CVE-2021-37644 : Exploit Details and Defense Strategies

A vulnerability has been identified in TensorFlow, affecting versions >= 2.3.4 and < 2.5.1. The issue arises from providing a negative element to a specific list argument, leading to process termination.

Understanding CVE-2021-37644

This section delves into the details of the vulnerability in TensorFlow.

What is CVE-2021-37644?

TensorFlow versions >= 2.3.4 and < 2.5.1 are impacted by a flaw that results in the abort of the runtime when a negative element is supplied to a certain list argument.

The Impact of CVE-2021-37644

The vulnerability can cause the runtime to prematurely terminate due to reallocating a standard vector to have a negative number of elements, affecting machine learning operations.

Technical Details of CVE-2021-37644

Explore the specific technical aspects of this TensorFlow vulnerability.

Vulnerability Description

Providing a negative element to

num_elements

list argument leads to process abort, caused by reallocation of a

std::vector

with a negative number of elements.

Affected Systems and Versions

Versions >= 2.3.4 and < 2.5.1 of TensorFlow are impacted by this vulnerability.

Exploitation Mechanism

The flaw occurs in the

TensorListReserve

function, triggering the premature termination of the runtime due to improper vector reallocation.

Mitigation and Prevention

Discover the steps to mitigate and prevent exploitation of CVE-2021-37644.

Immediate Steps to Take

Users are advised to update TensorFlow to version 2.6.0 or apply the specific patches to versions 2.3.4, 2.4.3, and 2.5.1.

Long-Term Security Practices

Implement secure coding practices, validate inputs, and conduct regular security assessments to prevent similar vulnerabilities.

Patching and Updates

Regularly check for security updates and apply patches provided by the TensorFlow team to mitigate the risk of exploitation.