CVE-2022-29210 : What You Need to Know

A detailed overview of CVE-2022-29210, a vulnerability in TensorFlow version 2.8.0 related to a heap buffer overflow due to incorrect hash function usage.

Understanding CVE-2022-29210

This section provides insights into the vulnerability, its impact, technical details, and mitigation steps.

What is CVE-2022-29210?

CVE-2022-29210 is a vulnerability in TensorFlow version 2.8.0 that involves the improper usage of the

TensorKey

hash function, leading to a heap buffer overflow.

The Impact of CVE-2022-29210

The vulnerability has a CVSS base score of 5.5 (Medium) and arises from a low complexity, local attack vector with high availability impact. It affects the integrity and availability of the system.

Technical Details of CVE-2022-29210

This section delves into the specifics of the vulnerability, including its description, affected systems and versions, and exploitation mechanism.

Vulnerability Description

In TensorFlow 2.8.0, the

TensorKey

hash function incorrectly used the estimated

AllocatedBytes()

, resulting in ASAN failures due to accessing individual tensor bytes through

tensor.data()

of size

AllocatedBytes()

.

Affected Systems and Versions

The vulnerability impacts TensorFlow version 2.8.0. It is crucial for users of this specific version to take immediate action to prevent exploitation.

Exploitation Mechanism

The issue stemmed from the inadequate hash function for constants like

int32_t

and the improper handling of tensor bytes allocation, leading to a heap buffer overflow.

Mitigation and Prevention

This section outlines the necessary steps to address the CVE-2022-29210 vulnerability and protect systems from exploitation.

Immediate Steps to Take

Users are advised to update TensorFlow to versions 2.9.0 or 2.8.1 as these versions contain patches to mitigate the vulnerability. It is essential to apply these updates promptly.

Long-Term Security Practices

Implementing secure coding practices, staying informed about software vulnerabilities, and regularly updating dependencies are crucial for maintaining a secure environment.

Patching and Updates

Regularly check for security advisories and updates from TensorFlow to ensure that known vulnerabilities are promptly patched.