CVE-2020-15204: TensorFlow GPU - Null Pointer Dereference in Eager Mode
Summary
CVE-2020-15204: In eager mode, TensorFlow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 does not set the session state. 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
< 9a133d73ae4b4664d22bd1aa6d654fec13c52ee1
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-2020-15204 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 GPU 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 leverages the power of GPUs to accelerate computations and improve performance in deep learning tasks.
Vulnerability Detail
The vulnerability (CVE-2020-15204) in TensorFlow GPU affects versions before 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1. In eager mode, the session state is not properly set, leading to a null pointer dereference when calling tf.raw_ops.GetSessionHandle or tf.raw_ops.GetSessionHandleV2. This results in a segmentation fault and can potentially lead to a denial of service (DoS) attack. The issue has been patched in commit 9a133d73ae4b4664d22bd1aa6d654fec13c52ee1 and is 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: 2.12.0 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 known workaround for this vulnerability. It is essential to update to the patched versions mentioned above to mitigate the risk.
Conclusion
The vulnerability in TensorFlow GPU (CVE-2020-15204) can be mitigated by updating to the patched versions. It is crucial to keep software dependencies up to date to ensure the security and stability of machine learning applications. For further support on vulnerability remediation, you can reach out to DevNack.com.
See every vulnerability recorded for tensorflow-gpu →
FAQ
TensorFlow GPU is an open-source machine learning framework developed by Google that utilizes GPUs for accelerated computations.
The vulnerability can lead to a null pointer dereference and a segmentation fault, potentially resulting in a denial of service (DoS) attack.
To fix the vulnerability, update TensorFlow GPU to one of the patched versions: 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
No, there is no known workaround for this vulnerability. Updating to the patched versions is the recommended solution.
You can find more information about this vulnerability in the official TensorFlow repository and the CVE database.
References
Related vulnerabilities
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