CVE-2021-29552 : Vulnerability Insights and Analysis

TensorFlow is an open-source platform for machine learning. In TensorFlow versions prior to 2.1.4, 2.2.3, 2.3.3, and 2.4.2, a vulnerability exists in the

UnsortedSegmentJoin

function. An attacker can trigger a denial of service attack by manipulating the

num_segments

tensor argument. The issue arises from a validation assumption, leading to a CHECK failure and process termination. A fix will be available in TensorFlow 2.5.0, with backports to affected supported versions.

Understanding CVE-2021-29552

This section provides an insight into the nature of the CVE and its implications.

What is CVE-2021-29552?

CVE-2021-29552 highlights a vulnerability in TensorFlow's

UnsortedSegmentJoin

function, where an attacker can exploit the

num_segments

tensor to launch a denial of service attack due to an incorrect assumption in the implementation.

The Impact of CVE-2021-29552

The vulnerability could allow an attacker to cause a denial of service by manipulating the

num_segments

tensor argument, potentially leading to process termination.

Technical Details of CVE-2021-29552

Delve deeper into the specifics of the CVE.

Vulnerability Description

The vulnerability arises from the assumption that the

num_segments

tensor is a valid scalar. When the tensor is empty, the associated CHECK will fail, triggering process termination.

Affected Systems and Versions

TensorFlow versions prior to 2.1.4, 2.2.3, 2.3.3, and 2.4.2 are impacted by this vulnerability, necessitating immediate action.

Exploitation Mechanism

By manipulating the

num_segments

tensor argument in the

UnsortedSegmentJoin

function, an attacker can exploit the vulnerability to orchestrate a denial of service attack.

Mitigation and Prevention

Explore steps to mitigate the risks posed by CVE-2021-29552.

Immediate Steps to Take

Users are advised to update to TensorFlow 2.5.0 to address the vulnerability. Backports will be available for TensorFlow versions 2.1.4, 2.2.3, 2.3.3, and 2.4.2.

Long-Term Security Practices

Develop robust security practices by staying informed about security updates and promptly applying patches to prevent exploitation.

Patching and Updates

Regularly check for security advisories and updates from TensorFlow to ensure the latest patches are applied for protection.