TensorFlow up to 2.3.0 Shard API Argument numeric truncation error

Summaryinfo

A vulnerability classified as critical has been found in TensorFlow up to 1.15.3/2.0.2/2.1.1/2.2.0/2.3.0. Affected is an unknown function of the component Shard API. This manipulation as part of Argument causes numeric truncation error. This vulnerability is tracked as CVE-2020-15202. The attack is possible to be carried out remotely. No exploit exists. It is recommended to upgrade the affected component.

Detailsinfo

A vulnerability was found in TensorFlow up to 1.15.3/2.0.2/2.1.1/2.2.0/2.3.0 (Artificial Intelligence Software). It has been declared as critical. Affected by this vulnerability is some unknown processing of the component Shard API. The manipulation as part of a Argument leads to a numeric truncation error vulnerability. The CWE definition for the vulnerability is CWE-197. Truncation errors occur when a primitive is cast to a primitive of a smaller size and data is lost in the conversion. As an impact it is known to affect confidentiality, integrity, and 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 `Shard` API in TensorFlow expects the last argument to be a function taking two `int64` (i.e., `long long`) arguments. However, there are several places in TensorFlow where a lambda taking `int` or `int32` arguments is being used. In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption. The issue is patched in commits 27b417360cbd671ef55915e4bb6bb06af8b8a832 and ca8c013b5e97b1373b3bb1c97ea655e69f31a575, 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 disclosed 09/25/2020 (GitHub Repository). It is possible to read the advisory at github.com. This vulnerability is known as CVE-2020-15202 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 1.15.4, 2.0.3, 2.1.2, 2.2.1 or 2.3.1 eliminates this vulnerability. Applying the patch 27b417360cbd671ef55915e4bb6bb06af8b8a832/ca8c013b5e97b1373b3bb1c97ea655e69f31a575 is able to eliminate this problem. The best possible mitigation is suggested to be upgrading to the latest version.

The entries VDB-162011, VDB-162010, VDB-162009 and VDB-162008 are pretty similar. Be aware that VulDB is the high quality source 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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