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CVE-2020-15213 Detail

Description

In 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:

NIST CVSS score
NIST: NVD
N/A
NVD assessment not yet provided.

References to Advisories, Solutions, and Tools

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Hyperlink Resource
https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a Patch  Third Party Advisory 
https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1 Third Party Advisory 
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hjmq-236j-8m87 Exploit  Third Party Advisory 

Weakness Enumeration

CWE-ID CWE Name Source
CWE-770 Allocation of Resources Without Limits or Throttling cwe source acceptance level NIST   GitHub, Inc.  
CWE-119 Improper Restriction of Operations within the Bounds of a Memory Buffer GitHub, Inc.  

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Change History

5 change records found show changes

Quick Info

CVE Dictionary Entry:
CVE-2020-15213
NVD Published Date:
09/25/2020
NVD Last Modified:
11/18/2021
Source:
GitHub, Inc.