CVE-2023-25676 Explained : Impact and Mitigation

In this CVE-2023-25676 article, you will find detailed information about a vulnerability found in TensorFlow regarding a null dereference on ParallelConcat with XLA.

Understanding CVE-2023-25676

This section will help you understand the nature of CVE-2023-25676 related to TensorFlow's vulnerability.

What is CVE-2023-25676?

CVE-2023-25676 is a vulnerability in TensorFlow, an open-source machine learning platform. Specifically, in versions prior to 2.12.0 and 2.11.1 with XLA, the function

tf.raw_ops.ParallelConcat

can result in a nullptr dereference when provided a parameter

shape

with a rank that is not greater than zero.

The Impact of CVE-2023-25676

The impact of CVE-2023-25676 is rated as high, with a CVSS v3.1 base score of 7.5. This vulnerability can lead to a denial of service due to null pointer dereference. However, it does not have direct impacts on confidentiality, integrity, or user interaction.

Technical Details of CVE-2023-25676

Delve into the technical aspects of CVE-2023-25676 to understand the vulnerability better.

Vulnerability Description

The vulnerability arises in TensorFlow versions prior to 2.11.1, where a nullptr dereference occurs in the function

tf.raw_ops.ParallelConcat

with XLA when the parameter

shape

has a rank less than or equal to zero.

Affected Systems and Versions

The impacted system is TensorFlow, specifically versions before 2.11.1. Users using TensorFlow versions older than 2.11.1 are at risk of encountering this vulnerability.

Exploitation Mechanism

The exploitation of CVE-2023-25676 involves manipulating the parameter

shape

in the function

tf.raw_ops.ParallelConcat

with XLA to trigger a null dereference, leading to a denial of service.

Mitigation and Prevention

Learn how to mitigate and prevent the CVE-2023-25676 vulnerability effectively.

Immediate Steps to Take

Users should update their TensorFlow installations to version 2.12.0 or at least version 2.11.1 to mitigate the vulnerability. It is crucial to apply security patches promptly.

Long-Term Security Practices

Implement secure coding practices, regularly update software dependencies, and conduct security audits to prevent similar vulnerabilities in the future.

Patching and Updates

Stay informed about security advisories from TensorFlow and promptly apply patches and updates to ensure your system is protected against known vulnerabilities.