CVE-2021-29596: TensorFlow - Division by Zero Error in EmbeddingLookup Operator
Summary
CVE-2021-29596: TensorFlow is an end-to-end open source platform for machine learning. Affects tensorflow 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
< f61c57bd425878be108ec787f4d96390579fb83e
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-2021-29596 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. The EmbeddingLookup operator is a key component in TensorFlow that allows efficient lookup of embeddings in a model. However, a vulnerability has been discovered in the implementation of this operator, which can lead to a division by zero error.
Vulnerability Detail
The vulnerability in the EmbeddingLookup TFLite operator arises from a division by zero error. An attacker can exploit this vulnerability by crafting a model in such a way that the first dimension of the value input is set to 0. This triggers the division by zero error, potentially leading to a crash or other unintended behavior. The vulnerability affects versions of TensorFlow ranging from 0 to 2.4.2, 2.3.3, 2.2.3, and 2.1.4. The TensorFlow team has addressed this issue and the fix will be included in TensorFlow 2.5.0.
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
As a temporary workaround, users can ensure that the first dimension of the value input in their models is not set to 0. By avoiding this specific configuration, the division by zero error can be mitigated. Here is an example of how to modify the value input to avoid the vulnerability:
import tensorflow as tf
# Load the model
model = tf.keras.models.load_model('model.h5')
# Modify the value input
value = tf.constant([[1, 2, 3]]) # Ensure the first dimension is not 0
# Perform the embedding lookup
result = model.layers[0](value)
Conclusion
The vulnerability in the EmbeddingLookup TFLite operator has been addressed by the TensorFlow team. Users are advised to update their TensorFlow installation to version 2.5.0 or one of the fixed versions within the supported range. By applying the recommended fix, the division by zero error can be mitigated. For further support on vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow →
FAQ
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.
The EmbeddingLookup operator is a component in TensorFlow that allows efficient lookup of embeddings in a model. It is commonly used in natural language processing and recommendation systems.
An attacker can exploit this vulnerability by crafting a model in such a way that the first dimension of the value input is set to 0. This triggers a division by zero error, potentially leading to a crash or other unintended behavior.
The vulnerability affects versions of TensorFlow ranging from 0 to 2.4.2, 2.3.3, 2.2.3, and 2.1.4. TensorFlow versions 2.5.0 and above are not affected.
To fix this vulnerability, it is recommended to update TensorFlow to version 2.5.0 once it is released. Alternatively, users can update to TensorFlow 2.4.2, 2.3.3, 2.2.3, or 2.1.4, which include the fix.
References
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