CVE-2021-29544 : Exploit Details and Defense Strategies

TensorFlow is an end-to-end open-source platform for machine learning. The CVE-2021-29544 vulnerability allows an attacker to trigger a denial of service via a

CHECK

-fail in

tf.raw_ops.QuantizeAndDequantizeV4Grad

. This flaw arises due to the lack of validation of the rank of certain tensors within TensorFlow, leading to a failure in the

vec<T>

method, requiring the rank to be 1. The fix for this issue will be incorporated in TensorFlow 2.5.0, with a cherrypick also planned for version 2.4.2.

Understanding CVE-2021-29544

This section delves into the impact and technical details of the CVE-2021-29544 vulnerability.

What is CVE-2021-29544?

The vulnerability in TensorFlow arises from a

CHECK

-fail in

QuantizeAndDequantizeV4Grad

, allowing an attacker to launch a denial of service attack.

The Impact of CVE-2021-29544

The impact of this vulnerability is rated as LOW, with a base CVSS score of 2.5. The attack complexity is considered HIGH, and the attack vector is LOCAL. The availability impact is rated as LOW, with no impact on confidentiality or integrity.

Technical Details of CVE-2021-29544

This section outlines the technical specifics of the CVE-2021-29544 vulnerability.

Vulnerability Description

The vulnerability lies in the improper check of unusual or exceptional conditions within TensorFlow, leading to a denial of service scenario.

Affected Systems and Versions

The vulnerability affects TensorFlow versions prior to 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 trigger a denial of service by exploiting the lack of validation in the rank of certain input tensors in TensorFlow.

Mitigation and Prevention

This section provides guidance on how to mitigate and prevent exploitation of the CVE-2021-29544 vulnerability.

Immediate Steps to Take

Users are advised to update to TensorFlow version 2.5.0 to address this vulnerability. For those using version 2.4.2, a fix will be cherrypicked to mitigate the issue.

Long-Term Security Practices

Implement regular security updates and patches for TensorFlow to prevent and mitigate potential vulnerabilities.

Patching and Updates

Stay informed about security advisories and updates from TensorFlow to ensure your system is protected against known vulnerabilities.