CVE-2020-15212: Out of Bounds Access in TensorFlow Lite
Summary
CVE-2020-15212: Out of bounds access in tensorflow-lite. Affects tensorflow across 2 version ranges.
>= 2.2.0, < 2.2.1
>= 2.3.0, < 2.3.1
Fixed in 2.2.1 or 2.3.1, depending on the release line you run. How to fix ↓
Also tracked for
- tensorflow · Bitnami
- tensorflow-cpu · PyPI
- tensorflow-gpu · PyPI
The same CVE-2020-15212 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 Lite is an open-source deep learning framework designed for mobile and embedded devices. It allows developers to deploy machine learning models on resource-constrained platforms. TensorFlow Lite provides efficient inference with low latency and small model sizes. However, a vulnerability has been discovered in TensorFlow Lite that can lead to out of bounds access.
Vulnerability Detail
The vulnerability in TensorFlow Lite arises when using segment sum, which can trigger writes outside of the bounds of heap allocated buffers. By inserting negative elements in the segment ids tensor, users with access to segment_ids_data can alter output_index and write outside of the output_data buffer. This can result in a segmentation fault and potentially be exploited to corrupt memory or create advanced exploits. The vulnerability has been patched in TensorFlow versions 2.2.1 and 2.3.1.
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.
-
>= 2.2.0, < 2.2.12.2.1pip install --upgrade "tensorflow==2.2.1" -
>= 2.3.0, < 2.3.12.3.1pip install --upgrade "tensorflow==2.3.1"
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Workaround
A potential workaround is to add a custom Verifier to the model loading code to ensure that the segment ids are all positive. This workaround can handle cases where the segment ids are stored statically in the model. If the segment ids are generated at runtime between inference steps, a similar validation can be performed. However, if the segment ids are generated as outputs of a tensor during inference steps, there are no possible workarounds, and upgrading to the patched code is advised.
Conclusion
The vulnerability in TensorFlow Lite that allows out of bounds access has been addressed in patched versions 2.2.1 and 2.3.1. It is recommended to upgrade to these versions to mitigate the vulnerability. For further support on vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow →
FAQ
TensorFlow Lite is an open-source deep learning framework designed for mobile and embedded devices. It allows developers to deploy machine learning models on resource-constrained platforms.
The vulnerability in TensorFlow Lite can lead to out of bounds access, potentially resulting in a segmentation fault and memory corruption. It can also be exploited in combination with other vulnerabilities to create more advanced exploits.
To fix the vulnerability, it is recommended to upgrade to TensorFlow 2.2.1 or 2.3.1, which contain the necessary patches to address the issue.
A potential workaround is to add a custom Verifier to the model loading code to ensure that the segment ids are all positive. However, this workaround only handles cases where the segment ids are stored statically in the model. If the segment ids are generated at runtime between inference steps or as outputs of a tensor during inference steps, there are no possible workarounds.
For more information regarding the security model of TensorFlow Lite and how to contact the TensorFlow team with issues and questions, please consult the TensorFlow security guide.
References
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