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TensorFlowGHSA-hx2x-85gr-wrpq

Out of bounds access in tensorflow-lite

Critical8.1CVE-2020-15212 · Published Sep 25, 2020 · updated Jul 8, 2026

### Impact In TensorFlow Lite models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/internal/reference/reference_ops.h#L2625-L2631 Users having access to `segment_ids_data` can alter `output_index` and then write to outside of `output_data` buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. ### Patches We have patched the issue in 204945b and will release patch releases for all affected versions. We recommend users to upgrade to TensorFlow 2.2.1, or 2.3.1. ### Workarounds A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a ...

GitHub advisory

Affected versions

PackageAffectedFixed in
tensorflow
PyPI
>= 2.2.0, < 2.2.12.2.1
>= 2.3.0, < 2.3.12.3.1
Details and references

### Impact In TensorFlow Lite models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/internal/reference/reference_ops.h#L2625-L2631 Users having access to `segment_ids_data` can alter `output_index` and then write to outside of `output_data` buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. ### Patches We have patched the issue in 204945b and will release patch releases for all affected versions. We recommend users to upgrade to TensorFlow 2.2.1, or 2.3.1. ### Workarounds A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code. ### For more information Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. ### Attribution This vulnerability has been discovered from a variant analysis of [GHSA-p2cq-cprg-frvm](https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p2cq-cprg-frvm).

CVSS 3.1
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:L/I:L/A:H
Severity from
GitHub (reviewed advisory)
Weakness
CWE-787
Also known as
BIT-tensorflow-2020-15212, CVE-2020-15212, PYSEC-2020-135, PYSEC-2020-292, PYSEC-2020-327

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