CVE-2020-15213 Detail
Modified
This vulnerability has been modified since it was last analyzed by the NVD. It is awaiting reanalysis which may result in further changes to the information provided. DescriptionIn TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code. Metrics
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CVSS 4.0 Severity and Vector Strings:
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Weakness Enumeration
Known Affected Software Configurations Switch to CPE 2.2CPEs loading, please wait.
Denotes Vulnerable Software Quick InfoCVE Dictionary Entry:CVE-2020-15213 NVD Published Date: 09/25/2020 NVD Last Modified: 11/21/2024 Source: GitHub, Inc. |