CVE-2022-21737 : Vulnerability Insights and Analysis

Tensorflow is an open-source machine learning framework that has been impacted by an assertion failure-based denial of service vulnerability. Malicious users can exploit the implementation of

*Bincount

operations to trigger a

CHECK

-fail, leading to denial of service attacks due to uncaught conditions in input arguments. This vulnerability affects TensorFlow versions up to 2.7.1 and has been addressed in TensorFlow 2.8.0.

Understanding CVE-2022-21737

This section delves into the details of the vulnerability, its impact, affected systems, and preventive measures.

What is CVE-2022-21737?

Tensorflow's vulnerability allows malicious actors to exploit

*Bincount

operations, leading to denial of service attacks by triggering

CHECK

-fail conditions, resulting in subsequent

CHECK

failures during tensor allocation.

The Impact of CVE-2022-21737

This vulnerability has a CVSS base score of 6.5, with a base severity of MEDIUM. The attack complexity is LOW, the attack vector is NETWORK, and the availability impact is HIGH.

Technical Details of CVE-2022-21737

Let's explore the technical aspects of this vulnerability.

Vulnerability Description

The vulnerability arises from uncaught conditions in input arguments during shape inference and kernel implementation, leading to denial of service attacks.

Affected Systems and Versions

The vulnerability impacts versions of TensorFlow up to 2.7.1. TensorFlow 2.5.3, 2.6.3, and 2.7.1 are affected, with TensorFlow 2.8.0 containing the necessary fix.

Exploitation Mechanism

Malicious users exploit the uncaught conditions in input arguments to trigger

CHECK

-fail conditions, leading to denial of service attacks.

Mitigation and Prevention

This section covers the necessary steps to mitigate the vulnerability and prevent future occurrences.

Immediate Steps to Take

Users are advised to update TensorFlow to version 2.8.0 to address the vulnerability. Apply the available patches for TensorFlow 2.5.3, 2.6.3, and 2.7.1 to ensure security.

Long-Term Security Practices

Implement secure coding practices and regularly update TensorFlow to the latest versions to prevent exploitation of known vulnerabilities.

Patching and Updates

Stay informed about security updates from TensorFlow and promptly apply patches to secure the framework against potential threats.