CVE-2022-35967: TensorFlow Segfault Vulnerability in QuantizedAdd Function
Summary
CVE-2022-35967: TensorFlow vulnerable to segfault in QuantizedAdd. Affects tensorflow across 3 version ranges.
< 2.7.2
>= 2.8.0, < 2.8.1
>= 2.9.0, < 2.9.1
Fixed in 2.7.2, 2.8.1 or 2.9.1, depending on the release line you run. How to fix ↓
Also tracked for
- tensorflow-cpu · PyPI
- tensorflow-gpu · PyPI
- tensorflow · Bitnami
The same CVE-2022-35967 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, widely used for building and training machine learning models. It provides a flexible architecture for deploying computation across various platforms. However, certain operations, such as QuantizedAdd, can lead to vulnerabilities if not handled correctly, particularly when dealing with tensor ranks.
Vulnerability Detail
The vulnerability arises in the QuantizedAdd operation when it is provided with min_input or max_input tensors that have a nonzero rank. This can lead to a segmentation fault (segfault), which can be exploited to perform a denial of service attack. The issue is particularly concerning as it can crash applications using TensorFlow, impacting their availability and reliability.
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.
-
< 2.7.22.7.2pip install --upgrade "tensorflow==2.7.2" -
>= 2.8.0, < 2.8.12.8.1pip install --upgrade "tensorflow==2.8.1" -
>= 2.9.0, < 2.9.12.9.1pip install --upgrade "tensorflow==2.9.1"
Tip — Not sure which release line you're on? Vulert can find the fix for the version you are using. Scan free →
Workaround
As a temporary workaround, avoid using the QuantizedAdd operation with min_input or max_input tensors that have a nonzero rank. Here is an example of code that triggers the vulnerability:
import tensorflow as tf
Toutput = tf.qint32 x = tf.constant(140, shape=[1], dtype=tf.quint8) y = tf.constant(26, shape=[10], dtype=tf.quint8) min_x = tf.constant([], shape=[0], dtype=tf.float32) max_x = tf.constant(0, shape=[], dtype=tf.float32) min_y = tf.constant(0, shape=[], dtype=tf.float32) max_y = tf.constant(0, shape=[], dtype=tf.float32) tf.raw_ops.QuantizedAdd(x=x, y=y, min_x=min_x, max_x=max_x, min_y=min_y, max_y=max_y, Toutput=Toutput)
Conclusion
The CVE-2022-35967 vulnerability in TensorFlow's QuantizedAdd operation poses a significant risk by potentially allowing denial of service attacks. Users are encouraged to update to the latest version of TensorFlow to mitigate this risk and ensure application stability.
See every vulnerability recorded for tensorflow →
FAQ
CVE-2022-35967 is a vulnerability in TensorFlow that can cause a segmentation fault when using the QuantizedAdd operation with improperly shaped tensors.
Exploiting this vulnerability can lead to a denial of service, crashing applications that utilize TensorFlow.
You can use Vulert's playground at Vulert Scanner to check if your application is affected by this vulnerability without needing to sign up.
TensorFlow versions prior to 2.10.0, as well as 2.9.0, 2.8.0, and 2.7.2, are affected by this vulnerability.
You can update TensorFlow to the patched version by running the command 'pip install --upgrade tensorflow'.
References
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