CVE-2020-15203: Format String Vulnerability in TensorFlow
Summary
CVE-2020-15203: 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 inte... Affects tensorflow across 6 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
< 33be22c65d86256e6826666662e40dbdfe70ee83
Fixed in 5 releases; the minimum fixed version depends on the release line you run. How to fix ↓
Also tracked for
- tensorflow · Bitnami
- tensorflow-cpu · PyPI
- tensorflow-gpu · PyPI
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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Background
TensorFlow is an open-source machine learning framework developed by Google. It provides a comprehensive ecosystem of tools, libraries, and community resources that enable researchers and developers to build and deploy machine learning models. TensorFlow is widely used in various domains, including computer vision, natural language processing, and deep learning. However, versions before 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 are vulnerable to a format string vulnerability.
Vulnerability Detail
The vulnerability (CVE-2020-15203) arises due to the improper handling of the fill argument in the tf.strings.as_string function. An attacker can exploit this vulnerability by controlling the fill argument and triggering a format string vulnerability. This can lead to a segmentation fault and potentially allow the attacker to execute arbitrary code or cause a denial of service. The vulnerability has been patched in commit 33be22c65d86256e6826666662e40dbdfe70ee83 and is addressed in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1.
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.
-
< 1.15.41.15.4pip install --upgrade "tensorflow==1.15.4" -
>= 2.0.0, < 2.0.32.0.3pip install --upgrade "tensorflow==2.0.3" -
>= 2.1.0, < 2.1.22.1.2pip install --upgrade "tensorflow==2.1.2" -
>= 2.2.0, < 2.2.12.2.1pip install --upgrade "tensorflow==2.2.1" -
>= 2.3.0, < 2.3.12.3.1pip install --upgrade "tensorflow==2.3.1"
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Workaround
There is no known workaround for this vulnerability. It is crucial to update to the patched versions as soon as possible to mitigate the risk.
Conclusion
The format string vulnerability in TensorFlow (CVE-2020-15203) can be exploited by controlling the fill argument of the tf.strings.as_string function. It is crucial to update to the patched versions (1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1) to mitigate the risk. For further support on vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow →
FAQ
TensorFlow is an open-source machine learning framework developed by Google. It provides tools and libraries for building and deploying machine learning models.
A format string vulnerability occurs when a program uses user-supplied input as the format string parameter in a formatted printing function, such as printf. If the input is not properly validated, an attacker can exploit this vulnerability to execute arbitrary code or cause a denial of service.
The format string vulnerability in TensorFlow can be exploited by controlling the fill argument of the tf.strings.as_string function. By providing a malicious format string, an attacker can trigger the vulnerability and potentially execute arbitrary code or cause a segmentation fault.
Yes, the vulnerability has been patched in commit 33be22c65d86256e6826666662e40dbdfe70ee83. Users should update their TensorFlow installation to versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1 to mitigate the risk.
No, there are no known workarounds for this vulnerability. It is recommended to update to the patched versions of TensorFlow to ensure the security of the system.
References
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