CVE-2021-29590 : What You Need to Know

TensorFlow, the popular open-source machine learning platform, has been found to have a security vulnerability labeled as CVE-2021-29590. This CVE relates to a heap out-of-bounds read issue in the implementation of the

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TFLite operators within TensorFlow. This article provides a detailed insight into the nature of the vulnerability, its impacts, technical details, and mitigation methods.

Understanding CVE-2021-20657

This section delves into the specifics of the CVE to illuminate the potential risks associated with the vulnerability.

What is CVE-2021-20657?

The vulnerability identified as CVE-2021-20657 concerns the TensorFlow platform's TFLite operators

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, which can be manipulated to read data outside the bounds of allocated memory. The issue arises when the input tensors are empty, allowing for unauthorized data access.

The Impact of CVE-2021-20657

The vulnerability can result in unauthorized data extraction beyond the designated memory boundaries, potentially leading to sensitive data exposure or system instability.

Technical Details of CVE-2021-20657

This section provides a technical breakdown of the CVE, including its description, affected systems and versions, and the exploitation mechanism.

Vulnerability Description

The vulnerability in TensorFlow's TFLite operators

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permits data access outside the allocated memory boundaries, leading to a security risk.

Affected Systems and Versions

The issue impacts TensorFlow versions prior to 2.1.4, between 2.2.0 and 2.2.3, between 2.3.0 and 2.3.3, and between 2.4.0 and 2.4.2.

Exploitation Mechanism

Exploiting this vulnerability requires leveraging the empty input tensors of the

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operators to access unauthorized data.

Mitigation and Prevention

This section outlines steps that users and organizations can take to mitigate the risks associated with CVE-2021-20657.

Immediate Steps to Take

Users are advised to update their TensorFlow installations to version 2.5.0 or implement the provided fix on versions 2.4.2, 2.3.3, 2.2.3, and 2.1.4 to address the vulnerability.

Long-Term Security Practices

In addition to immediate updates, adopting secure coding practices, monitoring for unusual data access, and conducting regular security audits can enhance system security.

Patching and Updates

Regularly applying security patches released by TensorFlow is essential to protect systems from known vulnerabilities.