CVE-2021-37665 : What You Need to Know

TensorFlow is a popular end-to-end open source platform for machine learning. However, a vulnerability in the MKL implementation of requantization can lead to undefined behavior and potential access to heap allocated arrays. This CVE highlights the impact, technical details, and mitigation steps related to the incomplete validation in TensorFlow.

Understanding CVE-2021-37665

This section delves into the specifics of the vulnerability found in TensorFlow.

What is CVE-2021-37665?

In affected versions of TensorFlow, incomplete validation in the MKL implementation of requantization opens the door for attackers to trigger undefined behavior and access data outside the bounds of heap allocated arrays. The vulnerability lies in the inadequate validation of input tensors, presenting a high-severity risk.

The Impact of CVE-2021-37665

The CVSS v3.1 base score of 7.8 categorizes this vulnerability as high severity. With low attack complexity and local attack vector, an attacker can exploit the vulnerability to compromise confidentiality, integrity, and availability, requiring low privileges.

Technical Details of CVE-2021-37665

Explore the technical aspects of the vulnerability in TensorFlow.

Vulnerability Description

The vulnerability arises from incomplete validation in the MKL requantization implementation, enabling attackers to trigger undefined behavior and access data beyond array bounds.

Affected Systems and Versions

The vulnerability affects TensorFlow versions >= 2.5.0 and < 2.5.1, >= 2.4.0 and < 2.4.3, and < 2.3.4, emphasizing the importance of immediate action.

Exploitation Mechanism

Attackers can exploit this vulnerability by binding a reference to a null pointer or accessing data outside the bounds of heap allocated arrays, leveraging the incomplete validation in the MKL implementation.

Mitigation and Prevention

Discover the essential steps to mitigate the risks posed by CVE-2021-37665.

Immediate Steps to Take

It is crucial to apply the patches released by TensorFlow to address the vulnerability promptly. Ensure that all affected versions are updated to prevent exploitation.

Long-Term Security Practices

Incorporate robust input validation mechanisms, conduct regular security audits, and stay informed about security updates to bolster your system's defenses against similar vulnerabilities in the future.

Patching and Updates

TensorFlow has released patches addressing the incomplete validation in the MKL implementation. Ensure that you update to TensorFlow 2.6.0 or implement the specific commits (9e62869465573cb2d9b5053f1fa02a81fce21d69 and 203214568f5bc237603dbab6e1fd389f1572f5c9) on affected versions (2.5.1, 2.4.3, and 2.3.4) to safeguard your systems.