CVE-2022-21732 : Vulnerability Insights and Analysis

Tensorflow, an Open Source Machine Learning Framework, is vulnerable to a denial of service attack due to a memory exhaustion issue. The vulnerability arises from the lack of an upper bound on the

num_threads

argument in the implementation of

ThreadPoolHandle

. The vulnerability affects versions up to TensorFlow 2.5.3, 2.6.3, and 2.7.1, with a fix implemented in TensorFlow 2.8.0.

Understanding CVE-2022-21732

This section will delve into the details of the memory exhaustion vulnerability in Tensorflow.

What is CVE-2022-21732?

Tensorflow's vulnerability allows an attacker to trigger a denial of service attack by consuming excessive memory through the unchecked

num_threads

argument.

The Impact of CVE-2022-21732

The impact is rated as MEDIUM with a CVSS base score of 4.3 due to the potential for resource exhaustion, affecting the availability of the system.

Technical Details of CVE-2022-21732

Let's explore the technical aspects of the vulnerability.

Vulnerability Description

The flaw in the

ThreadPoolHandle

implementation permits memory exhaustion through unbounded memory allocation, impacting system availability.

Affected Systems and Versions

Versions up to TensorFlow 2.7.1 are affected, necessitating immediate action to mitigate the risk.

Exploitation Mechanism

Attackers can exploit the vulnerability by manipulating the

num_threads

argument, leading to memory exhaustion and denial of service.

Mitigation and Prevention

Here's how you can mitigate and prevent the exploitation of CVE-2022-21732.

Immediate Steps to Take

Users are advised to update to TensorFlow 2.8.0 or apply provided patches immediately to mitigate the vulnerability.

Long-Term Security Practices

Implement secure coding practices and regularly update systems to prevent future vulnerabilities.

Patching and Updates

Regularly check for security updates and apply patches promptly to stay protected against known vulnerabilities.