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CVE-2021-41205 : What You Need to Know

Discover the impact and mitigation strategies for CVE-2021-41205, a high-severity heap out-of-bounds read vulnerability in TensorFlow affecting versions 2.4.4 to 2.6.0.

TensorFlow is an open-source platform for machine learning. In affected versions, the shape inference functions for the

QuantizeAndDequantizeV*
operations may trigger an out-of-bounds read, leading to a high severity vulnerability.

Understanding CVE-2021-41205

In this CVE, a heap out-of-bounds read vulnerability in all

tf.raw_ops.QuantizeAndDequantizeV*
ops in TensorFlow has been identified and addressed.

What is CVE-2021-41205?

The vulnerability in TensorFlow versions could allow an attacker to read outside the bounds of a heap-allocated array.

The Impact of CVE-2021-41205

The CVSS v3.1 base score for this vulnerability is 7.1, indicating a high severity issue with a significant impact on confidentiality and availability.

Technical Details of CVE-2021-41205

This section delves into the technical aspects of the vulnerability.

Vulnerability Description

The issue stems from the shape inference functions of certain operations, potentially leading to unauthorized reads beyond the memory bounds.

Affected Systems and Versions

        TensorFlow versions >= 2.6.0 and < 2.6.1
        TensorFlow versions >= 2.5.0 and < 2.5.2
        TensorFlow versions < 2.4.4

Exploitation Mechanism

The vulnerability can be exploited locally with low privileges required, impacting confidentiality and availability significantly.

Mitigation and Prevention

To address and mitigate the risks associated with CVE-2021-41205, consider the following steps:

Immediate Steps to Take

        Update TensorFlow to version 2.7.0 once available.
        For affected versions, apply the specific fix versions provided by TensorFlow.
        Monitor security advisories for further updates and patches.

Long-Term Security Practices

        Conduct regular security assessments and code reviews.
        Follow secure coding practices to prevent memory-related vulnerabilities.
        Educate developers on secure coding and potential risks.

Patching and Updates

Stay informed about security advisories issued by TensorFlow and promptly apply patches and updates to ensure a secure environment.

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