CVE-2021-29592 : Vulnerability Insights and Analysis

TensorFlow is an end-to-end open-source platform for machine learning. This CVE highlights a null pointer dereference vulnerability in TFLite's

Reshape

operator that affects certain versions of TensorFlow.

Understanding CVE-2021-29592

This CVE points out a critical vulnerability in TensorFlow related to NULL pointer dereference in TFLite's

Reshape

operator.

What is CVE-2021-29592?

TensorFlow was found to have a vulnerability in its TFLite's

Reshape

operator, allowing malicious actors to trigger a NULL pointer dereference issue. This could lead to a crash or potentially enable attackers to execute arbitrary code.

The Impact of CVE-2021-29592

The vulnerability's impact is rated as MEDIUM with a base CVSS score of 4.4. It has a low attack complexity and requires low privileges, but could result in code execution or system crashes.

Technical Details of CVE-2021-29592

The vulnerability stemmed from the fix for a previous CVE, CVE-2020-15209, which did not address the case where the target shape of the

Reshape

operator is defined by elements of a 1-D tensor.

Vulnerability Description

The vulnerability allowed passing a null-buffer-backed tensor with a 1D shape, potentially leading to a NULL pointer dereference in TensorFlow.

Affected Systems and Versions

TensorFlow versions < 2.1.4
TensorFlow versions >= 2.2.0, < 2.2.3
TensorFlow versions >= 2.3.0, < 2.3.3
TensorFlow versions >= 2.4.0, < 2.4.2

Exploitation Mechanism

A successful exploitation of this vulnerability could allow an attacker to crash systems or potentially execute arbitrary code by triggering the NULL pointer dereference issue.

Mitigation and Prevention

It is crucial to address this vulnerability to prevent potential exploitation and secure TensorFlow deployment.

Immediate Steps to Take

Update TensorFlow to version 2.5.0, which includes a fix for this vulnerability.
Apply patches for affected versions (2.1.4, 2.2.3, 2.3.3, 2.4.2) or upgrade to TensorFlow 2.5.0.

Long-Term Security Practices

Regularly check for security advisories and apply updates promptly to mitigate future vulnerabilities.

Patching and Updates

Stay informed about security updates from TensorFlow and apply patches as soon as they are released to ensure the security of your machine learning environment.