CVE-2022-21733: TensorFlow Memory Exhaustion Vulnerability
Summary
CVE-2022-21733: Memory exhaustion in Tensorflow. Affects tensorflow across 3 version ranges.
< 2.5.3
>= 2.6.0, < 2.6.3
>= 2.7.0, < 2.7.1
Fixed in 2.5.3, 2.6.3 or 2.7.1, depending on the release line you run. How to fix ↓
Also tracked for
- tensorflow · Bitnami
- tensorflow-gpu · PyPI
- tensorflow-cpu · PyPI
The same CVE-2022-21733 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 library developed by Google. It is widely used for building and training machine learning models, offering a flexible architecture that allows deployment across various platforms. However, TensorFlow has a vulnerability related to its StringNGrams operation, which can lead to memory exhaustion and denial of service attacks.
Vulnerability Detail
The vulnerability arises from the implementation of the StringNGrams operation in TensorFlow, where a lack of validation on the 'pad_width' parameter can lead to an integer overflow. This overflow results in a negative value for 'ngram_width', which is subsequently used to allocate memory for the output. If exploited, this can trigger an out-of-memory (OOM) condition, effectively causing a denial of service. The vulnerability is particularly concerning as it can be triggered with minimal input, making it a potential target for attackers.
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.
-
< 2.5.32.5.3pip install --upgrade "tensorflow==2.5.3" -
>= 2.6.0, < 2.6.32.6.3pip install --upgrade "tensorflow==2.6.3" -
>= 2.7.0, < 2.7.12.7.1pip install --upgrade "tensorflow==2.7.1"
Tip — Not sure which release line you're on? Vulert can find the fix for the version you are using. Scan free →
Workaround
Currently, there are no specific workarounds mentioned for this vulnerability. However, avoiding the use of the StringNGrams operation with invalid parameters may help mitigate the risk until the package is updated.
Conclusion
The CVE-2022-21733 vulnerability in TensorFlow poses a significant risk of memory exhaustion and denial of service. It is crucial for users to update to the patched versions to mitigate this risk. Regular monitoring and updating of dependencies can help maintain the security of applications.
See every vulnerability recorded for tensorflow →
FAQ
CVE-2022-21733 is a vulnerability in TensorFlow that can lead to memory exhaustion and denial of service due to improper validation of the 'pad_width' parameter in the StringNGrams operation.
You can check if your application is affected by using Vulert, which monitors and alerts for open-source vulnerabilities in real-time. You can use the Vulert playground without signup to test your application.
Exploitation of this vulnerability can lead to an out-of-memory condition, causing applications using TensorFlow to crash or become unresponsive, resulting in a denial of service.
You can update TensorFlow by running the command 'pip install --upgrade tensorflow==<version>' in your terminal, replacing '<version>' with 2.5.3, 2.6.3, or 2.7.1.
Yes, patches are available in TensorFlow versions 2.5.3, 2.6.3, and 2.7.1, which address the vulnerability.
References
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