CVE-2023-25663 : Security Advisory and Response

This CVE record discusses a vulnerability found in TensorFlow, specifically in versions prior to 2.11.1. The vulnerability, identified as a NULL Pointer Dereference (CWE-476), poses a high severity risk with a CVSS base score of 7.5.

Understanding CVE-2023-25663

This section delves into the details of CVE-2023-25663, outlining what the vulnerability entails and its potential impact on affected systems.

What is CVE-2023-25663?

The vulnerability in TensorFlow arises when the

ctx->step_container()

is a null pointer, leading to the execution of the Lookup function with a null pointer. This flaw allows threat actors to potentially exploit the application, compromising its security integrity.

The Impact of CVE-2023-25663

With a high base severity score and significant availability impact, CVE-2023-25663 can result in denial of service attacks, making systems vulnerable to exploitation by malicious entities.

Technical Details of CVE-2023-25663

In this section, we focus on the technical aspects of the CVE, including vulnerability description, affected systems, and the exploitation mechanism.

Vulnerability Description

The vulnerability stems from a null pointer error within TensorFlow, which can be leveraged by attackers to disrupt the application's normal operations and potentially gain unauthorized access.

Affected Systems and Versions

The vulnerability affects versions of TensorFlow prior to 2.11.1, highlighting the importance of updating to the patched versions to mitigate the risk of exploitation.

Exploitation Mechanism

Threat actors can exploit the NULL Pointer Dereference vulnerability in TensorFlow by manipulating the null pointer to execute malicious code, leading to potential system compromise and data breaches.

Mitigation and Prevention

Mitigating CVE-2023-25663 requires immediate action to secure systems and prevent exploitation. Implementing robust security measures is crucial to safeguard against such vulnerabilities.

Immediate Steps to Take

Update TensorFlow to versions 2.11.1 or higher to apply the necessary patches and eliminate the vulnerability.
Conduct regular security assessments and audits to detect and address any potential weaknesses in the application.

Long-Term Security Practices

Enforce secure coding practices and conduct thorough code reviews to identify and rectify vulnerabilities during the development phase.
Educate developers and IT teams on the importance of cybersecurity awareness to maintain a proactive security posture.

Patching and Updates

Stay informed about security advisories and updates released by TensorFlow to stay ahead of emerging threats and vulnerabilities.
Establish a regular patch management process to ensure timely implementation of security patches and updates to protect against known vulnerabilities.