CVE-2022-41896: TensorFlow - `tf.raw_ops.Mfcc` crashes
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.
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 developed by Google. It provides a comprehensive ecosystem of tools, libraries, and resources for building and deploying machine learning models. The tf.raw_ops.Mfcc function in TensorFlow is used to compute the Mel-frequency cepstral coefficients (MFCC) of a given spectrogram. MFCC is a widely used feature extraction technique in speech and audio processing tasks. However, a vulnerability has been discovered in the tf.raw_ops.Mfcc function that can cause TensorFlow to crash under certain conditions.
Vulnerability Detail
The vulnerability arises when the ThreadUnsafeUnigramCandidateSampler function in TensorFlow is provided with an input filterbank_channel_count value that exceeds the allowed maximum size. This can lead to a crash in TensorFlow. An attacker could potentially exploit this vulnerability by providing malicious input to the tf.raw_ops.Mfcc function, causing a denial of service (DoS) condition. The impact of this vulnerability is considered moderate, with a CVSS score of 4.8.
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"
Tip — Not sure which release line you're on? Vulert can find the fix for the version you are using. Scan free →
Workaround
There is no specific workaround provided for this vulnerability. Users are advised to update to the patched versions of TensorFlow as mentioned in the 'how_to_fix' section.
Conclusion
The vulnerability in TensorFlow's tf.raw_ops.Mfcc function has been addressed through patches in the latest versions of TensorFlow and backported to older supported versions. Users are advised to update their TensorFlow installations to the patched versions to mitigate the risk. For further support on 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 tf.raw_ops.Mfcc function is used to compute the Mel-frequency cepstral coefficients (MFCC) of a given spectrogram. MFCC is a feature extraction technique used in speech and audio processing tasks.
The vulnerability can cause TensorFlow to crash when provided with certain input values. This can lead to a denial of service (DoS) condition. The impact is considered moderate.
To fix this vulnerability, update to the latest version of TensorFlow (2.11) or one of the patched versions (2.10.1, 2.9.3, or 2.8.4).
No specific workaround is provided. Updating to the patched versions of TensorFlow is the recommended solution.
References
Related vulnerabilities
Recent vulnerabilities recorded for tensorflow.
Habilume
Habit tracker with a companion that grows with you
Complete habits, earn Lume Points, and watch your Lume grow as you stay consistent.
