CVE-2020-15206: TensorFlow-CPU - Denial of Service Vulnerability
Summary
CVE-2020-15206: In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, changing the TensorFlow's SavedModel protocol buffer and altering the name of required keys results in segfaults and data corruption while loading the... Affects tensorflow-cpu 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
< adf095206f25471e864a8e63a0f1caef53a0e3a6
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-gpu · PyPI
- tensorflow · PyPI
The same CVE-2020-15206 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-CPU is a specific package that focuses on running TensorFlow on CPUs. This vulnerability affects versions before 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1. The vulnerability allows an attacker to cause a denial of service by manipulating the TensorFlow's SavedModel protocol buffer and altering the name of required keys, resulting in segfaults and data corruption while loading the model. This can impact products using tensorflow-serving or other inference-as-a-service installations.
Vulnerability Detail
The vulnerability in TensorFlow-CPU arises from a flaw in the handling of SavedModel protocol buffer. By modifying the required keys' names, an attacker can trigger segfaults and data corruption during model loading. This can lead to a denial of service, rendering products using tensorflow-serving or similar inference-as-a-service setups unavailable. The vulnerability has the potential to disrupt machine learning workflows and impact the availability of services relying on TensorFlow-CPU. The issue was addressed 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: 2.21.0 as of 22 Oct 2026
pip install --upgrade "tensorflow-cpu==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.
-
< 1.15.41.15.4pip install --upgrade "tensorflow-cpu==1.15.4" -
>= 2.0.0, < 2.0.32.0.3pip install --upgrade "tensorflow-cpu==2.0.3" -
>= 2.1.0, < 2.1.22.1.2pip install --upgrade "tensorflow-cpu==2.1.2" -
>= 2.2.0, < 2.2.12.2.1pip install --upgrade "tensorflow-cpu==2.2.1" -
>= 2.3.0, < 2.3.12.3.1pip install --upgrade "tensorflow-cpu==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 known workaround for this vulnerability. It is essential to update TensorFlow-CPU to the patched versions mentioned above to address the issue effectively.
Conclusion
The vulnerability in TensorFlow-CPU can result in a denial of service, impacting products using tensorflow-serving or other inference-as-a-service installations. To address this issue, it is crucial to update TensorFlow-CPU to versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. This will ensure the security and stability of systems relying on TensorFlow-CPU. For further support and assistance in vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow-cpu →
FAQ
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-CPU is a specific package that focuses on running TensorFlow on CPUs. It is designed to provide efficient performance on systems without dedicated GPUs.
SavedModel is a serialization format used by TensorFlow to save and load models. It provides a language-agnostic way to store trained models and their associated metadata.
An attacker can exploit this vulnerability by manipulating the TensorFlow's SavedModel protocol buffer and altering the name of required keys. This can lead to segfaults and data corruption during model loading, causing a denial of service.
Yes, the vulnerability has been fixed 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 these versions to mitigate the issue.
References
Related vulnerabilities
Recent vulnerabilities recorded for tensorflow-cpu.
