CVE-2022-41894 : Exploit Details and Defense Strategies

This CVE record involves a buffer overflow vulnerability in

CONV_3D_TRANSPOSE

on TFLite, a TensorFlow Lite operator. The vulnerability could allow an attacker to craft a malicious model, leading to a buffer overflow. Find out more details below.

Understanding CVE-2022-41894

In this section, we will explore what CVE-2022-41894 is and its potential impact.

What is CVE-2022-41894?

CVE-2022-41894 is a buffer overflow vulnerability in the reference kernel of the

CONV_3D_TRANSPOSE

TensorFlow Lite operator. By exploiting this vulnerability, an attacker could manipulate the bias of a layer beyond the buffer's bounds.

The Impact of CVE-2022-41894

The impact of CVE-2022-41894 is significant as it could allow malicious actors to execute arbitrary code or crash the application, posing a serious security risk to TensorFlow users.

Technical Details of CVE-2022-41894

This section will cover the technical details of the vulnerability, including its description, affected systems, and exploitation mechanism.

Vulnerability Description

The vulnerability arises from an incorrect increment operation in the TensorFlow Lite operator, leading to a buffer overflow when certain conditions are met. An attacker can exploit this flaw to write specific values outside the buffer's boundaries.

Affected Systems and Versions

The affected product is TensorFlow, specifically versions >= 2.10.0, < 2.10.1, >= 2.9.0, < 2.9.3, and < 2.8.4. Users with these versions are advised to take immediate action to mitigate the risk.

Exploitation Mechanism

To exploit this vulnerability, an attacker can craft a model with a specific number of input channels and manipulate the bias of the layer to trigger the buffer overflow.

Mitigation and Prevention

In this section, we will discuss the steps to mitigate and prevent the exploitation of CVE-2022-41894.

Immediate Steps to Take

Users are strongly advised to apply the patch provided in GitHub commit 72c0bdcb25305b0b36842d746cc61d72658d2941. Additionally, upgrading to TensorFlow 2.11 or applying the fix on TensorFlow 2.10.1, 2.9.3, and 2.8.4 is essential.

Long-Term Security Practices

To enhance security posture, users should regularly update their TensorFlow installations, follow secure coding practices, and stay informed about the latest security advisories.

Patching and Updates

Stay informed about security patches and updates released by TensorFlow to address vulnerabilities like CVE-2022-41894. Regularly updating your software is crucial for maintaining a secure environment.