CVE-2021-29516: Null Pointer Dereference in TensorFlow
Summary
CVE-2021-29516: Null pointer dereference via invalid Ragged Tensors. Affects tensorflow across 4 version ranges.
< 2.1.4
>= 2.2.0, < 2.2.3
>= 2.3.0, < 2.3.3
>= 2.4.0, < 2.4.2
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-2021-29516 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 widely used for building and training various types of neural networks. It provides a comprehensive set of tools and libraries for numerical computation and data flow graphs. TensorFlow is vulnerable to a null pointer dereference issue when processing invalid ragged tensors.
Vulnerability Detail
The vulnerability arises when calling tf.raw_ops.RaggedTensorToVariant with arguments specifying an invalid ragged tensor. This can lead to a null pointer dereference, resulting in a crash or potential code execution. The issue is caused by the lack of validation for empty ragged tensors in the implementation of RaggedTensorToVariant operations. As a result, the code attempts to access a null vector, leading to a null pointer dereference.
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.
-
< 2.1.42.1.4pip install --upgrade "tensorflow==2.1.4" -
>= 2.2.0, < 2.2.32.2.3pip install --upgrade "tensorflow==2.2.3" -
>= 2.3.0, < 2.3.32.3.3pip install --upgrade "tensorflow==2.3.3" -
>= 2.4.0, < 2.4.22.4.2pip install --upgrade "tensorflow==2.4.2"
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Workaround
There is no known workaround for this vulnerability. It is recommended to update to the patched versions of TensorFlow as mentioned in the 'how_to_fix' section.
Conclusion
The null pointer dereference vulnerability in TensorFlow can be mitigated by updating to the patched versions of the framework. It is crucial to keep software dependencies up to date to ensure the security and stability of machine learning applications. For further support and assistance in vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow →
FAQ
The vulnerability can lead to a null pointer dereference, resulting in a crash or potential code execution. An attacker may be able to exploit this vulnerability to execute arbitrary code or cause a denial-of-service condition.
The vulnerability affects TensorFlow versions 2.1.4, 2.2.3, 2.3.3, and 2.4.2. These versions have been patched in TensorFlow 2.5.0 and the backported versions mentioned in the 'how_to_fix' section.
Yes, the CVSS score for this vulnerability is 2.5, indicating a low severity level.
There are no known exploits for this vulnerability at the time of this report. However, it is recommended to update to the patched versions of TensorFlow to prevent any potential exploitation.
For more information, please refer to the TensorFlow security guide and the GitHub advisory database linked in the vulnerability details section.
References
Related vulnerabilities
Recent vulnerabilities recorded for tensorflow.
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