TensorFlow up to 2.3.0 out-of-bounds write

Summaryinfo

A vulnerability was found in TensorFlow up to 1.15.3/2.0.2/2.1.1/2.2.0/2.3.0. It has been classified as critical. Impacted is an unknown function. This manipulation causes out-of-bounds write. This vulnerability appears as CVE-2020-15208. The attack may be initiated remotely. There is no available exploit. Upgrading the affected component is recommended.

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 critical. Affected by this vulnerability is an unknown code block. 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. The summary by CVE is:

In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when determining the common dimension size of two tensors, TFLite uses a `DCHECK` which is no-op outside of debug compilation modes. Since the function always returns the dimension of the first tensor, malicious attackers can craft cases where this is larger than that of the second tensor. In turn, this would result in reads/writes outside of bounds since the interpreter will wrongly assume that there is enough data in both tensors. The issue is patched in commit 8ee24e7949a203d234489f9da2c5bf45a7d5157d, 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 presented 09/25/2020 (GitHub Repository). The advisory is shared at github.com. This vulnerability is known as CVE-2020-15208 since 06/25/2020. The attack can be launched remotely. The exploitation doesn't need any form of authentication. Neither technical details nor an exploit are 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 8ee24e7949a203d234489f9da2c5bf45a7d5157d is able to eliminate this problem. The best possible mitigation is suggested to be upgrading to the latest version.

See VDB-162017, VDB-162016, VDB-162015 and VDB-162014 for similar entries. 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

• 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

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

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