CVE-2021-29522: TensorFlow GPU - Division by Zero Denial of Service
Summary
CVE-2021-29522: TensorFlow is an end-to-end open source platform for machine learning. Affects tensorflow-gpu across 5 version ranges.
< 2.1.4
>= 2.2.0, < 2.2.3
>= 2.3.0, < 2.3.3
>= 2.4.0, < 2.4.2
< 311403edbc9816df80274bd1ea8b3c0c0f22c3fa
Fixed in 4 releases; the minimum fixed version depends on the release line you run. How to fix ↓
Also tracked for
- tensorflow · Bitnami
- tensorflow · PyPI
- tensorflow · PyPI
- tensorflow-cpu · PyPI
The same CVE-2021-29522 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 GPU is a variant of TensorFlow that leverages the power of GPUs for accelerated computation. It is widely used in various industries and research domains for training and inference of deep learning models.
Vulnerability Detail
The vulnerability (CVE-2021-29522) exists in the tf.raw_ops.Conv3DBackprop* operations of TensorFlow GPU. These operations fail to validate the input tensors, allowing an attacker to provide empty inputs. As a result, a division by zero error occurs during the computation of shard size, leading to a denial of service (DoS) condition. The issue originates from a lack of proper input validation and divisor checking in the implementation of the affected operations. An attacker with control over the input sizes can exploit this vulnerability to trigger the DoS condition, disrupting the availability of the TensorFlow GPU system. The impact of this vulnerability can be severe, especially in production environments where TensorFlow GPU is used for critical machine learning tasks.
How To Fix
Latest published version: 2.12.0 as of 22 Oct 2026
pip install --upgrade "tensorflow-gpu==2.12.0"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-gpu==2.1.4" -
>= 2.2.0, < 2.2.32.2.3pip install --upgrade "tensorflow-gpu==2.2.3" -
>= 2.3.0, < 2.3.32.3.3pip install --upgrade "tensorflow-gpu==2.3.3" -
>= 2.4.0, < 2.4.22.4.2pip install --upgrade "tensorflow-gpu==2.4.2"
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Workaround
There is no known workaround for this vulnerability. It is essential to apply the provided fix or upgrade to a fixed version of TensorFlow GPU to address the issue.
Conclusion
The vulnerability (CVE-2021-29522) in TensorFlow GPU poses a risk of division by zero denial of service. It is crucial to update to the fixed versions of TensorFlow GPU (2.5.0 or later, or the backported versions) to mitigate this vulnerability. Regularly updating TensorFlow GPU and other dependencies is recommended to stay protected against known vulnerabilities. For further support on vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow-gpu →
FAQ
TensorFlow GPU is a variant of TensorFlow that utilizes GPUs for accelerated computation, enabling faster training and inference of deep learning models.
This vulnerability allows an attacker to trigger a division by zero error, leading to a denial of service condition in TensorFlow GPU. It can disrupt the availability of the system and impact critical machine learning tasks.
To fix this vulnerability, it is recommended to upgrade TensorFlow GPU to version 2.5.0 or later. Alternatively, you can update to one of the backported fixed versions: 2.4.2, 2.3.3, 2.2.3, or 2.1.4.
No, there are no known workarounds for this vulnerability. Applying the provided fix or upgrading to a fixed version is necessary.
No, this vulnerability requires an attacker to have control over the input sizes provided to the affected TensorFlow GPU operations. It cannot be exploited remotely without direct access to the system.
References
Related vulnerabilities
Recent vulnerabilities recorded for tensorflow-gpu.
