CVE-2020-15204: TensorFlow CPU - Null Pointer Dereference in Eager Mode

tensorflow-cpu Updated 02 Oct 2025

Summary

25/09/2020

Disclosed on

CVE-2020-15204

CVE

Yes i

Fix available

Unrated

Severity

Not published

CVSS Score

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-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 < 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-gpu · 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 is an open-source machine learning framework developed by Google. It provides a comprehensive ecosystem of tools, libraries, and community resources for building and deploying machine learning models. TensorFlow CPU is a specific package within TensorFlow that focuses on running TensorFlow on CPUs. It is widely used in various industries and research domains for training and inference tasks.

Vulnerability Detail

The vulnerability (CVE-2020-15204) in TensorFlow CPU 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 was 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: 2.21.0 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.

< 1.15.4 1.15.4

pip install --upgrade "tensorflow-cpu==1.15.4"

>= 2.0.0, < 2.0.3 2.0.3

pip install --upgrade "tensorflow-cpu==2.0.3"

>= 2.1.0, < 2.1.2 2.1.2

pip install --upgrade "tensorflow-cpu==2.1.2"

>= 2.2.0, < 2.2.1 2.2.1

pip install --upgrade "tensorflow-cpu==2.2.1"

>= 2.3.0, < 2.3.1 2.3.1

pip install --upgrade "tensorflow-cpu==2.3.1"

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Workaround

There is no specific workaround mentioned for this vulnerability.

Conclusion

The vulnerability in TensorFlow CPU (CVE-2020-15204) can lead to a null pointer dereference in eager mode, potentially resulting in a denial of service (DoS) attack. It is crucial to update TensorFlow CPU to one of the patched versions (1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1) to mitigate the risk. For further support on vulnerability remediation, you can reach out to DevNack.com.

See every vulnerability recorded for tensorflow-cpu →

FAQ

What is TensorFlow CPU?

TensorFlow CPU is a package within TensorFlow that focuses on running TensorFlow on CPUs. It provides the necessary components and optimizations for efficient execution on CPU architectures.

What is eager mode in TensorFlow?

Eager mode in TensorFlow allows for immediate execution of operations, providing a more intuitive and interactive programming experience. It enables users to evaluate TensorFlow operations and access their results directly.

What is a null pointer dereference?

A null pointer dereference occurs when a program attempts to access or dereference a null pointer, which does not point to a valid memory location. This can lead to crashes, segmentation faults, or other unexpected behavior.

What are the potential consequences of this vulnerability?

The vulnerability can result in a segmentation fault, leading to a denial of service (DoS) attack. This can disrupt the availability and functionality of TensorFlow CPU, impacting any applications or systems relying on it for machine learning tasks.

How can I check if my application is affected by this vulnerability?

You can use Vulert's playground (https://vulert.com/scanner) to check if your application is affected by this vulnerability. Vulert monitors and alerts in real-time for open-source vulnerabilities, using just the Pipfile.lock file as input.

References

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