CVE-2020-15211: TensorFlow Lite - Double Indexing Vulnerability
Summary
CVE-2020-15211: In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each... Affects tensorflow-cpu across 11 version ranges.
< 1.15.4
>= 2.0.0, < 2.0.3
>= 2.1.0, < 2.1.2
>= 2.2.0, < 2.2.1
>= 2.3.0, < 2.3.1
< 46d5b0852528ddfd614ded79bccc75589f801bd9
< 1970c2158b1ffa416d159d03c3370b9a462aee35
< 00302787b788c5ff04cb6f62aed5a74d936e86c0
< cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f
< e11f55585f614645b360563072ffeb5c3eeff162
< fff2c8326280c07733828f990548979bdc893859
Fixed in 5 releases; the minimum fixed version depends on the release line you run. How to fix ↓
Also tracked for
- tensorflow · Bitnami
- tensorflow · PyPI
- tensorflow-gpu · PyPI
- tensorflow · PyPI
The same CVE-2020-15211 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 developed by Google. It is designed to run machine learning models on resource-constrained devices such as mobile phones and embedded systems. TensorFlow Lite models are saved in the flatbuffer format, which uses a double indexing scheme for subgraphs, operators, and input/output tensors. This vulnerability affects TensorFlow Lite versions before 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1.
Vulnerability Detail
The vulnerability in TensorFlow Lite arises from the use of a negative -1 value as an index for optional tensors in the flatbuffer model. This allows writing and reading from outside the bounds of heap allocated arrays, albeit at a specific offset from the start of these arrays. While this results in both read and write gadgets, the impact is limited. An attacker could potentially exploit this vulnerability to manipulate or access data outside the intended boundaries of the model. The issue has been patched in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 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-cpu==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.
-
< 1.15.41.15.4pip install --upgrade "tensorflow-cpu==1.15.4" -
>= 2.0.0, < 2.0.32.0.3pip install --upgrade "tensorflow-cpu==2.0.3" -
>= 2.1.0, < 2.1.22.1.2pip install --upgrade "tensorflow-cpu==2.1.2" -
>= 2.2.0, < 2.2.12.2.1pip install --upgrade "tensorflow-cpu==2.2.1" -
>= 2.3.0, < 2.3.12.3.1pip install --upgrade "tensorflow-cpu==2.3.1"
Tip — Not sure which release line you're on? Vulert can find the fix for the version you are using. Scan free →
Workaround
A potential workaround for this vulnerability is to add a custom Verifier to the model loading code. This custom Verifier should ensure that only operators which accept optional inputs use the -1 special value, and only for the tensors that they expect to be optional. However, this approach is error-prone, and it is recommended to upgrade to the patched versions instead.
Conclusion
The double indexing vulnerability in TensorFlow Lite (CVE-2020-15211) allows for potential data manipulation or unauthorized access outside the intended boundaries of the model. It is crucial to upgrade to the patched versions of TensorFlow Lite (1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1) to mitigate this vulnerability. For further support on vulnerability remediation, contact DevNack.com.
See every vulnerability recorded for tensorflow-cpu →
FAQ
TensorFlow Lite is an open-source deep learning framework developed by Google. It is designed to run machine learning models on resource-constrained devices such as mobile phones and embedded systems.
The vulnerability affects TensorFlow Lite versions before 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1.
The vulnerability allows writing and reading from outside the bounds of heap allocated arrays, albeit at a specific offset. While this results in both read and write gadgets, the impact is limited.
To fix this vulnerability, it is recommended to upgrade to the patched versions of TensorFlow Lite (1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1). Update the TensorFlow Lite package in your project to ensure that the vulnerability is addressed.
A potential workaround is to add a custom Verifier to the model loading code. However, this approach is error-prone, and it is recommended to upgrade to the patched versions instead.
References
Related vulnerabilities
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