CVE-2022-23563: TensorFlow GPU - Insecure Usage of tempfile.mktemp
Summary
CVE-2022-23563: Tensorflow is an Open Source Machine Learning Framework. Affects tensorflow-gpu across 2 version ranges.
< 2.5.3
>= 2.6.0, < 2.6.3
Fixed in 2.5.3 or 2.6.3, depending on the release line you run. How to fix ↓
Also tracked for
- tensorflow · PyPI
- tensorflow · Bitnami
- tensorflow · PyPI
- tensorflow-cpu · PyPI
The same CVE-2022-23563 is tracked separately for each ecosystem and vendor. Its fix information is the same; the package and affected versions differ.
Check if this vulnerability affects your application
Upload a manifest or SBOM and we'll show whether you're affected, plus every other risky package in the same scan.
No signup required · Results in about 60 seconds
Background
TensorFlow is an open-source machine learning framework widely used for developing and deploying machine learning models. It provides a comprehensive set of tools and libraries for building and training neural networks. TensorFlow GPU is a variant of TensorFlow that leverages GPU acceleration for faster computation.
Vulnerability Detail
The vulnerability (CVE-2022-23563) in TensorFlow GPU arises from the insecure usage of the tempfile.mktemp function to create temporary files. This function is intended for testing purposes and can lead to a time-of-check to time-of-use (TOCTOU) vulnerability. In certain instances, TensorFlow incorrectly uses mktemp to create temporary files instead of directories, which can allow a different process to create a file with the same name before TensorFlow creates it. This logic flaw is masked by the usage of mktemp. The issue has been addressed in several commits by replacing mktemp with the safer mkstemp or mkdtemp functions, depending on the specific usage pattern.
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.
-
< 2.5.32.5.3pip install --upgrade "tensorflow-gpu==2.5.3" -
>= 2.6.0, < 2.6.32.6.3pip install --upgrade "tensorflow-gpu==2.6.3"
Tip — Not sure which release line you're on? Vulert can find the fix for the version you are using. Scan free →
Workaround
There is no specific workaround provided for this vulnerability. The recommended solution is to upgrade to the patched version of TensorFlow GPU.
Conclusion
The vulnerability (CVE-2022-23563) in TensorFlow GPU arises from the insecure usage of tempfile.mktemp to create temporary files. Upgrading to TensorFlow GPU version 2.6.3 or later resolves this issue by replacing the insecure function with safer alternatives. It is recommended to upgrade to the latest version to ensure the security of your TensorFlow GPU installation. For further support on vulnerability remediation, you can reach out to DevNack.com.
See every vulnerability recorded for tensorflow-gpu →
FAQ
TensorFlow GPU is a variant of TensorFlow that utilizes GPU acceleration for faster computation. It is commonly used in machine learning applications that require high-performance computing.
The vulnerability can potentially allow an attacker to manipulate or access temporary files created by TensorFlow GPU. This could lead to unauthorized access, data leakage, or other security breaches.
You can use Vulert's vulnerability monitoring service to check if your application is affected by this vulnerability. Vulert analyzes your Pipfile.lock and provides real-time alerts for open-source vulnerabilities, including this one.
Yes, the TensorFlow team has released a fix for this vulnerability in version 2.6.3. It is recommended to upgrade to this version or later to ensure the vulnerability is addressed.
There are no known exploits for this vulnerability at the time of this report. However, it is important to apply the recommended fix to mitigate any potential risks.
References
Related vulnerabilities
Recent vulnerabilities recorded for tensorflow-gpu.
