CVE-2021-37667 : Vulnerability Insights and Analysis

TensorFlow is an end-to-end open source platform for machine learning. In affected versions, an attacker can cause undefined behavior by binding a reference to a null pointer in

tf.raw_ops.UnicodeEncode

. The implementation reads the first dimension of the

input_splits

tensor before validating that this tensor is not empty. The issue has been patched in TensorFlow version 2.6.0, with updates also available for versions 2.5.1, 2.4.3, and 2.3.4. This vulnerability is classified as CWE-824: Access of Uninitialized Pointer, with a CVSS v3.1 base score of 7.8.

Understanding CVE-2021-37667

This section provides insights into the impact and technical details of the CVE-2021-37667 vulnerability in TensorFlow.

What is CVE-2021-37667?

CVE-2021-37667 refers to the vulnerability in TensorFlow that allows an attacker to manipulate a reference to a null pointer in Unicode encoding, leading to potential undefined behavior.

The Impact of CVE-2021-37667

The vulnerability has a high impact on confidentiality, integrity, and availability, with a CVSS v3.1 base score of 7.8, indicating a severe security risk. Attack complexity is low, requiring only local access to exploit the issue.

Technical Details of CVE-2021-37667

In this section, we delve into the specifics of the vulnerability, including its description, affected systems and versions, as well as the exploitation mechanism.

Vulnerability Description

The vulnerability arises from binding a reference to a null pointer in

tf.raw_ops.UnicodeEncode

, allowing attackers to trigger undefined behavior in affected versions of TensorFlow.

Affected Systems and Versions

The vulnerability affects TensorFlow versions >= 2.5.0 and < 2.5.1, >= 2.4.0 and < 2.4.3, as well as versions prior to 2.3.4, exposing a range of installations to potential exploitation.

Exploitation Mechanism

By exploiting the vulnerability, attackers can cause undefined behavior in TensorFlow by binding a reference to a null pointer, impacting the system's behavior and potentially compromising data.

Mitigation and Prevention

In this section, we outline the necessary steps to mitigate the risk posed by CVE-2021-37667, prioritizing immediate actions and long-term security practices.

Immediate Steps to Take

Users are advised to update their TensorFlow installations to the patched versions, including TensorFlow 2.6.0, 2.5.1, 2.4.3, and 2.3.4, to eliminate the vulnerability and enhance security postures.

Long-Term Security Practices

In addition to patching the software, organizations should implement robust security practices, such as regular security assessments, code reviews, and threat monitoring, to minimize the risk of similar vulnerabilities in the future.

Patching and Updates

Regularly updating TensorFlow to the latest versions and staying informed about security advisories and patches from the official sources can help mitigate the impact of potential vulnerabilities and ensure a secure machine learning environment.