact

The implementation of tf.raw_ops.QuantizedConv2D does not fully validate the input arguments:

import tensorflow as tf

input = tf.constant(1, shape=[1, 2, 3, 3], dtype=tf.quint8)
filter = tf.constant(1, shape=[1, 2, 3, 3], dtype=tf.quint8)

# bad args
min_input = tf.constant([], shape=[0], dtype=tf.float32)
max_input = tf.constant(0, shape=[], dtype=tf.float32)
min_filter = tf.constant(0, shape=[], dtype=tf.float32)
max_filter = tf.constant(0, shape=[], dtype=tf.float32)

tf.raw_ops.QuantizedConv2D(
  input=input,
  filter=filter,
  min_input=min_input,
  max_input=max_input,
  min_filter=min_filter,
  max_filter=max_filter, 
  strides=[1, 1, 1, 1],
  padding="SAME")

In this case, references get bound to nullptr for each argument that is empty (in the example, all arguments in the bad args section).

Patches

We have patched the issue in GitHub commit 0f0b080ecde4d3dfec158d6f60da34d5e31693c4.

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 by Neophytos Christou from Secure Systems Lab at Brown University.

Record: PYSEC-2026-1026
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-2026-3224 5.5

Missing validation results in undefined behavior in `QuantizedConv2D`

Impact

The implementation of tf.raw_ops.QuantizedConv2D does not fully validate the input arguments:

import tensorflow as tf

input = tf.constant(1, shape=[1, 2, 3, 3], dtype=tf.quint8)
filter = tf.constant(1, shape=[1, 2, 3, 3], dtype=tf.quint8)

# bad args
min_input = tf.constant([], shape=[0], dtype=tf.float32)
max_input = tf.constant(0, shape=[], dtype=tf.float32)
min_filter = tf.constant(0, shape=[], dtype=tf.float32)
max_filter = tf.constant(0, shape=[], dtype=tf.float32)

tf.raw_ops.QuantizedConv2D(
  input=input,
  filter=filter,
  min_input=min_input,
  max_input=max_input,
  min_filter=min_filter,
  max_filter=max_filter, 
  strides=[1, 1, 1, 1],
  padding="SAME")

In this case, references get bound to nullptr for each argument that is empty (in the example, all arguments in the bad args section).

Patches

We have patched the issue in GitHub commit 0f0b080ecde4d3dfec158d6f60da34d5e31693c4.

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 by Neophytos Christou from Secure Systems Lab at Brown University.

Record: PYSEC-2026-3224
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-2026-3358 5.5

Missing validation results in undefined behavior in `QuantizedConv2D`

Impact

The implementation of tf.raw_ops.QuantizedConv2D does not fully validate the input arguments:

import tensorflow as tf

input = tf.constant(1, shape=[1, 2, 3, 3], dtype=tf.quint8)
filter = tf.constant(1, shape=[1, 2, 3, 3], dtype=tf.quint8)

# bad args
min_input = tf.constant([], shape=[0], dtype=tf.float32)
max_input = tf.constant(0, shape=[], dtype=tf.float32)
min_filter = tf.constant(0, shape=[], dtype=tf.float32)
max_filter = tf.constant(0, shape=[], dtype=tf.float32)

tf.raw_ops.QuantizedConv2D(
  input=input,
  filter=filter,
  min_input=min_input,
  max_input=max_input,
  min_filter=min_filter,
  max_filter=max_filter, 
  strides=[1, 1, 1, 1],
  padding="SAME")

In this case, references get bound to nullptr for each argument that is empty (in the example, all arguments in the bad args section).

Patches

We have patched the issue in GitHub commit 0f0b080ecde4d3dfec158d6f60da34d5e31693c4.

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 by Neophytos Christou from Secure Systems Lab at Brown University.

Record: PYSEC-2026-3358
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-2022-29201 No score

Missing validation in `QuantizedConv2D` results in undefined behavior in TensorFlow

Record: BIT-tensorflow-2022-29201
Updated: 2026-11-02

Affected products

Affected packages

Affected packages
Package	Affected	Fixed
`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

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Affected packages
Package	Affected	Fixed
`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
Recorded	Changed	Sources
Nov 11, 2026	facts	OSV
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-2022-29201 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 0.34%, the 27th percentile. Fixed in tensorflow 2.6.4.

References

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This page doesn't list everything

Left out here: 60 versions. The JSON record has them.