CVE-2021-37678 : Security Advisory and Response

A critical vulnerability has been discovered in TensorFlow that could allow attackers to execute arbitrary code due to YAML deserialization. This article provides an overview of CVE-2021-37678, including its impact, technical details, and mitigation strategies.

Understanding CVE-2021-37678

This section delves into the details of the vulnerability found in TensorFlow regarding arbitrary code execution through YAML deserialization.

What is CVE-2021-37678?

CVE-2021-37678 is a security flaw in TensorFlow that enables threat actors to trigger arbitrary code execution when deserializing a Keras model from YAML format. The vulnerability arises from the usage of

yaml.unsafe_load

in the implementation, potentially leading to code execution on input data.

The Impact of CVE-2021-37678

With a CVSS base score of 9.3 (Critical), this vulnerability poses a significant threat. Attackers can exploit the flaw to compromise confidentiality, integrity, and availability, without requiring any specific privileges.

Technical Details of CVE-2021-37678

This section provides a deeper understanding of the technical aspects related to CVE-2021-37678, including how the vulnerability manifests, affected systems, and exploitation mechanisms.

Vulnerability Description

The vulnerability in TensorFlow allows attackers to gain control over the system by tricking it to execute arbitrary code during the deserialization of a Keras model from YAML format.

Affected Systems and Versions

The affected versions are TensorFlow >= 2.5.0, < 2.5.1, TensorFlow >= 2.4.0, < 2.4.3, and TensorFlow < 2.3.4. Users of these versions are at risk of arbitrary code execution.

Exploitation Mechanism

By utilizing the

yaml.unsafe_load

function, threat actors can inject malicious code during the deserialization process, enabling them to execute arbitrary commands on the target system.

Mitigation and Prevention

In response to CVE-2021-37678, users are advised to implement immediate security measures to safeguard their systems and data.

Immediate Steps to Take

To mitigate the risk associated with this vulnerability, users should update TensorFlow to the patched versions – TensorFlow 2.6.0, TensorFlow 2.5.1, TensorFlow 2.4.3, or TensorFlow 2.3.4 – where the issue has been resolved.

Long-Term Security Practices

In the long term, it is crucial for organizations to adhere to secure coding practices, regularly update software components, and conduct security assessments to identify and address potential vulnerabilities.

Patching and Updates

Users should promptly apply available patches provided by TensorFlow to address the vulnerability and ensure the security of their machine learning environments.