CVE-2020-15211: TensorFlow Lite Flatbuffer Model 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-gpu 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-cpu · 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 designed for mobile and embedded devices. It allows developers to deploy machine learning models on resource-constrained platforms. TensorFlow Lite models are saved in the flatbuffer format, which uses a double indexing scheme to represent 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 flatbuffer format in TensorFlow Lite uses a negative -1 value as an index for optional tensors. However, this index is also considered valid for operators that don't expect optional inputs and even for output tensors. As a result, it allows read and write operations outside the bounds of heap allocated arrays, albeit with limited scope. An attacker could exploit this vulnerability to manipulate data or execute arbitrary code. 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: 2.12.0 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.
-
< 1.15.41.15.4pip install --upgrade "tensorflow-gpu==1.15.4" -
>= 2.0.0, < 2.0.32.0.3pip install --upgrade "tensorflow-gpu==2.0.3" -
>= 2.1.0, < 2.1.22.1.2pip install --upgrade "tensorflow-gpu==2.1.2" -
>= 2.2.0, < 2.2.12.2.1pip install --upgrade "tensorflow-gpu==2.2.1" -
>= 2.3.0, < 2.3.12.3.1pip install --upgrade "tensorflow-gpu==2.3.1"
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Workaround
A potential workaround is to add a custom Verifier to the model loading code. This verifier should ensure that only operators that accept optional inputs use the -1 special value and only for the tensors they expect to be optional. However, this approach is error-prone, and it is recommended to upgrade to the patched code instead.
Conclusion
The TensorFlow Lite Flatbuffer Model Double Indexing Vulnerability allows for read and write operations outside the bounds of heap allocated arrays. It is crucial to upgrade to the patched versions of TensorFlow Lite to mitigate this vulnerability. For further support on vulnerability remediation, contact DevNack.com.
See every vulnerability recorded for tensorflow-gpu →
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 flatbuffer format is used to save TensorFlow Lite models. It uses a double indexing scheme to represent subgraphs, operators, and input/output tensors.
This vulnerability allows read and write operations outside the bounds of heap allocated arrays, although with limited scope. An attacker could manipulate data or execute arbitrary code.
To fix this vulnerability, upgrade to the patched versions of TensorFlow Lite (1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1).
A potential workaround is to add a custom Verifier to the model loading code. However, it is recommended to upgrade to the patched code instead.
References
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