TensorFlow is an Open Source Machine Learning Framework. In versions prior to 2.11.1 a malicious invalid input crashes a tensorflow model (Check Failed) and can be used to trigger a denial of service attack. A proof of concept can be constructed with the `Convolution3DTranspose` function. This Convolution3DTranspose layer is a very common API in modern neural networks. The ML models containing such vulnerable components could be deployed in ML applications or as cloud services. This failure could be potentially used to trigger a denial of service attack on ML cloud services. An attacker must have privilege to provide input to a `Convolution3DTranspose` call. This issue has been patched and users are advised to upgrade to version 2.11.1. There are no known workarounds for this vulnerability.
Published 2023-03-27 20:15:09
Updated 2023-04-03 16:19:24
Source GitHub, Inc.
View at NVD,   CVE.org
Vulnerability category: Input validationDenial of service

Products affected by CVE-2023-25661

Exploit prediction scoring system (EPSS) score for CVE-2023-25661

0.07%
Probability of exploitation activity in the next 30 days EPSS Score History
~ 29 %
Percentile, the proportion of vulnerabilities that are scored at or less

CVSS scores for CVE-2023-25661

Base Score Base Severity CVSS Vector Exploitability Score Impact Score Score Source First Seen
6.5
MEDIUM CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
2.8
3.6
NIST
6.5
MEDIUM CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
2.8
3.6
GitHub, Inc.

CWE ids for CVE-2023-25661

  • The product receives input or data, but it does not validate or incorrectly validates that the input has the properties that are required to process the data safely and correctly.
    Assigned by: security-advisories@github.com (Secondary)

References for CVE-2023-25661

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