CVE-2023-25669 : Exploit Details and Defense Strategies

This CVE record pertains to a vulnerability in TensorFlow, an open-source platform for machine learning. The issue specifically involves a floating point exception in AvgPoolGrad when using XLA, affecting versions prior to 2.11.1. The vulnerability has been assigned a CVSS base score of 7.5, indicating a high severity level.

Understanding CVE-2023-25669

This section will delve into the details of CVE-2023-25669, shedding light on what this vulnerability entails and its potential impact on affected systems.

What is CVE-2023-25669?

The CVE-2023-25669 vulnerability in TensorFlow arises when the parameters stride and window size are not positive for

tf.raw_ops.AvgPoolGrad

. This leads to a floating point exception, potentially exposing the system to exploitation by threat actors. It is crucial for users of TensorFlow to be aware of this vulnerability to take appropriate measures to mitigate the risk.

The Impact of CVE-2023-25669

The impact of CVE-2023-25669 can be significant, especially for systems running affected versions of TensorFlow. A successful exploit could result in system instability, denial of service, or potentially unauthorized access to sensitive information. Understanding the potential consequences can help organizations prioritize their response to this vulnerability.

Technical Details of CVE-2023-25669

In this section, we will explore the technical aspects of CVE-2023-25669, including a description of the vulnerability, the systems and versions impacted, and the exploitation mechanism.

Vulnerability Description

The vulnerability in TensorFlow version < 2.11.1 occurs due to an incorrect comparison in the

tf.raw_ops.AvgPoolGrad

function when the stride and window size are not positive. This leads to a floating point exception, which can be exploited by malicious actors to disrupt the normal operation of the system.

Affected Systems and Versions

The vulnerability impacts systems running TensorFlow versions prior to 2.11.1. Specifically, any system using TensorFlow with a version less than 2.11.1 is susceptible to exploitation through this floating point exception in AvgPoolGrad with XLA.

Exploitation Mechanism

Threat actors can potentially exploit this vulnerability by manipulating the parameters of the

tf.raw_ops.AvgPoolGrad

function within TensorFlow. By providing negative values for the stride and window size, an attacker may trigger a floating point exception, leading to system instability or other malicious outcomes.

Mitigation and Prevention

To address CVE-2023-25669 effectively, it is crucial for users to implement appropriate mitigation and preventive measures. This section will outline immediate steps to take, long-term security practices, and the importance of applying available patches and updates.

Immediate Steps to Take

Users of TensorFlow should take immediate action by updating their installations to versions 2.11.1 or later. This will ensure that the floating point exception in AvgPoolGrad with XLA is addressed, reducing the risk of exploitation and potential security incidents.

Long-Term Security Practices

In the long term, it is advisable for organizations to prioritize security hygiene practices such as regular vulnerability scanning, timely software updates, and continuous monitoring for emerging threats. By maintaining a proactive security posture, businesses can better protect their systems and data from potential exploits.

Patching and Updates

TensorFlow users are strongly encouraged to apply the necessary patches and updates provided by the official TensorFlow repository. Installing the latest versions of the software will not only address the CVE-2023-25669 vulnerability but also incorporate additional security enhancements and bug fixes to strengthen the overall resilience of the platform.