CVE-2020-15203: Format String Vulnerability in TensorFlow-GPU
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-gpu 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 · PyPI
- tensorflow-cpu · PyPI
- tensorflow · 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-GPU is an open-source machine learning framework developed by Google. It provides a comprehensive ecosystem of tools, libraries, and resources for building and deploying machine learning models. TensorFlow-GPU leverages the power of GPUs to accelerate computation and is widely used in various industries and research domains. However, a vulnerability has been discovered in TensorFlow-GPU versions before 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1.
Vulnerability Detail
The vulnerability (CVE-2020-15203) arises from a format string vulnerability in the tf.strings.as_string function of TensorFlow-GPU. By manipulating the fill argument, an attacker can trigger the vulnerability, which is caused by the construction of the internal format used in a printf call. Exploiting this vulnerability can lead to a segmentation fault, potentially enabling arbitrary code execution or denial of service attacks. The issue has been patched in commit 33be22c65d86256e6826666662e40dbdfe70ee83 and is addressed in TensorFlow-GPU versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1.
How To Fix
Latest published version: 2.12.0 as of 22 Oct 2026
pip install --upgrade "tensorflow-gpu==2.12.0"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-gpu==1.15.4" -
>= 2.0.0, < 2.0.32.0.3pip install --upgrade "tensorflow-gpu==2.0.3" -
>= 2.1.0, < 2.1.22.1.2pip install --upgrade "tensorflow-gpu==2.1.2" -
>= 2.2.0, < 2.2.12.2.1pip install --upgrade "tensorflow-gpu==2.2.1" -
>= 2.3.0, < 2.3.12.3.1pip install --upgrade "tensorflow-gpu==2.3.1"
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Workaround
No workaround has been provided for this vulnerability.
Conclusion
In conclusion, a format string vulnerability has been discovered in TensorFlow-GPU versions before 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1. This vulnerability can be exploited by manipulating the fill argument of tf.strings.as_string, leading to a segmentation fault. It is crucial to update TensorFlow-GPU to the patched versions to mitigate the risk of arbitrary code execution or denial of service attacks. For further support on vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow-gpu →
FAQ
TensorFlow-GPU is an open-source machine learning framework developed by Google that leverages GPUs for accelerated computation.
A format string vulnerability occurs when a program uses user-supplied input as the format string parameter in a formatted output function, such as printf. If the input is not properly validated, an attacker can exploit this vulnerability to execute arbitrary code or crash the program.
Exploiting this vulnerability can lead to a segmentation fault, potentially enabling arbitrary code execution or denial of service attacks.
You can use the Vulert Playground (https://vulert.com/scanner) to check if your application is affected by this vulnerability. Simply provide your Pipfile.lock and it will alert you in real-time for open-source vulnerabilities.
For more information about this vulnerability, you can refer to the official GitHub advisory database: https://github.com/pypa/advisory-database/blob/main/vulns/tensorflow-gpu/PYSEC-2020-318.yaml
References
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