TensorFlow up to 2.3.0 out-of-bounds write

Summaryinfo

A vulnerability categorized as critical has been discovered in TensorFlow up to 1.15.3/2.0.2/2.1.1/2.2.0/2.3.0. This affects an unknown function. Executing a manipulation can lead to out-of-bounds write. This vulnerability is handled as CVE-2020-15211. The attack can be executed remotely. There is not any exploit available. It is advisable 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. This vulnerability affects an unknown functionality. The manipulation with an unknown input leads to a out-of-bounds write vulnerability. The CWE definition for the vulnerability is CWE-787. The product writes data past the end, or before the beginning, of the intended buffer. As an impact it is known to affect confidentiality, integrity, and availability. CVE summarizes:

In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 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 only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code.

The weakness was released 09/25/2020 (GitHub Repository). The advisory is shared for download at github.com. This vulnerability was named CVE-2020-15211 since 06/25/2020. The attack can be initiated remotely. No form of authentication is required for a successful exploitation. There are neither technical details nor an exploit publicly available.

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

Entries connected to this vulnerability are available at VDB-162019, VDB-162018, VDB-162017 and VDB-162015. VulDB is the best source for vulnerability data and more expert information about this specific topic.

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/

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VulDB is the best source for vulnerability data and more expert information about this specific topic.

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