TensorFlow up to 2.2.0/2.3.0 out-of-bounds write

Summaryinfo

A vulnerability marked as critical has been reported in TensorFlow up to 2.2.0/2.3.0. Affected by this vulnerability is an unknown functionality. This manipulation causes out-of-bounds write. The identification of this vulnerability is CVE-2020-15214. It is possible to initiate the attack remotely. There is no exploit available. It is suggested to upgrade the affected component.

Detailsinfo

A vulnerability classified as critical was found in TensorFlow up to 2.2.0/2.3.0 (Artificial Intelligence Software). Affected by this vulnerability is an unknown code. The manipulation with an unknown input leads to a out-of-bounds write vulnerability. The CWE definition for the vulnerability is CWE-787. The product writes data past the end, or before the beginning, of the intended buffer. As an impact it is known to affect confidentiality, integrity, and availability. The summary by CVE is:

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.

The weakness was shared 09/25/2020 (GitHub Repository). It is possible to read the advisory at github.com. This vulnerability is known as CVE-2020-15214 since 06/25/2020. The attack can be launched remotely. The exploitation doesn't need any form of authentication. The technical details are unknown and an exploit is not publicly available.

Upgrading to version 2.2.1 or 2.3.1 eliminates this vulnerability. Applying the patch 204945b19e44b57906c9344c0d00120eeeae178a is able to eliminate this problem. The best possible mitigation is suggested to be upgrading to the latest version.

The entries VDB-162018, VDB-162017, VDB-162016 and VDB-162015 are related to this item. Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.

Productinfo

Type

• Artificial Intelligence Software

Name

• TensorFlow

Version

• 2.0
• 2.1
• 2.2
• 2.2.0
• 2.3.0

License

• free

Website

• Product: https://github.com/tensorflow/tensorflow/

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