CVE-2021-29612: TensorFlow - Heap Buffer Overflow in BandedTriangularSolve

tensorflow Updated 02 Oct 2025

Summary

14/05/2021

Disclosed on

CVE-2021-29612

CVE

Yes i

Fix available

Low

Severity

3.6

CVSS Score

CVE-2021-29612: TensorFlow is an end-to-end open source platform for machine learning. Affects tensorflow across 6 version ranges.

< 0ab290774f91a23bebe30a358fde4e53ab4876a0 < 2.1.4 >= 2.2.0, < 2.2.3 >= 2.3.0, < 2.3.3 >= 2.4.0, < 2.4.2 < ba6822bd7b7324ba201a28b2f278c29a98edbef2

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-2021-29612 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 building and deploying machine learning models. TensorFlow is widely used in various industries and research domains for tasks such as image recognition, natural language processing, and recommendation systems. However, a vulnerability has been discovered in the implementation of tf.raw_ops.BandedTriangularSolve in TensorFlow.

Vulnerability Detail

The vulnerability in TensorFlow's tf.raw_ops.BandedTriangularSolve stems from a heap buffer overflow in the Eigen implementation. The implementation fails to validate that the two input tensors are not empty, leading to the overflow. Additionally, the validation present in the code is ineffective due to a missing check of the ctx->status() value before continuing execution. An attacker can exploit this vulnerability to trigger the heap buffer overflow, potentially leading to arbitrary code execution or a denial of service attack. The issue affects TensorFlow versions 2.1.4 and above.

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

As a temporary workaround, users can disable or restrict access to the affected functionality in their TensorFlow deployment. This can be achieved by modifying the code or configuration to avoid using the vulnerable tf.raw_ops.BandedTriangularSolve operation. However, it is strongly advised to apply the official fix by upgrading to a patched version of TensorFlow as soon as possible.

Conclusion

The vulnerability in TensorFlow's tf.raw_ops.BandedTriangularSolve can be mitigated by upgrading to the latest patched versions of TensorFlow. It is crucial to promptly apply the fix to ensure the security and stability of machine learning deployments. For further support and assistance with 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 building and deploying machine learning models.

What is `tf.raw_ops.BandedTriangularSolve`?

tf.raw_ops.BandedTriangularSolve is an operation in TensorFlow that performs a banded triangular solve using the Eigen library. It is used in various machine learning algorithms and computations.

What is a heap buffer overflow?

A heap buffer overflow is a type of vulnerability where a program writes data beyond the allocated buffer in the heap memory. This can lead to memory corruption, crashes, and potentially allow an attacker to execute arbitrary code.

What are the potential consequences of this vulnerability?

Exploiting this vulnerability can lead to arbitrary code execution or a denial of service attack. An attacker could potentially gain control over the affected TensorFlow deployment or cause it to crash, disrupting machine learning operations.

How can I check if my TensorFlow installation is affected?

You can use the Vulert Playground at Vulert Scanner to check if your TensorFlow installation is affected by this vulnerability. Simply provide your Pipfile.lock file, and Vulert will analyze it for any open-source vulnerabilities, including this one.

References

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