CVE-2023-27579 : Exploit Details and Defense Strategies

This CVE-2023-27579 involves TensorFlow experiencing a Floating Point Exception (FPE) in TFLite in the convolution kernel due to an incorrect comparison in the system.

Understanding CVE-2023-27579

This vulnerability affects TensorFlow, an end-to-end open-source platform for machine learning. The issue arises when constructing a tflite model with a parameter 'filter_input_channel' of less than 1, resulting in a Floating Point Exception (FPE).

What is CVE-2023-27579?

The vulnerability in TensorFlow occurs when certain parameters are set incorrectly, leading to a Floating Point Exception in the TFLite component's convolution kernel.

The Impact of CVE-2023-27579

The impact of this vulnerability is rated as high, with a CVSS v3.1 base score of 7.5. It has a low attack complexity and occurs over a network, potentially causing a high availability impact.

Technical Details of CVE-2023-27579

This section outlines the vulnerability description, affected systems, and exploitation mechanism associated with CVE-2023-27579.

Vulnerability Description

The vulnerability arises in TensorFlow when creating a tflite model with a 'filter_input_channel' parameter set to less than 1, resulting in a Floating Point Exception (FPE).

Affected Systems and Versions

The issue impacts TensorFlow versions prior to 2.11.1, with the vulnerability status marked as 'affected.'

Exploitation Mechanism

The vulnerability can be exploited by constructing a tflite model with the specific parameter 'filter_input_channel' set to a value less than 1, triggering the Floating Point Exception in the convolution kernel.

Mitigation and Prevention

To address CVE-2023-27579, immediate steps should be taken to mitigate the risk and prevent potential exploitation.

Immediate Steps to Take

Users are advised to update their TensorFlow installations to version 2.11.1 or higher to eliminate the vulnerability.
Organizations should apply the necessary patches and security updates provided by TensorFlow to remediate the issue.

Long-Term Security Practices

Implementing secure coding practices, conducting regular security assessments, and staying informed about vulnerabilities in TensorFlow can help prevent similar issues in the future.

Patching and Updates

Users should ensure that they regularly update their TensorFlow installations and stay informed about security advisories and patches released by the TensorFlow project to maintain a secure environment.