CVE-2023-25674 : Exploit Details and Defense Strategies

This CVE record pertains to a vulnerability identified in TensorFlow, an open-source machine learning platform, where versions prior to 2.12.0 and 2.11.1 are susceptible to a null pointer error in RandomShuffle when XLA is enabled.

Understanding CVE-2023-25674

This section will delve into the details regarding CVE-2023-25674, outlining the vulnerability, its impact, technical aspects, and mitigation strategies.

What is CVE-2023-25674?

CVE-2023-25674 is a vulnerability in TensorFlow that manifests as a null pointer error in the RandomShuffle function when XLA (Accelerated Linear Algebra) is enabled. This vulnerability can be exploited by attackers to potentially disrupt the availability of affected systems.

The Impact of CVE-2023-25674

The impact of CVE-2023-25674 is rated as high, with a CVSSv3 base severity score of 7.5. The vulnerability can lead to a denial of service attack, causing a significant impact on the availability of TensorFlow-dependent systems.

Technical Details of CVE-2023-25674

In this section, we will explore the technical aspects of CVE-2023-25674, including the vulnerability description, affected systems and versions, and the exploitation mechanism.

Vulnerability Description

The vulnerability in TensorFlow results from a null pointer dereference in the RandomShuffle function, specifically when XLA is enabled. This flaw allows malicious actors to cause a crash or potentially execute arbitrary code on the affected system.

Affected Systems and Versions

The vulnerability affects versions of TensorFlow before 2.12.0 and 2.11.1. Systems running these versions with XLA enabled are at risk of exploitation and should take immediate action to remediate the issue.

Exploitation Mechanism

Attackers can exploit this vulnerability by crafting and executing malicious inputs that trigger the null pointer error in the RandomShuffle function. This can lead to a crash or other undesired behavior in TensorFlow applications.

Mitigation and Prevention

This section outlines the necessary steps for mitigating the impact of CVE-2023-25674 and preventing potential exploitation.

Immediate Steps to Take

To address CVE-2023-25674, users and administrators are advised to upgrade TensorFlow to version 2.12.0 or 2.11.1, where the fix for the null pointer error in RandomShuffle with XLA enabled has been implemented.

Long-Term Security Practices

Implementing secure coding practices, regularly updating software components, and conducting security assessments can help mitigate the risk of similar vulnerabilities in the future.

Patching and Updates

Regularly applying security patches and updates provided by the TensorFlow project is crucial for maintaining the security posture of machine learning models and applications utilizing TensorFlow. Scheduled vulnerability scans can also help identify and address any overlooked vulnerabilities.