CVE-2022-41896: TensorFlow Crashes Due to Invalid Mfcc Input
Summary
CVE-2022-41896: tf.raw_ops.Mfcc crashes. Affects tensorflow across 3 version ranges.
< 2.8.4
>= 2.9.0, < 2.9.3
>= 2.10.0, < 2.10.1
Fixed in 2.8.4, 2.9.3 or 2.10.1, depending on the release line you run. How to fix ↓
Also tracked for
- tensorflow-cpu · PyPI
- tensorflow-gpu · PyPI
- tensorflow · Bitnami
The same CVE-2022-41896 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. It provides a flexible platform for building and deploying machine learning models. However, like any software, TensorFlow can have vulnerabilities that may affect its stability and security. This particular vulnerability arises from the tf.raw_ops.Mfcc function, which can lead to crashes when provided with invalid input parameters.
Vulnerability Detail
The vulnerability occurs when the ThreadUnsafeUnigramCandidateSampler is given an input filterbank_channel_count that exceeds the maximum allowed size. This can cause TensorFlow to crash, leading to potential disruptions in applications relying on this library. The issue is triggered specifically when the filterbank_channel_count is set to a value greater than 2^31 - 1, which is not handled correctly by the library, resulting in a crash during execution.
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.
-
< 2.8.42.8.4pip install --upgrade "tensorflow==2.8.4" -
>= 2.9.0, < 2.9.32.9.3pip install --upgrade "tensorflow==2.9.3" -
>= 2.10.0, < 2.10.12.10.1pip install --upgrade "tensorflow==2.10.1"
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Workaround
As a temporary workaround, avoid setting the filterbank_channel_count parameter to excessively high values. For example, ensure that the value is within the acceptable range, such as:
import tensorflow as tf
tf.raw_ops.Mfcc(
spectrogram = [[[1.38, 6.32, 5.75, 9.51]]],
sample_rate = 2,
upper_frequency_limit = 5.0,
lower_frequency_limit = 1.0,
filterbank_channel_count = 128, # Use a safe value
dct_coefficient_count = 1
)
Conclusion
In summary, CVE-2022-41896 is a significant vulnerability in TensorFlow that can lead to crashes when improper input values are used. It is crucial to update to the latest patched versions to mitigate this risk. Additionally, users should be cautious with input parameters to avoid triggering the vulnerability.
See every vulnerability recorded for tensorflow →
FAQ
CVE-2022-41896 is a vulnerability in TensorFlow that causes the library to crash when the filterbank_channel_count parameter is set to an excessively high value.
You can use the Vulert playground at Vulert Scanner to check if your application is affected by this vulnerability without needing to sign up.
The affected versions include TensorFlow versions prior to 2.8.4, 2.9.3, and 2.10.1. It is recommended to update to version 2.11 or the patched versions.
If you cannot update TensorFlow immediately, ensure that you do not set the filterbank_channel_count parameter to values greater than 128 to avoid crashes.
Vulert is a tool that monitors and alerts for open-source vulnerabilities in software using just the Pipfile.lock, providing real-time insights without requiring access to the code.
References
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