CVE-2022-41883

Out of bounds segmentation fault due to unequal op inputs in Tensorflow

6.8 MediumFix available

TensorFlow is an open source platform for machine learning. When ops that have specified input sizes receive a differing number of inputs, the executor will crash. We have patched the issue in GitHub commit f5381e0e10b5a61344109c1b7c174c68110f7629. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

Attack profile

How easy it is to exploit

Attack vector: Network
Complexity: Low
Privileges needed: Low
User interaction: Required

What an attacker can affect

Confidentiality: Low
Integrity: Low
Availability: High

Exploit likelihood

0.38%Down 0.6 points since Nov 19, 2022

What each source reports

CVE List Score used 6.8

Out of bounds segmentation fault due to unequal op inputs in Tensorflow

Record: CVE-2022-41883
CVSS 3.1: 6.8 MediumCVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:U/C:L/I:L/A:H
Weakness: CWE-125
Updated: 2026-11-02

EUVDEUVD-2022-7448 6.8

Record: EUVD-2022-7448
CVSS 3.1: 6.8 MediumCVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:U/C:L/I:L/A:H
Updated: 2026-11-02

GitHub AdvisoriesGHSA-w58w-79xv-6vcj 6.8

Out of bounds segmentation fault due to unequal op inputs in Tensorflow

Impact

tf.raw_ops.DynamicStitch specifies input sizes when it is registered.

REGISTER_OP("DynamicStitch")
    .Input("indices: N * int32")
    .Input("data: N * T")
    .Output("merged: T")
    .Attr("N : int >= 1")
    .Attr("T : type")
    .SetShapeFn(DynamicStitchShapeFunction);

When it receives a differing number of inputs, such as when it is called with an indices size 1 and a data size 2, it will crash.

import tensorflow as tf

# indices = 1*[tf.random.uniform([1,2], dtype=tf.dtypes.int32, maxval=100)]
indices = [tf.constant([[0, 1]]),]

# data = 2*[tf.random.uniform([1,2], dtype=tf.dtypes.float32, maxval=100)]
data = [tf.constant([[5, 6]]), tf.constant([[7, 8]])]

tf.raw_ops.DynamicStitch(
    indices=indices, 
    data=data)

Patches

We have patched the issue in GitHub commit f5381e0e10b5a61344109c1b7c174c68110f7629.

The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1 as this is also affected.

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 Zizhuang Deng of IIE, UCAS

Record: GHSA-w58w-79xv-6vcj
CVSS 3.1: 6.8 MediumCVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:U/C:L/I:L/A:H
Weakness: CWE-125
Updated: 2026-11-02

NVD 6.8

Record: CVE-2022-41883
CVSS 3.1: 6.8 MediumCVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:U/C:L/I:L/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-125
Updated: 2026-11-02

OSV 6.8

Out of bounds segmentation fault due to unequal op inputs in Tensorflow

Record: CVE-2022-41883
CVSS 3.1: 6.8 MediumCVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:U/C:L/I:L/A:H
Weakness: CWE-125
Updated: 2026-11-02

OSVGHSA-w58w-79xv-6vcj 6.8

Out of bounds segmentation fault due to unequal op inputs in Tensorflow

Impact

tf.raw_ops.DynamicStitch specifies input sizes when it is registered.

REGISTER_OP("DynamicStitch")
    .Input("indices: N * int32")
    .Input("data: N * T")
    .Output("merged: T")
    .Attr("N : int >= 1")
    .Attr("T : type")
    .SetShapeFn(DynamicStitchShapeFunction);

When it receives a differing number of inputs, such as when it is called with an indices size 1 and a data size 2, it will crash.

import tensorflow as tf

# indices = 1*[tf.random.uniform([1,2], dtype=tf.dtypes.int32, maxval=100)]
indices = [tf.constant([[0, 1]]),]

# data = 2*[tf.random.uniform([1,2], dtype=tf.dtypes.float32, maxval=100)]
data = [tf.constant([[5, 6]]), tf.constant([[7, 8]])]

tf.raw_ops.DynamicStitch(
    indices=indices, 
    data=data)

Patches

We have patched the issue in GitHub commit f5381e0e10b5a61344109c1b7c174c68110f7629.

The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1 as this is also affected.

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 Zizhuang Deng of IIE, UCAS

Record: GHSA-w58w-79xv-6vcj
CVSS 3.1: 6.8 MediumCVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:U/C:L/I:L/A:H
Weakness: CWE-125
Updated: 2026-11-02

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OSVPYSEC-2026-1042 6.8

Out of bounds segmentation fault due to unequal op inputs in Tensorflow

Impact

tf.raw_ops.DynamicStitch specifies input sizes when it is registered.

REGISTER_OP("DynamicStitch")
    .Input("indices: N * int32")
    .Input("data: N * T")
    .Output("merged: T")
    .Attr("N : int >= 1")
    .Attr("T : type")
    .SetShapeFn(DynamicStitchShapeFunction);

When it receives a differing number of inputs, such as when it is called with an indices size 1 and a data size 2, it will crash.

import tensorflow as tf

# indices = 1*[tf.random.uniform([1,2], dtype=tf.dtypes.int32, maxval=100)]
indices = [tf.constant([[0, 1]]),]

# data = 2*[tf.random.uniform([1,2], dtype=tf.dtypes.float32, maxval=100)]
data = [tf.constant([[5, 6]]), tf.constant([[7, 8]])]

tf.raw_ops.DynamicStitch(
    indices=indices, 
    data=data)

Patches

We have patched the issue in GitHub commit f5381e0e10b5a61344109c1b7c174c68110f7629.

The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1 as this is also affected.

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 Zizhuang Deng of IIE, UCAS

Record: PYSEC-2026-1042
CVSS 3.1: 6.8 MediumCVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:U/C:L/I:L/A:H
Updated: 2026-11-02

OSVPYSEC-2026-3251 6.8

Out of bounds segmentation fault due to unequal op inputs in Tensorflow

Impact

tf.raw_ops.DynamicStitch specifies input sizes when it is registered.

REGISTER_OP("DynamicStitch")
    .Input("indices: N * int32")
    .Input("data: N * T")
    .Output("merged: T")
    .Attr("N : int >= 1")
    .Attr("T : type")
    .SetShapeFn(DynamicStitchShapeFunction);

When it receives a differing number of inputs, such as when it is called with an indices size 1 and a data size 2, it will crash.

import tensorflow as tf

# indices = 1*[tf.random.uniform([1,2], dtype=tf.dtypes.int32, maxval=100)]
indices = [tf.constant([[0, 1]]),]

# data = 2*[tf.random.uniform([1,2], dtype=tf.dtypes.float32, maxval=100)]
data = [tf.constant([[5, 6]]), tf.constant([[7, 8]])]

tf.raw_ops.DynamicStitch(
    indices=indices, 
    data=data)

Patches

We have patched the issue in GitHub commit f5381e0e10b5a61344109c1b7c174c68110f7629.

The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1 as this is also affected.

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 Zizhuang Deng of IIE, UCAS

Record: PYSEC-2026-3251
CVSS 3.1: 6.8 MediumCVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:U/C:L/I:L/A:H
Updated: 2026-11-02

OSVPYSEC-2026-3376 6.8

Out of bounds segmentation fault due to unequal op inputs in Tensorflow

Impact

tf.raw_ops.DynamicStitch specifies input sizes when it is registered.

REGISTER_OP("DynamicStitch")
    .Input("indices: N * int32")
    .Input("data: N * T")
    .Output("merged: T")
    .Attr("N : int >= 1")
    .Attr("T : type")
    .SetShapeFn(DynamicStitchShapeFunction);

When it receives a differing number of inputs, such as when it is called with an indices size 1 and a data size 2, it will crash.

import tensorflow as tf

# indices = 1*[tf.random.uniform([1,2], dtype=tf.dtypes.int32, maxval=100)]
indices = [tf.constant([[0, 1]]),]

# data = 2*[tf.random.uniform([1,2], dtype=tf.dtypes.float32, maxval=100)]
data = [tf.constant([[5, 6]]), tf.constant([[7, 8]])]

tf.raw_ops.DynamicStitch(
    indices=indices, 
    data=data)

Patches

We have patched the issue in GitHub commit f5381e0e10b5a61344109c1b7c174c68110f7629.

The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1 as this is also affected.

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 Zizhuang Deng of IIE, UCAS

Record: PYSEC-2026-3376
CVSS 3.1: 6.8 MediumCVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:U/C:L/I:L/A:H
Updated: 2026-11-02

OSVBIT-tensorflow-2022-41883 No score

Out of bounds segmentation fault due to unequal op inputs in Tensorflow

Record: BIT-tensorflow-2022-41883
Updated: 2026-11-02

Affected products

Affected packages

Affected packages
Package	Affected	Fixed
`tensorflow` PyPI	2.10.0 to 2.10.0	2.10.1
`tensorflow-cpu` PyPI	2.10.0 to 2.10.0	2.10.1
`tensorflow-gpu` PyPI	2.10.0 to 2.10.0	2.10.1
`tensorflow` Bitnami	2.10.0 up to 2.10.1	2.10.1
`tensorflow` PyPI	2.10.0 up to 2.10.1	2.10.1
`tensorflow-cpu` PyPI	2.10.0 up to 2.10.1	2.10.1
`tensorflow-gpu` PyPI	2.10.0 up to 2.10.1	2.10.1

History

History
Recorded	Changed	Sources
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-2022-41883 is a medium vulnerability (CVSS 6.8) in Google Tensorflow, published November 18, 2022. No source lists public exploit code. EPSS puts the chance of exploitation in the next 30 days at 0.38%, the 32nd percentile. Fixed in tensorflow 2.10.1.

References

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