CVE-2021-37684 : Exploit Details and Defense Strategies

TensorFlow is an end-to-end open source platform for machine learning. In affected versions, the implementations of pooling in TFLite are vulnerable to division by 0 errors as there are no checks for divisors not being 0. The fix is included in TensorFlow 2.6.0 and also cherrypicked on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4.

Understanding CVE-2021-37684

This CVE highlights a vulnerability in TensorFlow Lite pooling operations that could lead to division by zero errors, impacting the integrity and availability of the affected systems.

What is CVE-2021-37684?

CVE-2021-37684 involves a vulnerability in TensorFlow Lite's pooling operations that could allow for division by zero errors due to the absence of checks for non-zero divisors.

The Impact of CVE-2021-37684

The impact of this vulnerability is graded as medium severity with potential for high availability impact, affecting TensorFlow versions >= 2.3.4 and < 2.5.1.

Technical Details of CVE-2021-37684

This section dives deeper into the technical aspects of the vulnerability, including the vulnerability description, affected systems, versions, and exploitation mechanism.

Vulnerability Description

The vulnerability arises from the lack of checks for divisors being 0 in TensorFlow Lite pooling operations, leading to possible division by zero errors.

Affected Systems and Versions

Affected systems include installations of TensorFlow versions >= 2.3.4 and < 2.5.1, with TensorFlow Lite pooling implementations susceptible to the vulnerability.

Exploitation Mechanism

The vulnerability can be exploited by triggering pooling operations with divisor values of 0, causing the division by zero errors.

Mitigation and Prevention

To address CVE-2021-37684, immediate steps should be taken to secure affected systems and implement long-term security practices.

Immediate Steps to Take

Users should update TensorFlow to versions 2.6.0 or apply the patch provided by TensorFlow for versions 2.5.1, 2.4.3, and 2.3.4 to mitigate the vulnerability.

Long-Term Security Practices

Regularly monitor for security advisories from TensorFlow and apply patches promptly to prevent exposure to known vulnerabilities.

Patching and Updates

Ensure timely application of security patches released by TensorFlow to address vulnerabilities and enhance system security.