CVE-2021-29515 : What You Need to Know

This CVE-2021-29515 article provides details about a vulnerability in TensorFlow, impacting versions before 2.1.4 and between 2.2.0 to 2.4.2. The vulnerability is related to the reference binding to null pointer in

MatrixDiag*

operations.

Understanding CVE-2021-29515

CVE-2021-29515 is a security flaw in TensorFlow that arises from the lack of validation for non-empty tensor arguments in specific operations.

What is CVE-2021-29515?

TensorFlow, a popular open-source machine learning platform, is affected by a vulnerability that could potentially lead to null pointer dereference issues.

The Impact of CVE-2021-29515

The impact of this CVE includes the risk of unauthorized access to sensitive information and possible system compromise due to improper handling of tensor arguments.

Technical Details of CVE-2021-29515

The vulnerability allows for local vector attacks with high complexity and low availability impact. The base score is 2.5, indicating a low severity level.

Vulnerability Description

The vulnerability lies in the

MatrixDiag*

operations of TensorFlow, where the lack of validation for non-empty tensor arguments poses a security risk.

Affected Systems and Versions

TensorFlow versions before 2.1.4 and versions between 2.2.0 to 2.4.2 are affected by this vulnerability.

Exploitation Mechanism

The vulnerability can be exploited by an attacker with low privileges locally, without requiring user interaction.

Mitigation and Prevention

To address CVE-2021-29515, immediate actions and long-term security measures are necessary.

Immediate Steps to Take

Users are advised to update TensorFlow to version 2.5.0 to mitigate the vulnerability. Additionally, applying the corresponding patches to versions 2.4.2, 2.3.3, 2.2.3, and 2.1.4 is crucial.

Long-Term Security Practices

Implementing rigorous input validation, monitoring, and access controls can enhance the security posture of TensorFlow and other machine learning applications.

Patching and Updates

Regularly checking for security advisories, applying patches promptly, and maintaining up-to-date versions are essential for preventing security vulnerabilities in TensorFlow and related software.