CVE-2021-29574 : Exploit Details and Defense Strategies

TensorFlow, an open-source platform for machine learning, is affected by a vulnerability in

tf.raw_ops.MaxPool3DGradGrad

that allows for undefined behavior by dereferencing null pointers when processing attacker-supplied empty tensors.

Understanding CVE-2021-29574

This CVE highlights a critical security issue in TensorFlow's implementation of

tf.raw_ops.MaxPool3DGradGrad

.

What is CVE-2021-29574?

The vulnerability in TensorFlow's

tf.raw_ops.MaxPool3DGradGrad

allows attackers to exploit the system by providing empty tensors, leading to the dereferencing of null pointers, resulting in undefined behavior.

The Impact of CVE-2021-29574

With a CVSS base score of 2.5 (Low), this vulnerability has a high attack complexity and local attack vector. While the availability impact is low, the exploitation requires low privileges and no user interaction.

Technical Details of CVE-2021-29574

This section delves into the specifics of the vulnerability, including its description, affected systems, and exploitation mechanism.

Vulnerability Description

The vulnerability arises from the failure to validate empty tensors, leading to null pointer dereference and undefined behavior in TensorFlow's

tf.raw_ops.MaxPool3DGradGrad

implementation.

Affected Systems and Versions

The versions affected by this vulnerability include TensorFlow versions less than 2.1.4, >= 2.2.0 and < 2.2.3, >= 2.3.0 and < 2.3.3, and >= 2.4.0 and < 2.4.2.

Exploitation Mechanism

Attackers can exploit this vulnerability by supplying empty tensors to the

MaxPool3DGradGrad

function, causing null pointer dereference and triggering undefined behavior.

Mitigation and Prevention

To address CVE-2021-29574, immediate steps, long-term security practices, and the importance of patching and updates are crucial.

Immediate Steps to Take

Users are advised to update TensorFlow to version 2.5.0 or apply the fix cherrypicked in versions 2.4.2, 2.3.3, 2.2.3, and 2.1.4.

Long-Term Security Practices

Implement secure coding practices, conduct regular security audits, and stay informed about potential vulnerabilities in TensorFlow.

Patching and Updates

Regularly update TensorFlow to the latest version, implement security patches promptly, and monitor official advisories for any new vulnerability disclosures.