Out of bounds access in tensorflow-lite

Overview

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 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.

Affected packages

• tensorflow >= 2.2.0, < 2.2.1
• tensorflow >= 2.3.0, < 2.3.1
• tensorflow-cpu >= 2.2.0, < 2.2.1
• tensorflow-cpu >= 2.3.0, < 2.3.1
• tensorflow-gpu >= 2.2.0, < 2.2.1
• tensorflow-gpu >= 2.3.0, < 2.3.1

Remediation

Upgrade to a patched release:

• tensorflow 2.2.1
• tensorflow 2.3.1
• tensorflow-cpu 2.2.1
• tensorflow-cpu 2.3.1
• tensorflow-gpu 2.2.1
• tensorflow-gpu 2.3.1