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Denial of Service in Tensorflow

tensorflow Updated 02 Oct 2025

Summary

25/09/2020
CVE-2020-15203
Yes i
High
7.5

CVE-2020-15203: Denial of Service in Tensorflow. Affects tensorflow across 5 version ranges.

< 1.15.4 >= 2.0.0, < 2.0.3 >= 2.1.0, < 2.1.2 >= 2.2.0, < 2.2.1 >= 2.3.0, < 2.3.1

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

Also tracked for

The same CVE-2020-15203 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

By controlling the fill argument of tf.strings.as_string, a malicious attacker is able to trigger a format string vulnerability due to the way the internal format use in a printf call is constructed: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/as_string_op.cc#L68-L74

This can result in unexpected output:

In [1]: tf.strings.as_string(input=[1234], width=6, fill='-')                                                                     
Out[1]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['1234  '], dtype=object)>                                              
In [2]: tf.strings.as_string(input=[1234], width=6, fill='+')                                                                     
Out[2]: <tf.Tensor: shape=(1,), dtype=string, numpy=array([' +1234'], dtype=object)> 
In [3]: tf.strings.as_string(input=[1234], width=6, fill="h")                                                                     
Out[3]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['%6d'], dtype=object)> 
In [4]: tf.strings.as_string(input=[1234], width=6, fill="d")                                                                     
Out[4]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['12346d'], dtype=object)> 
In [5]: tf.strings.as_string(input=[1234], width=6, fill="o")
Out[5]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['23226d'], dtype=object)>
In [6]: tf.strings.as_string(input=[1234], width=6, fill="x")
Out[6]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['4d26d'], dtype=object)>
In [7]: tf.strings.as_string(input=[1234], width=6, fill="g")
Out[7]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['8.67458e-3116d'], dtype=object)>
In [8]: tf.strings.as_string(input=[1234], width=6, fill="a")
Out[8]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['0x0.00ff7eebb4d4p-10226d'], dtype=object)>
In [9]: tf.strings.as_string(input=[1234], width=6, fill="c")
Out[9]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['\xd26d'], dtype=object)>
In [10]: tf.strings.as_string(input=[1234], width=6, fill="p")
Out[10]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['0x4d26d'], dtype=object)>
In [11]: tf.strings.as_string(input=[1234], width=6, fill='m') 
Out[11]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['Success6d'], dtype=object)>

However, passing in n or s results in segmentation fault.

Patches

We have patched the issue in 33be22c65d86256e6826666662e40dbdfe70ee83 and will release patch releases for all versions between 1.15 and 2.3.

We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

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.

  • < 1.15.4 1.15.4 
    pip install --upgrade "tensorflow==1.15.4"
  • >= 2.0.0, < 2.0.3 2.0.3 
    pip install --upgrade "tensorflow==2.0.3"
  • >= 2.1.0, < 2.1.2 2.1.2 
    pip install --upgrade "tensorflow==2.1.2"
  • >= 2.2.0, < 2.2.1 2.2.1 
    pip install --upgrade "tensorflow==2.2.1"
  • >= 2.3.0, < 2.3.1 2.3.1 
    pip install --upgrade "tensorflow==2.3.1"

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.

  • < 1.15.4 1.15.4 
    pip install --upgrade "tensorflow==1.15.4"
  • >= 2.0.0, < 2.0.3 2.0.3 
    pip install --upgrade "tensorflow==2.0.3"
  • >= 2.1.0, < 2.1.2 2.1.2 
    pip install --upgrade "tensorflow==2.1.2"
  • >= 2.2.0, < 2.2.1 2.2.1 
    pip install --upgrade "tensorflow==2.2.1"
  • >= 2.3.0, < 2.3.1 2.3.1 
    pip install --upgrade "tensorflow==2.3.1"

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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