CVE-2022-23567: Integer overflows in Tensorflow
Summary
CVE-2022-23567: Integer overflows 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-2022-23567 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 resources for building and deploying machine learning models. Tensorflow is widely used in various domains, including research, industry, and academia. However, certain versions of Tensorflow are vulnerable to integer overflows, which can lead to denial of service attacks or assert failures.
Vulnerability Detail
The vulnerability in Tensorflow arises from the implementations of Sparse*Cwise* ops. These operations are susceptible to integer overflows, which can be exploited to trigger large allocations, causing out-of-memory denial of service attacks. Additionally, the vulnerability can also result in CHECK failures when constructing new TensorShape objects, leading to assert failures and denial of service. An attacker can leverage this vulnerability to disrupt the normal functioning of Tensorflow-based applications.
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"
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Workaround
If updating Tensorflow is not immediately feasible, users can implement a workaround by validating the shapes of input tensors and avoiding the construction of large TensorShape objects with user-provided dimensions. This can be achieved by replacing the call to the TensorShape constructor with a call to the BuildTensorShape static helper factory. Here is an example of the workaround in Python:
import tensorflow as tf
import numpy as np
tf.raw_ops.SparseDenseCwiseDiv(
sp_indices=np.array([[9]]),
sp_values=np.array([5]),
sp_shape=np.array([92233720368., 92233720368]),
dense=np.array([4]))
By implementing this workaround, users can mitigate the risk of exploitation until they are able to update to a patched version of Tensorflow.
Conclusion
The vulnerability in Tensorflow can be mitigated by updating to one of the patched versions or implementing the provided workaround. It is crucial for users to take prompt action to protect their Tensorflow-based applications from potential exploitation. For further support and assistance in 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 resources for building and deploying machine learning models.
The vulnerability allows attackers to trigger denial of service attacks or assert failures in Tensorflow-based applications.
To fix the vulnerability, update your Tensorflow installation to one of the patched versions: 2.5.3, 2.6.3, 2.7.1, or 2.8.0.
Yes, you can implement a workaround by validating input tensor shapes and avoiding the construction of large TensorShape objects with user-provided dimensions.
For more information about Tensorflow security, please consult the official Tensorflow security guide.
References
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