CVE-2020-15213

Advisory

Tensorflow-rocm versions 2.2.1 and 2.3.1 includes a fix for CVE-2020-15213: In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimension of the output tensor, attackers can use a very large value to trigger a large allocation. The issue was patched in commit 204945b19e44b57906c9344c0d00120eeeae178a. A potential workaround is to add a custom "Verifier" to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, 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.
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hjmq-236j-8m87

Affected package

Affected versions

Fixed versions

Vulnerability changelog

This vulnerability has no description

Resources

• https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15213
• https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a
• https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1
• https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hjmq-236j-8m87