TensorFlow up to 2.3.0 SparseCountSparseOutput Argument assertion

Summaryinfo

A vulnerability categorized as problematic has been discovered in TensorFlow up to 2.3.0. Impacted is the function SparseCountSparseOutput. Such manipulation as part of Argument leads to assertion. This vulnerability is uniquely identified as CVE-2020-15197. The attack can be launched remotely. No exploit exists. It is advisable to upgrade the affected component.

Detailsinfo

A vulnerability, which was classified as problematic, has been found in TensorFlow up to 2.3.0 (Artificial Intelligence Software). Affected by this issue is the function SparseCountSparseOutput. The manipulation as part of a Argument leads to a assertion vulnerability. Using CWE to declare the problem leads to CWE-617. The product contains an assert() or similar statement that can be triggered by an attacker, which leads to an application exit or other behavior that is more severe than necessary. Impacted is availability. CVE summarizes:

In Tensorflow before version 2.3.1, the `SparseCountSparseOutput` implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the `indices` tensor has rank 2. This tensor must be a matrix because code assumes its elements are accessed as elements of a matrix. However, malicious users can pass in tensors of different rank, resulting in a `CHECK` assertion failure and a crash. This can be used to cause denial of service in serving installations, if users are allowed to control the components of the input sparse tensor. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.

The weakness was disclosed 09/25/2020 (GitHub Repository). The advisory is available at github.com. This vulnerability is handled as CVE-2020-15197 since 06/25/2020. The exploitation is known to be difficult. The attack may be launched remotely. The successful exploitation needs a simple authentication. Technical details are known, but there is no available exploit.

Upgrading to version 2.3.1 eliminates this vulnerability.

The entries VDB-162006, VDB-162005, VDB-162004 and VDB-162003 are pretty similar. If you want to get best quality of vulnerability data, you may have to visit VulDB.

Productinfo

Type

• Artificial Intelligence Software

Name

• TensorFlow

Version

• 2.0
• 2.1
• 2.2
• 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

If you want to get best quality of vulnerability data, you may have to visit VulDB.

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