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CVE-2022-29197

Missing validation causes denial of service in TensorFlow via `UnsortedSegmentJoin`

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.UnsortedSegmentJoin does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack. The code assumes num_segments is a scalar but there is no validation for this before accessing its value. 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

0.33%Down 0.7 points since May 21, 2022
90%50%10%1%

What each source reports

CVE List Score used 5.5

Missing validation causes denial of service in TensorFlow via `UnsortedSegmentJoin`

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.UnsortedSegmentJoin does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack. The code assumes num_segments is a scalar but there is no validation for this before accessing its value. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

Record
CVE-2022-29197
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
Updated
2026-11-02
EUVDEUVD-2022-4208 5.5

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.UnsortedSegmentJoin does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack. The code assumes num_segments is a scalar but there is no validation for this before accessing its value. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

Record
EUVD-2022-4208
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-hrg5-737c-2p56 5.5

Missing validation causes denial of service via `UnsortedSegmentJoin`

Impact

The implementation of tf.raw_ops.UnsortedSegmentJoin does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack:

import tensorflow as tf

tf.raw_ops.UnsortedSegmentJoin(
  inputs=tf.constant("this", shape=[12], dtype=tf.string),
  segment_ids=tf.constant(0, shape=[12], dtype=tf.int64),
  num_segments=tf.constant(0, shape=[12], dtype=tf.int64))

The code assumes num_segments is a scalar but there is no validation for this before accessing its value:

const Tensor& num_segments_tensor = context->input(2);
OP_REQUIRES(context, num_segments_tensor.NumElements() != 0,
            errors::InvalidArgument("Number of segments cannot be empty."));
auto num_segments = num_segments_tensor.scalar<NUM_SEGMENTS_TYPE>()();

Patches

We have patched the issue in GitHub commit 13d38a07ce9143e044aa737cfd7bb759d0e9b400.

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-hrg5-737c-2p56
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
Updated
2026-11-02
NVD 5.5

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.UnsortedSegmentJoin does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack. The code assumes num_segments is a scalar but there is no validation for this before accessing its value. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

Record
CVE-2022-29197
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
Updated
2026-11-02
OSV 5.5

Missing validation causes denial of service in TensorFlow via `UnsortedSegmentJoin`

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.UnsortedSegmentJoin does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack. The code assumes num_segments is a scalar but there is no validation for this before accessing its value. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

Record
CVE-2022-29197
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
Updated
2026-11-02
OSVGHSA-hrg5-737c-2p56 5.5

Missing validation causes denial of service via `UnsortedSegmentJoin`

Impact

The implementation of tf.raw_ops.UnsortedSegmentJoin does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack:

import tensorflow as tf

tf.raw_ops.UnsortedSegmentJoin(
  inputs=tf.constant("this", shape=[12], dtype=tf.string),
  segment_ids=tf.constant(0, shape=[12], dtype=tf.int64),
  num_segments=tf.constant(0, shape=[12], dtype=tf.int64))

The code assumes num_segments is a scalar but there is no validation for this before accessing its value:

const Tensor& num_segments_tensor = context->input(2);
OP_REQUIRES(context, num_segments_tensor.NumElements() != 0,
            errors::InvalidArgument("Number of segments cannot be empty."));
auto num_segments = num_segments_tensor.scalar<NUM_SEGMENTS_TYPE>()();

Patches

We have patched the issue in GitHub commit 13d38a07ce9143e044aa737cfd7bb759d0e9b400.

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-hrg5-737c-2p56
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
Updated
2026-11-02
Show 4 moreShow less
OSVPYSEC-2026-1009 5.5

Missing validation causes denial of service via `UnsortedSegmentJoin`

Impact

The implementation of tf.raw_ops.UnsortedSegmentJoin does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack:

import tensorflow as tf

tf.raw_ops.UnsortedSegmentJoin(
  inputs=tf.constant("this", shape=[12], dtype=tf.string),
  segment_ids=tf.constant(0, shape=[12], dtype=tf.int64),
  num_segments=tf.constant(0, shape=[12], dtype=tf.int64))

The code assumes num_segments is a scalar but there is no validation for this before accessing its value:

const Tensor& num_segments_tensor = context->input(2);
OP_REQUIRES(context, num_segments_tensor.NumElements() != 0,
            errors::InvalidArgument("Number of segments cannot be empty."));
auto num_segments = num_segments_tensor.scalar<NUM_SEGMENTS_TYPE>()();

Patches

We have patched the issue in GitHub commit 13d38a07ce9143e044aa737cfd7bb759d0e9b400.

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-2026-1009
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-2026-3202 5.5

Missing validation causes denial of service via `UnsortedSegmentJoin`

Impact

The implementation of tf.raw_ops.UnsortedSegmentJoin does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack:

import tensorflow as tf

tf.raw_ops.UnsortedSegmentJoin(
  inputs=tf.constant("this", shape=[12], dtype=tf.string),
  segment_ids=tf.constant(0, shape=[12], dtype=tf.int64),
  num_segments=tf.constant(0, shape=[12], dtype=tf.int64))

The code assumes num_segments is a scalar but there is no validation for this before accessing its value:

const Tensor& num_segments_tensor = context->input(2);
OP_REQUIRES(context, num_segments_tensor.NumElements() != 0,
            errors::InvalidArgument("Number of segments cannot be empty."));
auto num_segments = num_segments_tensor.scalar<NUM_SEGMENTS_TYPE>()();

Patches

We have patched the issue in GitHub commit 13d38a07ce9143e044aa737cfd7bb759d0e9b400.

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-2026-3202
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-2026-3341 5.5

Missing validation causes denial of service via `UnsortedSegmentJoin`

Impact

The implementation of tf.raw_ops.UnsortedSegmentJoin does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack:

import tensorflow as tf

tf.raw_ops.UnsortedSegmentJoin(
  inputs=tf.constant("this", shape=[12], dtype=tf.string),
  segment_ids=tf.constant(0, shape=[12], dtype=tf.int64),
  num_segments=tf.constant(0, shape=[12], dtype=tf.int64))

The code assumes num_segments is a scalar but there is no validation for this before accessing its value:

const Tensor& num_segments_tensor = context->input(2);
OP_REQUIRES(context, num_segments_tensor.NumElements() != 0,
            errors::InvalidArgument("Number of segments cannot be empty."));
auto num_segments = num_segments_tensor.scalar<NUM_SEGMENTS_TYPE>()();

Patches

We have patched the issue in GitHub commit 13d38a07ce9143e044aa737cfd7bb759d0e9b400.

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-2026-3341
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-2022-29197 No score

Missing validation causes denial of service in TensorFlow via `UnsortedSegmentJoin`

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.UnsortedSegmentJoin does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack. The code assumes num_segments is a scalar but there is no validation for this before accessing its value. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

Record
BIT-tensorflow-2022-29197
Updated
2026-11-02

Affected products

Affected packages

Affected packages
PackageAffectedFixed
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
2.6.42.7.2
Show 9 moreShow less
Affected packages
PackageAffectedFixed
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
RecordedChangedSources
first indexedCVE List, EUVD, GitHub Advisories, NVD, OSV

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

In short

CVE-2022-29197 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 0.33%, the 25th percentile. Fixed in tensorflow 2.6.4.

References

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