CVE-2020-15190: TensorFlow GPU - Segmentation Fault in Switch Operation
Summary
CVE-2020-15190: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the tf.raw_ops.Switch operation takes as input a tensor and a boolean and outputs two tensors. Affects tensorflow-gpu across 6 version ranges.
< 1.15.4
>= 2.0.0, < 2.0.3
>= 2.1.0, < 2.1.2
>= 2.2.0, < 2.2.1
>= 2.3.0, < 2.3.1
< da8558533d925694483d2c136a9220d6d49d843c
Fixed in 5 releases; the minimum fixed version depends on the release line you run. How to fix ↓
Also tracked for
- tensorflow · Bitnami
- tensorflow · PyPI
- tensorflow-cpu · PyPI
- tensorflow · PyPI
The same CVE-2020-15190 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
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 GPU is a variant of TensorFlow that leverages the power of GPUs for accelerated computation. It allows users to train and run machine learning models on GPU devices, resulting in improved performance and efficiency. However, a vulnerability has been discovered in TensorFlow GPU versions before 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1.
Vulnerability Detail
The vulnerability, identified as CVE-2020-15190, exists in the tf.raw_ops.Switch operation in TensorFlow GPU. This operation takes a tensor and a boolean as input and outputs two tensors. Depending on the boolean value, one of the tensors is the input tensor itself, while the other should be an empty tensor. However, the eager runtime in TensorFlow traverses all tensors in the output, even though only one of them is defined. As a result, a reference is bound to a nullptr, leading to undefined behavior. If the code is compiled with -fsanitize=null, this can result in a segmentation fault. The issue has been patched in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1.
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 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.
-
< 1.15.41.15.4pip install --upgrade "tensorflow-gpu==1.15.4" -
>= 2.0.0, < 2.0.32.0.3pip install --upgrade "tensorflow-gpu==2.0.3" -
>= 2.1.0, < 2.1.22.1.2pip install --upgrade "tensorflow-gpu==2.1.2" -
>= 2.2.0, < 2.2.12.2.1pip install --upgrade "tensorflow-gpu==2.2.1" -
>= 2.3.0, < 2.3.12.3.1pip install --upgrade "tensorflow-gpu==2.3.1"
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 mentioned for this vulnerability.
Conclusion
The vulnerability in TensorFlow GPU versions before 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 can lead to a segmentation fault and program crashes. It is crucial to update to the patched versions to ensure the security and stability of machine learning workflows. For further support on vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow-gpu →
FAQ
TensorFlow GPU is a variant of TensorFlow that utilizes GPU devices for accelerated computation in machine learning tasks.
The tf.raw_ops.Switch operation in TensorFlow GPU takes a tensor and a boolean as input and outputs two tensors. It allows conditional branching in the computation graph.
The vulnerability affects TensorFlow GPU versions before 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1.
The vulnerability can lead to a segmentation fault in TensorFlow GPU, causing the program to crash. This can result in disruption of machine learning workflows and potential data loss.
Yes, the vulnerability has been patched in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1. It is recommended to update to one of these versions to mitigate the risk.
References
Related vulnerabilities
Recent vulnerabilities recorded for tensorflow-gpu.
