CVE-2020-15214: Out of bounds write in tensorflow-lite
Impact In TensorFlow Lite models using segment sum can trigger a write out bounds / segmentation fault if the segment ids are not sorted. Code assumes that the segment ids are in increasing order, using the last element of the tensor holding them to determine the dimensionality of output tensor: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/segmentsum.cc#L39-L44
This results in allocating insufficient memory for the output tensor and in a write outside the bounds of the output array: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/internal/reference/referenceops.h#L2625-L2631
This usually results in a segmentation fault, but depending on runtime conditions it can provide for a write gadget to be used in future memory corruption-based 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 sorted, 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 reported by members of the Aivul Team from Qihoo 360.
Other sources
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a write out bounds / segmentation fault if the segment ids are not sorted. Code assumes that the segment ids are in increasing order, using the last element of the tensor holding them to determine the dimensionality of output tensor. This results in allocating insufficient memory for the output tensor and in a write outside the bounds of the output array. This usually results in a segmentation fault, but depending on runtime conditions it can provide for a write gadget to be used in future memory corruption-based exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom Verifier to the model loading code to ensure that the segment ids are sorted, 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.
Affected Software
Remediation
Event History
Frequently Asked Questions
What is CVE-2020-15214?
CVE-2020-15214 is a vulnerability in TensorFlow Lite before versions 2.2.1 and 2.3.1 that can trigger a write out-of-bounds or segmentation fault if the segment ids are not sorted.
How can models using segment sum trigger a vulnerability in TensorFlow Lite?
Models using segment sum in TensorFlow Lite can trigger the vulnerability if the segment ids are not sorted, causing a write out-of-bounds or segmentation fault.
What is the severity of CVE-2020-15214?
The severity of CVE-2020-15214 is high with a score of 8.1.
How do I fix CVE-2020-15214 in TensorFlow Lite?
To fix CVE-2020-15214 in TensorFlow Lite, update to versions 2.2.1 or 2.3.1.
Where can I find more information about CVE-2020-15214?
You can find more information about CVE-2020-15214 at the following references: [Link 1](https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a), [Link 2](https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1), [Link 3](https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p2cq-cprg-frvm).