CVE-2020-15205: Data Leak in Tensorflow

tensorflow Updated 02 Oct 2025

Summary

25/09/2020

Disclosed on

CVE-2020-15205

CVE

Yes i

Fix available

Moderate

Severity

9.0

CVSS Score

CVE-2020-15205: Data leak in Tensorflow. Affects tensorflow across 5 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

Fixed in 5 releases; the minimum fixed version depends on the release line you run. How to fix ↓

Also tracked for

tensorflow · Bitnami
tensorflow-cpu · PyPI
tensorflow-gpu · PyPI

The same CVE-2020-15205 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 resources for building and deploying machine learning models. Tensorflow is widely used in various domains, including research, academia, and industry. However, a vulnerability has been discovered in Tensorflow that can lead to a data leak and potential heap overflow errors.

Vulnerability Detail

The vulnerability, identified as CVE-2020-15205, exists in the data_splits argument of the tf.raw_ops.StringNGrams function in Tensorflow. This argument lacks proper validation, allowing an attacker to pass values that can cause heap overflow errors and leak the contents of memory. By exploiting this vulnerability, an attacker can potentially access sensitive information, defeat Address Space Layout Randomization (ASLR), and execute arbitrary code.

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.

< 1.15.4 1.15.4

pip install --upgrade "tensorflow==1.15.4"

>= 2.0.0, < 2.0.3 2.0.3

pip install --upgrade "tensorflow==2.0.3"

>= 2.1.0, < 2.1.2 2.1.2

pip install --upgrade "tensorflow==2.1.2"

>= 2.2.0, < 2.2.1 2.2.1

pip install --upgrade "tensorflow==2.2.1"

>= 2.3.0, < 2.3.1 2.3.1

pip install --upgrade "tensorflow==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

As a temporary workaround, users can avoid using the tf.raw_ops.StringNGrams function with untrusted input or disable its usage until the patch is applied. It is important to note that workarounds may not provide complete protection and upgrading to the patched versions is the recommended solution.

Conclusion

CVE-2020-15205 is a critical vulnerability in Tensorflow that allows for a data leak and potential heap overflow errors. It is crucial to upgrade to the patched versions of Tensorflow (1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1) to mitigate the risk. Workarounds may provide temporary protection, but they are not a substitute for applying the patches. For further support on vulnerability remediation, please contact DevNack.com.

See every vulnerability recorded for tensorflow →

FAQ

What is the impact of this vulnerability?

The vulnerability allows an attacker to leak the contents of memory, potentially accessing sensitive information and defeating ASLR. This can lead to unauthorized access, data breaches, and remote code execution.

How can I check if my application is affected?

You can use Vulert's playground at Vulert Scanner to check if your application is affected by this vulnerability. Vulert monitors and alerts in real-time for open-source vulnerabilities, using only the Pipfile.lock file.

Are there any known exploits for this vulnerability?

At the time of this advisory, there are no known public exploits for this vulnerability. However, it is important to apply the patches as soon as possible to mitigate the risk.

Can this vulnerability be exploited remotely?

Yes, this vulnerability can be exploited remotely. An attacker can send specially crafted input to the vulnerable function, leading to a data leak and potential code execution.

Is there any additional information available?

For more information, please refer to the TensorFlow security guide at https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md. This guide provides details about the security model, how to contact the TensorFlow team with issues, and general security best practices.

References

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