CVE-2022-36016 Explained : Impact and Mitigation

TensorFlow is an open-source platform for machine learning. A vulnerability exists in the

tensorflow::full_type::SubstituteFromAttrs

function, triggering a

CHECK

-fail instead of returning a status when the function receives a

FullTypeDef& t

that does not have exactly three arguments. The issue has been patched in GitHub commit 6104f0d4091c260ce9352f9155f7e9b725eab012 and will be addressed in TensorFlow 2.10.0. The vulnerability affects versions < 2.7.2, >= 2.8.0, < 2.8.1, and >= 2.9.0, < 2.9.1.

Understanding CVE-2022-36016

This section provides insights into the nature and impact of the vulnerability.

What is CVE-2022-36016?

The CVE-2022-36016 vulnerability in TensorFlow arises from improper handling within the

tensorflow::full_type::SubstituteFromAttrs

function, leading to a

CHECK

-fail under certain conditions.

The Impact of CVE-2022-36016

The vulnerability could be exploited to cause a denial of service (DoS) condition, impacting the availability of the TensorFlow application when triggered.

Technical Details of CVE-2022-36016

This section delves into the technical specifics of the CVE.

Vulnerability Description

The flaw allows an attacker to disrupt the functioning of TensorFlow by triggering a

CHECK

-fail within the

tensorflow::full_type::SubstituteFromAttrs

function.

Affected Systems and Versions

The vulnerability affects TensorFlow versions < 2.7.2, >= 2.8.0, < 2.8.1, and >= 2.9.0, < 2.9.1.

Exploitation Mechanism

By providing input that does not conform to the expected format, an adversary could exploit this vulnerability to disrupt TensorFlow's operations.

Mitigation and Prevention

This section outlines the steps to mitigate the risks associated with CVE-2022-36016.

Immediate Steps to Take

Users are advised to update affected TensorFlow versions to 2.7.2, 2.8.1, 2.9.1, or install the upcoming TensorFlow 2.10.0 release to address the vulnerability.

Long-Term Security Practices

Implementing secure coding practices and regularly updating TensorFlow installations can help prevent similar vulnerabilities from being exploited.

Patching and Updates

Keep an eye on TensorFlow releases and promptly apply patches and updates to stay protected against known vulnerabilities.