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CVE-2020-26267

Lack of validation in data format attributes in TensorFlow

4.4 MediumFix available

In affected versions of TensorFlow the tf.raw_ops.DataFormatVecPermute API does not validate the src_format and dst_format attributes. The code assumes that these two arguments define a permutation of NHWC. This can result in uninitialized memory accesses, read outside of bounds and even crashes. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.

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
Low
Availability
Low

Exploit likelihood

0.24%Down 0.4 points since Apr 14, 2021
90%50%10%1%

What each source reports

CVE List Score used 4.4

Lack of validation in data format attributes in TensorFlow

In affected versions of TensorFlow the tf.raw_ops.DataFormatVecPermute API does not validate the src_format and dst_format attributes. The code assumes that these two arguments define a permutation of NHWC. This can result in uninitialized memory accesses, read outside of bounds and even crashes. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.

Record
CVE-2020-26267
CVSS 3.1
4.4 MediumCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L
Weakness
CWE-125
Updated
2026-11-02
EUVDEUVD-2020-0214 4.4

In affected versions of TensorFlow the tf.raw_ops.DataFormatVecPermute API does not validate the src_format and dst_format attributes. The code assumes that these two arguments define a permutation of NHWC. This can result in uninitialized memory accesses, read outside of bounds and even crashes. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.

Record
EUVD-2020-0214
CVSS 3.1
4.4 MediumCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L
Updated
2026-11-03
GitHub AdvisoriesGHSA-c9f3-9wfr-wgh7 2.1

Lack of validation in data format attributes in TensorFlow

Impact

The tf.raw_ops.DataFormatVecPermute API does not validate the src_format and dst_format attributes. The code assumes that these two arguments define a permutation of NHWC.

However, these assumptions are not checked and this can result in uninitialized memory accesses, read outside of bounds and even crashes.

>>> import tensorflow as tf
>>> tf.raw_ops.DataFormatVecPermute(x=[1,4], src_format='1234', dst_format='1234')
<tf.Tensor: shape=(2,), dtype=int32, numpy=array([4, 757100143], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,4], src_format='HHHH', dst_format='WWWW')
<tf.Tensor: shape=(2,), dtype=int32, numpy=array([4, 32701], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,4], src_format='H', dst_format='W')
<tf.Tensor: shape=(2,), dtype=int32, numpy=array([4, 32701], dtype=int32)>
>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4], 
                                    src_format='1234', dst_format='1253')
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([4, 2, 939037184, 3], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4],
                                    src_format='1234', dst_format='1223')
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([4, 32701, 2, 3], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4],
                                    src_format='1224', dst_format='1423')
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([1, 4, 3, 32701], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4], src_format='1234', dst_format='432')
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([4, 3, 2, 32701], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4],
                                    src_format='12345678', dst_format='87654321')
munmap_chunk(): invalid pointer
Aborted
...
>>> tf.raw_ops.DataFormatVecPermute(x=[[1,5],[2,6],[3,7],[4,8]],           
                                    src_format='12345678', dst_format='87654321')
<tf.Tensor: shape=(4, 2), dtype=int32, numpy=
array([[71364624,        0],
       [71365824,        0],
       [     560,        0],
       [      48,        0]], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[[1,5],[2,6],[3,7],[4,8]], 
                                    src_format='12345678', dst_format='87654321')
free(): invalid next size (fast)
Aborted

A similar issue occurs in tf.raw_ops.DataFormatDimMap, for the same reasons:

>>> tf.raw_ops.DataFormatDimMap(x=[[1,5],[2,6],[3,7],[4,8]], src_format='1234',
>>> dst_format='8765')
<tf.Tensor: shape=(4, 2), dtype=int32, numpy=
array([[1954047348, 1954047348],
       [1852793646, 1852793646],
       [1954047348, 1954047348],
       [1852793632, 1852793632]], dtype=int32)>

Patches

We have patched the issue in GitHub commit ebc70b7a592420d3d2f359e4b1694c236b82c7ae and will release TensorFlow 2.4.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.

Since this issue also impacts TF versions before 2.4, we will patch all releases between 1.15 and 2.3 inclusive.

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 members of the Aivul Team from Qihoo 360.

Record
GHSA-c9f3-9wfr-wgh7
CVSS 4.0
2.1 LowCVSS:4.0/AV:L/AC:L/AT:P/PR:N/UI:N/VC:N/VI:L/VA:L/SC:N/SI:N/SA:N
CVSS 3.1
4.4 MediumCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L
Weakness
CWE-125
Updated
2026-11-02
NVD 4.4

In affected versions of TensorFlow the tf.raw_ops.DataFormatVecPermute API does not validate the src_format and dst_format attributes. The code assumes that these two arguments define a permutation of NHWC. This can result in uninitialized memory accesses, read outside of bounds and even crashes. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.

Record
CVE-2020-26267
CVSS 3.1
4.4 MediumCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L
CVSS 3.1
7.8 HighCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
CVSS 2.0
4.3 MediumAV:L/AC:L/Au:S/C:P/I:P/A:P
Weakness
CWE-125
Updated
2026-11-02
OSV 7.8

In affected versions of TensorFlow the tf.raw_ops.DataFormatVecPermute API does not validate the src_format and dst_format attributes. The code assumes that these two arguments define a permutation of NHWC. This can result in uninitialized memory accesses, read outside of bounds and even crashes. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.

Record
CVE-2020-26267
CVSS 3.1
7.8 HighCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
Updated
2026-11-02
OSVGHSA-c9f3-9wfr-wgh7 2.1

Lack of validation in data format attributes in TensorFlow

Impact

The tf.raw_ops.DataFormatVecPermute API does not validate the src_format and dst_format attributes. The code assumes that these two arguments define a permutation of NHWC.

However, these assumptions are not checked and this can result in uninitialized memory accesses, read outside of bounds and even crashes.

>>> import tensorflow as tf
>>> tf.raw_ops.DataFormatVecPermute(x=[1,4], src_format='1234', dst_format='1234')
<tf.Tensor: shape=(2,), dtype=int32, numpy=array([4, 757100143], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,4], src_format='HHHH', dst_format='WWWW')
<tf.Tensor: shape=(2,), dtype=int32, numpy=array([4, 32701], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,4], src_format='H', dst_format='W')
<tf.Tensor: shape=(2,), dtype=int32, numpy=array([4, 32701], dtype=int32)>
>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4], 
                                    src_format='1234', dst_format='1253')
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([4, 2, 939037184, 3], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4],
                                    src_format='1234', dst_format='1223')
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([4, 32701, 2, 3], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4],
                                    src_format='1224', dst_format='1423')
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([1, 4, 3, 32701], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4], src_format='1234', dst_format='432')
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([4, 3, 2, 32701], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4],
                                    src_format='12345678', dst_format='87654321')
munmap_chunk(): invalid pointer
Aborted
...
>>> tf.raw_ops.DataFormatVecPermute(x=[[1,5],[2,6],[3,7],[4,8]],           
                                    src_format='12345678', dst_format='87654321')
<tf.Tensor: shape=(4, 2), dtype=int32, numpy=
array([[71364624,        0],
       [71365824,        0],
       [     560,        0],
       [      48,        0]], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[[1,5],[2,6],[3,7],[4,8]], 
                                    src_format='12345678', dst_format='87654321')
free(): invalid next size (fast)
Aborted

A similar issue occurs in tf.raw_ops.DataFormatDimMap, for the same reasons:

>>> tf.raw_ops.DataFormatDimMap(x=[[1,5],[2,6],[3,7],[4,8]], src_format='1234',
>>> dst_format='8765')
<tf.Tensor: shape=(4, 2), dtype=int32, numpy=
array([[1954047348, 1954047348],
       [1852793646, 1852793646],
       [1954047348, 1954047348],
       [1852793632, 1852793632]], dtype=int32)>

Patches

We have patched the issue in GitHub commit ebc70b7a592420d3d2f359e4b1694c236b82c7ae and will release TensorFlow 2.4.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.

Since this issue also impacts TF versions before 2.4, we will patch all releases between 1.15 and 2.3 inclusive.

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 members of the Aivul Team from Qihoo 360.

Record
GHSA-c9f3-9wfr-wgh7
CVSS 4.0
2.1 LowCVSS:4.0/AV:L/AC:L/AT:P/PR:N/UI:N/VC:N/VI:L/VA:L/SC:N/SI:N/SA:N
CVSS 3.1
4.4 MediumCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L
Weakness
CWE-125
Updated
2026-11-02
Show 5 moreShow less
OSVBIT-tensorflow-2020-26267 No score

Lack of validation in data format attributes in TensorFlow

In affected versions of TensorFlow the tf.raw_ops.DataFormatVecPermute API does not validate the src_format and dst_format attributes. The code assumes that these two arguments define a permutation of NHWC. This can result in uninitialized memory accesses, read outside of bounds and even crashes. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.

Record
BIT-tensorflow-2020-26267
Updated
2026-11-02
OSVopenSUSE-SU-2022:10014-1 No score

Security update for tensorflow2

This update for tensorflow fixes the following issues:

Update to TF2 2.6.0 which fixes multiple CVEs (boo#1189423).

  • Introduction of bazel6.3 and basel-skylib1.0.3 as build dependencies. The latter has been adapted to all a version in its package name (if %set_ver_suffix is set to 1). This allows multiple versions to exist for one product (not installed). NOTE: basel-skylib1.0.3 does not exist in oS:Factory: basel-skylib in oS:Factory - the base version - is 1.0.3.
Record
openSUSE-SU-2022:10014-1
Updated
2026-11-02
OSVPYSEC-2020-140 No score

In affected versions of TensorFlow the tf.raw_ops.DataFormatVecPermute API does not validate the src_format and dst_format attributes. The code assumes that these two arguments define a permutation of NHWC. This can result in uninitialized memory accesses, read outside of bounds and even crashes. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.

Record
PYSEC-2020-140
Updated
2026-11-02
OSVPYSEC-2020-298 No score

In affected versions of TensorFlow the tf.raw_ops.DataFormatVecPermute API does not validate the src_format and dst_format attributes. The code assumes that these two arguments define a permutation of NHWC. This can result in uninitialized memory accesses, read outside of bounds and even crashes. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.

Record
PYSEC-2020-298
Updated
2026-11-02
OSVPYSEC-2020-333 No score

In affected versions of TensorFlow the tf.raw_ops.DataFormatVecPermute API does not validate the src_format and dst_format attributes. The code assumes that these two arguments define a permutation of NHWC. This can result in uninitialized memory accesses, read outside of bounds and even crashes. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.

Record
PYSEC-2020-333
Updated
2026-11-02

Affected products

Affected packages

Affected packages
PackageAffectedFixed
tensorflow
PyPI
 before 1.15.5
1.15.5
tensorflow
PyPI
 2.0.0 up to 2.0.4
2.0.4
tensorflow
PyPI
 2.1.0 up to 2.1.3
2.1.3
tensorflow
PyPI
 2.2.0 up to 2.2.2
2.2.2
tensorflow
PyPI
 2.3.0 up to 2.3.2
2.3.2
tensorflow-cpu
PyPI
 before 1.15.5
1.15.5
tensorflow-cpu
PyPI
 2.0.0 up to 2.0.4
2.0.4
tensorflow-cpu
PyPI
 2.1.0 up to 2.1.3
2.1.3
tensorflow-cpu
PyPI
 2.2.0 up to 2.2.2
2.2.2
tensorflow-cpu
PyPI
 2.3.0 up to 2.3.2
2.3.2
Show 35 moreShow less
Affected packages
PackageAffectedFixed
tensorflow-gpu
PyPI
 before 1.15.5
1.15.5
tensorflow-gpu
PyPI
 2.0.0 up to 2.0.4
2.0.4
tensorflow-gpu
PyPI
 2.1.0 up to 2.1.3
2.1.3
tensorflow-gpu
PyPI
 2.2.0 up to 2.2.2
2.2.2
tensorflow-gpu
PyPI
 2.3.0 up to 2.3.2
2.3.2
tensorflow
Bitnami
 before 1.15.5, 2.0.0 up to 2.0.4, 2.1.0 up to 2.1.3, 2.2.0 up to 2.2.2, 2.3.0 up to 2.3.2
1.15.52.0.42.1.32.2.22.3.2
tensorflow
PyPI
 before 1.15.5, plus listed versions
0.12.00.12.11.0.01.0.11.1.0
Show 25 moreShow less
1.10.01.10.11.11.01.12.01.12.21.12.31.13.11.13.21.14.01.15.01.15.21.15.31.15.41.2.01.2.11.3.01.4.01.4.11.5.01.5.11.6.01.7.01.7.11.8.01.9.0
1.15.5
tensorflow
PyPI
 2.0.0 up to 2.0.4, plus listed versions
2.0.12.0.22.0.3
2.0.4
tensorflow
PyPI
 2.1.0 up to 2.1.3, plus listed versions
2.1.12.1.2
2.1.3
tensorflow
PyPI
 2.2.0 up to 2.2.2, plus listed versions
2.2.1
2.2.2
tensorflow
PyPI
 2.3.0 up to 2.3.2, plus listed versions
2.3.1
2.3.2
tensorflow-cpu
PyPI
 before 1.15.5, plus listed versions
1.15.0
1.15.5
tensorflow-cpu
PyPI
 2.1.0 up to 2.1.3, plus listed versions
2.1.12.1.2
2.1.3
tensorflow-cpu
PyPI
 2.2.0 up to 2.2.2, plus listed versions
2.2.1
2.2.2
tensorflow-cpu
PyPI
 2.3.0 up to 2.3.2, plus listed versions
2.3.1
2.3.2
tensorflow-gpu
PyPI
 before 1.15.5, plus listed versions
0.12.00.12.11.0.01.0.11.1.0
Show 25 moreShow less
1.10.01.10.11.11.01.12.01.12.21.12.31.13.11.13.21.14.01.15.01.15.21.15.31.15.41.2.01.2.11.3.01.4.01.4.11.5.01.5.11.6.01.7.01.7.11.8.01.9.0
1.15.5
tensorflow-gpu
PyPI
 2.0.0 up to 2.0.4, plus listed versions
2.0.12.0.22.0.3
2.0.4
tensorflow-gpu
PyPI
 2.1.0 up to 2.1.3, plus listed versions
2.1.12.1.2
2.1.3
tensorflow-gpu
PyPI
 2.2.0 up to 2.2.2, plus listed versions
2.2.1
2.2.2
tensorflow-gpu
PyPI
 2.3.0 up to 2.3.2, plus listed versions
2.3.1
2.3.2
tensorflow
PyPI
 before 1.15.5, 2.0.0 up to 2.0.4, 2.1.0 up to 2.1.3, 2.2.0 up to 2.2.2, 2.3.0 up to 2.3.2, plus listed versions
0.12.0rc00.12.0rc10.12.00.12.11.0.0
Show 45 moreShow less
1.0.11.1.0rc01.1.0rc11.1.0rc21.1.01.2.0rc01.2.0rc11.2.0rc21.2.01.2.11.3.0rc01.3.0rc11.3.0rc21.3.01.4.0rc01.4.0rc11.4.01.4.11.5.0rc01.5.0rc11.5.01.5.11.6.0rc01.6.0rc11.6.01.7.0rc01.7.0rc11.7.01.7.11.8.0rc01.8.0rc11.8.01.9.0rc01.9.0rc11.9.0rc21.9.01.10.0rc01.10.0rc11.10.01.10.11.11.0rc01.11.0rc11.11.0rc21.11.01.12.0rc0
1.15.52.0.42.1.32.2.22.3.2
tensorflow-cpu
PyPI
 before 1.15.5, 2.0.0 up to 2.0.4, 2.1.0 up to 2.1.3, 2.2.0 up to 2.2.2, 2.3.0 up to 2.3.2, plus listed versions
1.15.02.1.12.1.22.2.12.3.1
1.15.52.0.42.1.32.2.22.3.2
tensorflow-gpu
PyPI
 before 1.15.5, 2.0.0 up to 2.0.4, 2.1.0 up to 2.1.3, 2.2.0 up to 2.2.2, 2.3.0 up to 2.3.2, plus listed versions
0.12.00.12.11.0.01.0.11.1.0
Show 32 moreShow less
1.10.01.10.11.11.01.12.01.12.21.12.31.13.11.13.21.14.01.15.01.15.21.15.31.15.41.2.01.2.11.3.01.4.01.4.11.5.01.5.11.6.01.7.01.7.11.8.01.9.02.0.12.0.22.0.32.1.12.1.22.2.12.3.1
1.15.52.0.42.1.32.2.22.3.2
bazel-skylib1.0.3
SUSE:Package Hub 15 SP3
From an advisory covering 63 CVEs
 before 1.0.3-bp153.2.1
1.0.3-bp153.2.1
bazel3.7
SUSE:Package Hub 15 SP3
From an advisory covering 63 CVEs
 before 3.7.2-bp153.4.1
3.7.2-bp153.4.1
tensorflow2
SUSE:Package Hub 15 SP3
From an advisory covering 63 CVEs
 before 2.6.0-bp153.2.3.1
2.6.0-bp153.2.3.1
tensorflow2-lite
SUSE:Package Hub 15 SP3
From an advisory covering 63 CVEs
 before 2.6.0-bp153.2.3.1
2.6.0-bp153.2.3.1
tensorflow2_2_6_0-gnu-hpc
SUSE:Package Hub 15 SP3
From an advisory covering 63 CVEs
 before 2.6.0-bp153.2.3.1
2.6.0-bp153.2.3.1
tensorflow2_2_6_0-gnu-openmpi2-hpc
SUSE:Package Hub 15 SP3
From an advisory covering 63 CVEs
 before 2.6.0-bp153.2.3.1
2.6.0-bp153.2.3.1
bazel-skylib1.0.3
openSUSE:Leap 15.3
From an advisory covering 63 CVEs
 before 1.0.3-bp153.2.1
1.0.3-bp153.2.1
bazel3.7
openSUSE:Leap 15.3
From an advisory covering 63 CVEs
 before 3.7.2-bp153.4.1
3.7.2-bp153.4.1
tensorflow2
openSUSE:Leap 15.3
From an advisory covering 63 CVEs
 before 2.6.0-bp153.2.3.1
2.6.0-bp153.2.3.1
tensorflow2-lite
openSUSE:Leap 15.3
From an advisory covering 63 CVEs
 before 2.6.0-bp153.2.3.1
2.6.0-bp153.2.3.1
tensorflow2_2_6_0-gnu-hpc
openSUSE:Leap 15.3
From an advisory covering 63 CVEs
 before 2.6.0-bp153.2.3.1
2.6.0-bp153.2.3.1
tensorflow2_2_6_0-gnu-openmpi2-hpc
openSUSE:Leap 15.3
From an advisory covering 63 CVEs
 before 2.6.0-bp153.2.3.1
2.6.0-bp153.2.3.1

History

History
RecordedChangedSources
affected, facts
aliasesEUVD
first indexedCVE List, GitHub Advisories, NVD, OSV

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

In short

CVE-2020-26267 is a medium vulnerability (CVSS 4.4) in Google Tensorflow, published December 10, 2020. No source lists public exploit code. EPSS puts the chance of exploitation in the next 30 days at 0.24%, the 15th percentile. Fixed in tensorflow 1.15.5.

References

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Left out here: 28 versions. The JSON record has them.

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