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CVE-2020-15203

HIGH
Published 2020-09-25T18:46:08
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CVSS Score

V3.1
7.5
/10
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
Base Score Metrics
Exploitability: N/A Impact: N/A

EPSS Score

v2025.03.14
0.004
probability
of exploitation in the wild

There is a 0.4% chance that this vulnerability will be exploited in the wild within the next 30 days.

Updated: 2025-06-25
Exploit Probability
Percentile: 0.574
Higher than 57.4% of all CVEs

Attack Vector Metrics

Attack Vector
NETWORK
Attack Complexity
LOW
Privileges Required
NONE
User Interaction
NONE
Scope
UNCHANGED

Impact Metrics

Confidentiality
NONE
Integrity
NONE
Availability
HIGH

Description

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, 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. This may result in segmentation fault. The issue is patched in commit 33be22c65d86256e6826666662e40dbdfe70ee83, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

Available Exploits

No exploits available for this CVE.

Related News

No news articles found for this CVE.

Affected Products

GitHub Security Advisories

Community-driven vulnerability intelligence from GitHub

✓ GitHub Reviewed HIGH

Denial of Service in Tensorflow

GHSA-xmq7-7fxm-rr79

Advisory Details

### Impact By controlling the `fill` argument of [`tf.strings.as_string`](https://www.tensorflow.org/api_docs/python/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: ```python 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](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) 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.

Affected Packages

PyPI tensorflow
ECOSYSTEM: ≥0 <1.15.4
PyPI tensorflow
ECOSYSTEM: ≥2.0.0 <2.0.3
PyPI tensorflow
ECOSYSTEM: ≥2.1.0 <2.1.2
PyPI tensorflow
ECOSYSTEM: ≥2.2.0 <2.2.1
PyPI tensorflow
ECOSYSTEM: ≥2.3.0 <2.3.1
PyPI tensorflow-cpu
ECOSYSTEM: ≥0 <1.15.4
PyPI tensorflow-cpu
ECOSYSTEM: ≥2.0.0 <2.0.3
PyPI tensorflow-cpu
ECOSYSTEM: ≥2.1.0 <2.1.2
PyPI tensorflow-cpu
ECOSYSTEM: ≥2.2.0 <2.2.1
PyPI tensorflow-cpu
ECOSYSTEM: ≥2.3.0 <2.3.1
PyPI tensorflow-gpu
ECOSYSTEM: ≥0 <1.15.4
PyPI tensorflow-gpu
ECOSYSTEM: ≥2.0.0 <2.0.3
PyPI tensorflow-gpu
ECOSYSTEM: ≥2.1.0 <2.1.2
PyPI tensorflow-gpu
ECOSYSTEM: ≥2.2.0 <2.2.1
PyPI tensorflow-gpu
ECOSYSTEM: ≥2.3.0 <2.3.1

CVSS Scoring

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

Advisory provided by GitHub Security Advisory Database. Published: September 25, 2020, Modified: October 28, 2024

References

Published: 2020-09-25T18:46:08
Last Modified: 2024-08-04T13:08:22.972Z
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