CVE-2021-37662 : Vulnerability Insights and Analysis

TensorFlow is an end-to-end open source platform for machine learning. In affected versions, an attacker can generate undefined behavior via a reference binding to nullptr in BoostedTreesCalculateBestGainsPerFeature and similar attacks can occur in BoostedTreesCalculateBestFeatureSplitV2. The implementation does not validate input values, leading to a high severity vulnerability. The issue has been patched in GitHub commit 9c87c32c710d0b5b53dc6fd3bfde4046e1f7a5ad and 429f009d2b2c09028647dd4bb7b3f6f414bbaad7. The fix will be included in TensorFlow 2.6.0 and also backported to TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4.

Understanding CVE-2021-37662

This section provides insights into the vulnerability, its impact, affected systems, and how to mitigate the risks.

What is CVE-2021-37662?

CVE-2021-37662 involves a reference binding to nullptr in boosted trees in TensorFlow, allowing attackers to trigger undefined behavior due to lack of input value validation.

The Impact of CVE-2021-37662

The vulnerability has a base score of 7.1, reflecting a high severity issue. An attacker can exploit this flaw locally with low privileges, leading to high integrity impact and availability impact.

Technical Details of CVE-2021-37662

Explore the specific technical aspects of the CVE to understand the vulnerability better.

Vulnerability Description

The vulnerability stems from improper handling of reference binding to nullptr in Boosted Trees calculations within TensorFlow, enabling attackers to cause undefined behavior.

Affected Systems and Versions

TensorFlow versions >= 2.3.4, 2.4.0 to < 2.4.3, and 2.5.0 to < 2.5.1 are impacted by this vulnerability.

Exploitation Mechanism

Attackers can exploit this vulnerability by manipulating reference bindings to nullptr in Boosted Trees calculations, bypassing input validation.

Mitigation and Prevention

Discover the steps to secure systems against CVE-2021-37662 and prevent potential exploitation.

Immediate Steps to Take

Users should apply the provided patches immediately to mitigate the risk of exploitation. Update TensorFlow to versions mentioned with the fix.

Long-Term Security Practices

Establish robust security practices, including continuous monitoring, regular updates, and threat intelligence integration to prevent similar vulnerabilities.

Patching and Updates

Regularly check for security updates from TensorFlow and apply patches promptly to protect systems from known vulnerabilities.