CVE-2021-29584 : Exploit Details and Defense Strategies

TensorFlow, an open-source machine learning platform, is affected by a vulnerability allowing an attacker to trigger a denial of service through an integer overflow in constructing tensor shapes. This issue affects versions below 2.1.4 and versions between 2.2.0 to 2.4.2. It has a low impact severity score of 2.5.

Understanding CVE-2021-29584

This CVE highlights an integer overflow vulnerability in TensorFlow, potentially leading to denial of service attacks.

What is CVE-2021-29584?

This vulnerability in TensorFlow allows an attacker to exploit an integer overflow in constructing tensor shapes, triggering a denial of service situation due to a legacy implementation.

The Impact of CVE-2021-29584

The impact of this CVE is a denial of service due to the integer overflow in constructing new tensor shapes, affecting various versions of TensorFlow.

Technical Details of CVE-2021-29584

This section covers the vulnerability description, affected systems and versions, and the exploitation mechanism.

Vulnerability Description

The vulnerability arises from an integer overflow during construction of tensor shapes in TensorFlow, potentially leading to denial of service attacks.

Affected Systems and Versions

The affected systems are those running TensorFlow versions below 2.1.4 and versions between 2.2.0 to 2.4.2.

Exploitation Mechanism

Attackers can exploit this vulnerability by triggering a

CHECK

-fail operation due to the integer overflow during new tensor shape construction.

Mitigation and Prevention

To address CVE-2021-29584, immediate steps, long-term security practices, and patching guidelines are crucial.

Immediate Steps to Take

Users should update their TensorFlow installations to versions 2.1.4, 2.2.3, 2.3.3, 2.4.2, or upgrade to 2.5.0, where the fix is included.

Long-Term Security Practices

Adopting secure coding practices and regularly updating TensorFlow to the latest versions can help prevent such vulnerabilities.

Patching and Updates

Regularly check for security advisories from TensorFlow and apply patches promptly to mitigate the risk of exploitation.