CVE-2023-25674: TensorFlow GPU Null Pointer Error in RandomShuffle with XLA Enabled
Summary
CVE-2023-25674: TensorFlow has Null Pointer Error in RandomShuffle with XLA enable. Affects tensorflow-gpu versions < 2.11.1. Fixed in 2.11.1. How to fix ↓
Also tracked for
- tensorflow · PyPI
- tensorflow-cpu · PyPI
- tensorflow · Bitnami
- tensorflow · PyPI
The same CVE-2023-25674 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 widely used for various applications, including deep learning and neural networks. The 'tensorflow-gpu' package allows users to leverage GPU acceleration for improved performance. However, like any software, TensorFlow is susceptible to vulnerabilities that can impact its functionality and security. This particular vulnerability arises in the RandomShuffle operation when XLA (Accelerated Linear Algebra) is enabled, potentially leading to application crashes or unexpected behavior.
Vulnerability Detail
The vulnerability identified as CVE-2023-25674 is a Null Pointer Error (NPE) that occurs in the RandomShuffle function when XLA is enabled. This issue can be triggered by providing specific parameters, such as a large value for 'value' and negative integers for 'seed' and 'seed2'. The NPE can cause the application to crash, leading to denial of service and potentially exposing sensitive data if the application is compromised. The technical details indicate that the error originates from improper handling of input parameters within the TensorFlow library.
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 version below is the minimum fixed release for this vulnerability.
If you need the minimum change
2.11.1
This is the first release that contains the fix.
pip install --upgrade "tensorflow-gpu==2.11.1"Tip — Not sure which version your app runs? Vulert can match this fix to it. Scan free →
Workaround
As a temporary workaround, users can avoid using the RandomShuffle function with the problematic parameters. Here is an example of how to modify the parameters to prevent the NPE:
import tensorflow as tf
func = tf.raw_ops.RandomShuffle
para = {'value': [1, 2, 3, 4, 5], 'seed': 42, 'seed2': 42}
@tf.function(jit_compile=True)
def test():
y = func(**para)
return y
test()
Conclusion
The CVE-2023-25674 vulnerability in TensorFlow's RandomShuffle function poses a significant risk due to the potential for application crashes. Users are advised to update to the latest version of 'tensorflow-gpu' to mitigate this issue. Additionally, utilizing tools like Vulert can help monitor and alert for such vulnerabilities in real-time.
See every vulnerability recorded for tensorflow-gpu →
FAQ
CVE-2023-25674 is a vulnerability in TensorFlow's RandomShuffle function that can lead to a Null Pointer Error when XLA is enabled.
This vulnerability can cause your application to crash, leading to denial of service and potential exposure of sensitive data.
You can use Vulert's playground at Vulert Scanner to check if your application is affected by this vulnerability without needing to sign up.
Update your 'tensorflow-gpu' package to version 2.11.1 or later using 'pip install --upgrade tensorflow-gpu'.
Yes, you can modify the parameters used in the RandomShuffle function to avoid triggering the Null Pointer Error.
References
Related vulnerabilities
Recent vulnerabilities recorded for tensorflow-gpu.
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