CVE-2022-23595: TensorFlow GPU - Null Pointer Dereference
Summary
CVE-2022-23595: Tensorflow is an Open Source Machine Learning Framework. Affects tensorflow-gpu across 3 version ranges.
< 2.5.3
>= 2.6.0, < 2.6.3
< e21af685e1828f7ca65038307df5cc06de4479e8
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-2022-23595 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
Habilume
Habit tracker with a companion that grows with you
Complete habits, earn Lume Points, and watch your Lume grow as you stay consistent.
Background
TensorFlow is an open-source machine learning framework that provides a comprehensive ecosystem of tools, libraries, and resources for developing and deploying machine learning models. It is widely used in various domains, including research, industry, and academia. TensorFlow GPU is a specific package that enables GPU acceleration for TensorFlow computations, allowing for faster training and inference on compatible hardware.
Vulnerability Detail
The vulnerability (CVE-2022-23595) in TensorFlow GPU arises when building an XLA compilation cache using default settings. In this scenario, TensorFlow triggers a null pointer dereference, leading to a crash or unexpected behavior. By default, all devices are allowed, resulting in a null flr->config_proto. This vulnerability can be exploited by an attacker to cause a denial of service (DoS) or potentially execute arbitrary code. The issue affects TensorFlow versions 2.5.3, 2.6.3, and 2.7.1, as well as earlier versions still within the supported range.
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
As of now, no specific workaround has been provided for this vulnerability. Users are encouraged to apply the fix by updating to the patched versions of TensorFlow GPU once they are released.
Conclusion
The vulnerability in TensorFlow GPU (CVE-2022-23595) poses a risk of null pointer dereference, potentially leading to a denial of service or arbitrary code execution. The TensorFlow development team has addressed the issue and will release patched versions to fix the vulnerability. Users are advised to update their TensorFlow GPU installations to the latest patched versions as soon as they are available. For further support on vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow-gpu →
FAQ
TensorFlow is an open-source machine learning framework that provides tools and libraries for developing and deploying machine learning models.
TensorFlow GPU is a package that enables GPU acceleration for TensorFlow computations, allowing for faster training and inference on compatible hardware.
A null pointer dereference occurs when a program attempts to access or manipulate memory using a null pointer, resulting in a crash or unexpected behavior.
This vulnerability can lead to a denial of service (DoS) or potentially allow an attacker to execute arbitrary code, depending on how it is exploited.
To protect your system, it is recommended to update your TensorFlow GPU installation to the latest patched version once it becomes available.
References
Related vulnerabilities
Recent vulnerabilities recorded for tensorflow-gpu.
Habilume
Habit tracker with a companion that grows with you
Complete habits, earn Lume Points, and watch your Lume grow as you stay consistent.
