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CVE-2021-29537 : Vulnerability Insights and Analysis

Learn about CVE-2021-29537, a heap buffer overflow vulnerability in TensorFlow allowing attackers to trigger issues via invalid quantization thresholds. Discover impact, affected versions, and mitigation steps.

A heap buffer overflow vulnerability in

QuantizedResizeBilinear
in TensorFlow versions before 2.1.4 allows attackers to trigger the issue by providing invalid thresholds for quantization, potentially leading to exploitation. This vulnerability has a CVSS base score of 2.5, indicating a low severity impact.

Understanding CVE-2021-29537

This section provides insights into the nature of the vulnerability and its implications.

What is CVE-2021-29537?

TensorFlow, an open-source machine learning platform, is susceptible to a heap buffer overflow in the

QuantizedResizeBilinear
function due to improper handling of threshold values for quantization.

The Impact of CVE-2021-29537

The heap buffer overflow can result in a security breach by malicious actors exploiting this vulnerability, compromising the integrity of affected systems.

Technical Details of CVE-2021-29537

This section delves deep into the technical aspects of the vulnerability.

Vulnerability Description

The vulnerability arises in TensorFlow's

QuantizedResizeBilinear
function, triggered by providing malformed quantization thresholds.

Affected Systems and Versions

TensorFlow versions prior to 2.1.4, as well as >= 2.2.0 and < 2.2.3, >= 2.3.0 and < 2.3.3, and >= 2.4.0 and < 2.4.2 are impacted by this vulnerability.

Exploitation Mechanism

Attackers exploit this flaw by supplying invalid quantization thresholds, causing a heap buffer overflow.

Mitigation and Prevention

This section highlights strategies to mitigate the risks posed by CVE-2021-29537.

Immediate Steps to Take

Users are advised to update their TensorFlow installations to version 2.1.4 or higher to mitigate the vulnerability.

Long-Term Security Practices

Implementing secure coding practices and staying informed about TensorFlow security updates are essential for maintaining robust cybersecurity measures.

Patching and Updates

Ensure timely application of patches released by TensorFlow to address the heap buffer overflow vulnerability.

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