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

Summaryinfo

A vulnerability identified as critical has been detected in TensorFlow up to 2.2.0/2.3.0. This impacts an unknown function. The manipulation leads to out-of-bounds write. This vulnerability is uniquely identified as CVE-2020-15212. The attack is possible to be carried out remotely. No exploit exists. You should upgrade the affected component.

Detailsinfo

A vulnerability was found in TensorFlow up to 2.2.0/2.3.0 (Artificial Intelligence Software). It has been rated as critical. This issue affects some unknown functionality. The manipulation with an unknown input leads to a out-of-bounds write vulnerability. Using CWE to declare the problem leads to CWE-787. The product writes data past the end, or before the beginning, of the intended buffer. Impacted is 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 writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. 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. 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 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.

The weakness was disclosed 09/25/2020 (GitHub Repository). The advisory is shared at github.com. The identification of this vulnerability is CVE-2020-15212 since 06/25/2020. The attack may be initiated remotely. No form of authentication is needed for a successful exploitation. Neither technical details nor an exploit are 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-162019, VDB-162018, VDB-162016 and VDB-162015 are pretty similar. Several companies clearly confirm that VulDB is the primary source for best 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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Several companies clearly confirm that VulDB is the primary source for best vulnerability data.

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