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Security Advisories: MDE2OlNlY3VyaXR5QWR2aXNvcnlHSFNBLXhtcTctN2Z4bS1ycjc5
Denial of Service in Tensorflow
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.
Permalink: https://github.com/advisories/GHSA-xmq7-7fxm-rr79JSON: https://advisories.ecosyste.ms/api/v1/advisories/MDE2OlNlY3VyaXR5QWR2aXNvcnlHSFNBLXhtcTctN2Z4bS1ycjc5
Source: GitHub Advisory Database
Origin: Unspecified
Severity: High
Classification: General
Published: over 3 years ago
Updated: about 1 year ago
CVSS Score: 7.5
CVSS vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
Identifiers: GHSA-xmq7-7fxm-rr79, CVE-2020-15203
References:
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xmq7-7fxm-rr79
- https://github.com/tensorflow/tensorflow/commit/33be22c65d86256e6826666662e40dbdfe70ee83
- https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1
- https://nvd.nist.gov/vuln/detail/CVE-2020-15203
- http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html
- https://github.com/advisories/GHSA-xmq7-7fxm-rr79
Affected Packages
pypi:tensorflow-gpu
Dependent packages: 146Dependent repositories: 11,499
Downloads: 354,170 last month
Affected Version Ranges: = 2.3.0, = 2.2.0, >= 2.1.0, < 2.1.2, >= 2.0.0, < 2.0.3, < 1.15.4
Fixed in: 2.3.1, 2.2.1, 2.1.2, 2.0.3, 1.15.4
All affected versions: 0.12.0, 0.12.1, 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, 2.0.0, 2.0.1, 2.0.2, 2.1.0, 2.1.1, 2.2.0, 2.3.0
All unaffected versions: 1.15.4, 1.15.5, 2.0.3, 2.0.4, 2.1.2, 2.1.3, 2.1.4, 2.2.1, 2.2.2, 2.2.3, 2.3.1, 2.3.2, 2.3.3, 2.3.4, 2.4.0, 2.4.1, 2.4.2, 2.4.3, 2.4.4, 2.5.0, 2.5.1, 2.5.2, 2.5.3, 2.6.0, 2.6.1, 2.6.2, 2.6.3, 2.6.4, 2.6.5, 2.7.0, 2.7.1, 2.7.2, 2.7.3, 2.7.4, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.0, 2.9.1, 2.9.2, 2.9.3, 2.10.0, 2.10.1, 2.11.0, 2.12.0
pypi:tensorflow-cpu
Dependent packages: 71Dependent repositories: 2,483
Downloads: 1,005,140 last month
Affected Version Ranges: = 2.3.0, = 2.2.0, >= 2.1.0, < 2.1.2, >= 2.0.0, < 2.0.3, < 1.15.4
Fixed in: 2.3.1, 2.2.1, 2.1.2, 2.0.3, 1.15.4
All affected versions: 1.15.0, 2.1.0, 2.1.1, 2.2.0, 2.3.0
All unaffected versions: 2.1.2, 2.1.3, 2.1.4, 2.2.1, 2.2.2, 2.2.3, 2.3.1, 2.3.2, 2.3.3, 2.3.4, 2.4.0, 2.4.1, 2.4.2, 2.4.3, 2.4.4, 2.5.0, 2.5.1, 2.5.2, 2.5.3, 2.6.0, 2.6.1, 2.6.2, 2.6.3, 2.6.4, 2.6.5, 2.7.0, 2.7.1, 2.7.2, 2.7.3, 2.7.4, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.0, 2.9.1, 2.9.2, 2.9.3, 2.10.0, 2.10.1, 2.11.0, 2.11.1, 2.12.0, 2.12.1, 2.13.0, 2.13.1, 2.14.0, 2.14.1, 2.15.0, 2.15.1, 2.16.1
pypi:tensorflow
Dependent packages: 1,733Dependent repositories: 73,755
Downloads: 21,673,933 last month
Affected Version Ranges: = 2.3.0, = 2.2.0, >= 2.1.0, < 2.1.2, >= 2.0.0, < 2.0.3, < 1.15.4
Fixed in: 2.3.1, 2.2.1, 2.1.2, 2.0.3, 1.15.4
All affected versions: 0.12.0, 0.12.1, 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, 2.0.0, 2.0.1, 2.0.2, 2.1.0, 2.1.1, 2.2.0, 2.3.0
All unaffected versions: 1.15.4, 1.15.5, 2.0.3, 2.0.4, 2.1.2, 2.1.3, 2.1.4, 2.2.1, 2.2.2, 2.2.3, 2.3.1, 2.3.2, 2.3.3, 2.3.4, 2.4.0, 2.4.1, 2.4.2, 2.4.3, 2.4.4, 2.5.0, 2.5.1, 2.5.2, 2.5.3, 2.6.0, 2.6.1, 2.6.2, 2.6.3, 2.6.4, 2.6.5, 2.7.0, 2.7.1, 2.7.2, 2.7.3, 2.7.4, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.0, 2.9.1, 2.9.2, 2.9.3, 2.10.0, 2.10.1, 2.11.0, 2.11.1, 2.12.0, 2.12.1, 2.13.0, 2.13.1, 2.14.0, 2.14.1, 2.15.0, 2.15.1, 2.16.1