TensorFlow up to 2.3.0 tf.raw_ops.Switch input validation

Summaryinfo

A vulnerability, which was classified as problematic, has been found in TensorFlow up to 1.15.3/2.0.2/2.1.1/2.2.0/2.3.0. This impacts the function tf.raw_ops.Switch. This manipulation causes input validation. This vulnerability is registered as CVE-2020-15190. Remote exploitation of the attack is possible. No exploit is available. It is advisable to upgrade the affected component.

Detailsinfo

A vulnerability has been found in TensorFlow up to 1.15.3/2.0.2/2.1.1/2.2.0/2.3.0 (Artificial Intelligence Software) and classified as problematic. Affected by this vulnerability is the function tf.raw_ops.Switch. The manipulation with an unknown input leads to a input validation vulnerability. The CWE definition for the vulnerability is CWE-20. The product receives input or data, but it does not validate or incorrectly validates that the input has the properties that are required to process the data safely and correctly. As an impact it is known to affect availability. The summary by CVE is:

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `tf.raw_ops.Switch` operation takes as input a tensor and a boolean and outputs two tensors. Depending on the boolean value, one of the tensors is exactly the input tensor whereas the other one should be an empty tensor. However, the eager runtime traverses all tensors in the output. Since only one of the tensors is defined, the other one is `nullptr`, hence we are binding a reference to `nullptr`. This is undefined behavior and reported as an error if compiling with `-fsanitize=null`. In this case, this results in a segmentation fault The issue is patched in commit da8558533d925694483d2c136a9220d6d49d843c, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

The weakness was published 09/25/2020 (GitHub Repository). It is possible to read the advisory at github.com. This vulnerability is known as CVE-2020-15190 since 06/25/2020. The attack can be launched remotely. The exploitation doesn't need any form of authentication. Technical details of the vulnerability are known, but there is no available exploit.

Upgrading to version 1.15.4, 2.0.3, 2.1.2, 2.2.1 or 2.3.1 eliminates this vulnerability.

Similar entries are available at VDB-161999, VDB-161998, VDB-161997 and VDB-161996. Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.

Productinfo

Type

• Artificial Intelligence Software

Name

• TensorFlow

Version

• 1.15.0
• 1.15.1
• 1.15.2
• 1.15.3
• 2.0
• 2.0.0
• 2.0.1
• 2.0.2
• 2.1
• 2.1.0
• 2.1.1
• 2.2
• 2.2.0
• 2.3.0

License

• free

Website

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

CPE 2.3info

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CPE 2.2info

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Entryinfo

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