CVE-2021-37674 : Exploit Details and Defense Strategies

TensorFlow is an end-to-end open source platform for machine learning. In affected versions, an attacker can trigger a denial of service via a segmentation fault in

tf.raw_ops.MaxPoolGrad

caused by missing validation. The implementation misses some validation for the

orig_input

and

orig_output

tensors. The fixes for CVE-2021-29579 were incomplete. The issue has been patched in GitHub commit 136b51f10903e044308cf77117c0ed9871350475 and will be included in TensorFlow 2.6.0. The commit will also be applied to TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4.

Understanding CVE-2021-37674

This section provides insights into the impact, technical details, and mitigation strategies related to CVE-2021-37674.

What is CVE-2021-37674?

CVE-2021-37674 involves incomplete validation in

MaxPoolGrad

in TensorFlow, allowing an attacker to exploit a segmentation fault and trigger a denial of service through missing input validation.

The Impact of CVE-2021-37674

The vulnerability has a CVSS base score of 5.5, classifying it as a medium severity issue. With a low attack complexity and local attack vector, the flaw can result in high availability impact.

Technical Details of CVE-2021-37674

Let's delve deeper into the specifics of this vulnerability.

Vulnerability Description

The vulnerability arises from missing validation for certain tensors in the

MaxPoolGrad

function within TensorFlow, potentially leading to a denial of service through a segmentation fault.

Affected Systems and Versions

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

Exploitation Mechanism

Attackers can exploit this vulnerability by causing a segmentation fault in

tf.raw_ops.MaxPoolGrad

due to inadequate validation of specific tensors.

Mitigation and Prevention

Understanding how to mitigate and prevent the exploit is crucial for maintaining system security.

Immediate Steps to Take

Users are advised to update to TensorFlow 2.6.0 or apply the provided patch on versions 2.5.1, 2.4.3, and 2.3.4 to address the vulnerability.

Long-Term Security Practices

Incorporating robust input validation practices and regularly updating TensorFlow installations can help prevent similar vulnerabilities in the future.

Patching and Updates

Stay informed about security advisories and promptly apply patches released by TensorFlow to address known vulnerabilities.