TensorFlow up to 2.3.0 RaggedCountSparseOutput Argument heap-based overflow

Summaryinfo

A vulnerability marked as critical has been reported in TensorFlow up to 2.3.0. This affects the function RaggedCountSparseOutput. The manipulation as part of Argument leads to heap-based overflow. This vulnerability is referenced as CVE-2020-15200. Remote exploitation of the attack is possible. No exploit is available. It is suggested to upgrade the affected component.

Detailsinfo

A vulnerability was found in TensorFlow up to 2.3.0 (Artificial Intelligence Software) and classified as critical. This issue affects the function RaggedCountSparseOutput. The manipulation as part of a Argument leads to a heap-based overflow vulnerability. Using CWE to declare the problem leads to CWE-122. A heap overflow condition is a buffer overflow, where the buffer that can be overwritten is allocated in the heap portion of memory, generally meaning that the buffer was allocated using a routine such as malloc(). Impacted is confidentiality, integrity, and availability. The summary by CVE is:

In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the `splits` tensor generate a valid partitioning of the `values` tensor. Thus, the code sets up conditions to cause a heap buffer overflow. A `BatchedMap` is equivalent to a vector where each element is a hashmap. However, if the first element of `splits_values` is not 0, `batch_idx` will never be 1, hence there will be no hashmap at index 0 in `per_batch_counts`. Trying to access that in the user code results in a segmentation fault. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.

The weakness was published 09/25/2020 (GitHub Repository). The advisory is shared at github.com. The identification of this vulnerability is CVE-2020-15200 since 06/25/2020. The attack may be initiated remotely. No form of authentication is needed for a successful exploitation. Technical details are known, but no exploit is available.

Upgrading to version 2.3.1 eliminates this vulnerability. Applying the patch 3cbb917b4714766030b28eba9fb41bb97ce9ee02 is able to eliminate this problem. The best possible mitigation is suggested to be upgrading to the latest version.

Similar entries are available at VDB-162009, VDB-162008, VDB-162007 and VDB-162006. If you want to get the best quality for vulnerability data then you always have to consider 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/

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If you want to get the best quality for vulnerability data then you always have to consider VulDB.

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