CVE-2021-29557 : Vulnerability Insights and Analysis

TensorFlow is an end-to-end open-source platform for machine learning. The CVE-2021-29557 vulnerability allows an attacker to cause a denial of service through a division by 0 error in

tf.raw_ops.SparseMatMul

. The issue arises as the

b

tensor is empty, triggering the error in Eigen code. The vulnerability affects TensorFlow versions < 2.1.4, >= 2.2.0, < 2.2.3, >= 2.3.0, < 2.3.3, and >= 2.4.0, < 2.4.2. The fix will be included in TensorFlow 2.5.0 and prior versions will also receive patches.

Understanding CVE-2021-29557

This section provides insights into the nature of the vulnerability and its impacts.

What is CVE-2021-29557?

CVE-2021-29557 is a vulnerability in TensorFlow that enables a malicious actor to trigger a denial of service attack by exploiting a division by 0 scenario in the

SparseMatMul

function.

The Impact of CVE-2021-29557

The vulnerability poses a low severity risk with high attack complexity, local attack vector, and low availability impact. Although it requires low privileges for exploitation, immediate mitigation is crucial to prevent service disruptions.

Technical Details of CVE-2021-29557

Explore the technical specifics of CVE-2021-29557 to understand its implications for affected systems.

Vulnerability Description

The vulnerability arises from a division by 0 error in

tf.raw_ops.SparseMatMul

, leading to a denial of service condition due to an empty

b

tensor.

Affected Systems and Versions

TensorFlow versions < 2.1.4, >= 2.2.0, < 2.2.3, >= 2.3.0, < 2.3.3, and >= 2.4.0, < 2.4.2 are affected by CVE-2021-29557, making them susceptible to exploitation.

Exploitation Mechanism

The vulnerability can be exploited by malicious actors to cause a denial of service by manipulating input conditions to trigger the division by 0 error.

Mitigation and Prevention

Discover the strategies that can be employed to mitigate the risks associated with CVE-2021-29557.

Immediate Steps to Take

To address CVE-2021-29557, it is recommended to update affected TensorFlow installations to version 2.5.0 or apply patches for versions 2.4.2, 2.3.3, 2.2.3, and 2.1.4, as they fall within the supported range.

Long-Term Security Practices

Incorporating rigorous security protocols, conducting regular vulnerability assessments, and promoting a culture of security awareness are essential for safeguarding against similar vulnerabilities in the future.

Patching and Updates

Stay informed about security updates from TensorFlow and promptly apply patches to ensure that known vulnerabilities are mitigated through timely updates.