CVE-2021-29613: TensorFlow - Incomplete Validation in CTCLoss

tensorflow Updated 02 Oct 2025

Summary

14/05/2021

Disclosed on

CVE-2021-29613

CVE

Yes i

Fix available

Moderate

Severity

6.3

CVSS Score

CVE-2021-29613: TensorFlow is an end-to-end open source platform for machine learning. Affects tensorflow across 6 version ranges.

< 2.1.4 >= 2.2.0, < 2.2.3 >= 2.3.0, < 2.3.3 >= 2.4.0, < 2.4.2 < 4504a081af71514bb1828048363e6540f797005b < 14607c0707040d775e06b6817325640cb4b5864c

Fixed in 4 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-2021-29613 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 end-to-end open source platform for machine learning. It provides a comprehensive ecosystem of tools, libraries, and resources for developing and deploying machine learning models. TensorFlow is widely used in various domains, including research, industry, and academia. The CTCLoss function in TensorFlow is responsible for computing the Connectionist Temporal Classification (CTC) loss, which is commonly used in sequence-to-sequence models. This vulnerability affects versions 0 to 2.5.0 of TensorFlow and can be exploited to trigger an out-of-bounds read from the heap.

Vulnerability Detail

The vulnerability (CVE-2021-29613) arises from incomplete validation in the tf.raw_ops.CTCLoss function of TensorFlow. An attacker can exploit this vulnerability to read data from the heap outside of its allocated boundaries. This can lead to information disclosure or potentially be leveraged in combination with other vulnerabilities to achieve arbitrary code execution. The impact of this vulnerability depends on the context in which TensorFlow is used and the sensitivity of the data being processed.

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.

< 2.1.4 2.1.4

pip install --upgrade "tensorflow==2.1.4"

>= 2.2.0, < 2.2.3 2.2.3

pip install --upgrade "tensorflow==2.2.3"

>= 2.3.0, < 2.3.3 2.3.3

pip install --upgrade "tensorflow==2.3.3"

>= 2.4.0, < 2.4.2 2.4.2

pip install --upgrade "tensorflow==2.4.2"

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 recommended to apply the available patches or upgrade to the fixed versions of TensorFlow as mentioned in the 'how_to_fix' section.

Conclusion

The vulnerability (CVE-2021-29613) in TensorFlow's CTCLoss function allows an attacker to trigger an out-of-bounds read from the heap. It is crucial to update TensorFlow to version 2.5.0 or apply the available patches for earlier versions to mitigate the risk. Regularly updating TensorFlow and its dependencies is essential for maintaining the security of machine learning applications. For further support on vulnerability remediation, please contact DevNack.com.

See every vulnerability recorded for tensorflow →

FAQ

What is TensorFlow?

TensorFlow is an open source platform for machine learning that provides a comprehensive set of tools and resources for developing and deploying machine learning models.

What is the CTCLoss function in TensorFlow?

The CTCLoss function in TensorFlow is responsible for computing the Connectionist Temporal Classification (CTC) loss, which is commonly used in sequence-to-sequence models.

What is the impact of this vulnerability?

The impact of this vulnerability depends on the context in which TensorFlow is used and the sensitivity of the data being processed. It can potentially lead to information disclosure or arbitrary code execution.

How can I fix this vulnerability?

To fix this vulnerability, it is recommended to update TensorFlow to version 2.5.0. Patches are also available for earlier versions (2.4.2, 2.3.3, 2.2.3, and 2.1.4). Regularly updating TensorFlow and its dependencies is important for applying the latest security fixes.

Is there a workaround for this vulnerability?

There is no known workaround for this vulnerability. It is recommended to apply the available patches or upgrade to the fixed versions of TensorFlow.

References

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