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CVE-2021-29598 : Security Advisory and Response

Learn about CVE-2021-29598 impacting TensorFlow versions < 2.1.4, 2.2.0 to 2.2.3, 2.3.0 to 2.3.3, and 2.4.0 to 2.4.2. Understand the exploit, impact, and mitigation steps for this vulnerability.

TensorFlow, a popular open-source machine learning platform, is susceptible to a division by zero vulnerability in its implementation of the

SVDF
TFLite operator. This vulnerability affects versions prior to 2.1.4, as well as versions between 2.2.0 and 2.2.3, 2.3.0 and 2.3.3, and 2.4.0 and 2.4.2. An attacker can exploit this flaw by crafting a model to make
params->rank
equal to 0. The fix will be implemented in TensorFlow 2.5.0, with backports to versions 2.4.2, 2.3.3, 2.2.3, and 2.1.4.

Understanding CVE-2021-29598

This section delves into the specifics of the TensorFlow vulnerability.

What is CVE-2021-29598?

TensorFlow's implementation of the

SVDF
TFLite operator is vulnerable to a division by zero error due to a flaw in handling a specific model parameter.

The Impact of CVE-2021-29598

The vulnerability allows an attacker to trigger a division by zero scenario by manipulating model parameters, potentially leading to denial of service or data integrity issues.

Technical Details of CVE-2021-29598

Explore the technical aspects of the CVE.

Vulnerability Description

The vulnerability arises from a specific parameter (

params->rank
) being set to 0, causing a division by zero when processed by the
SVDF
TFLite operator.

Affected Systems and Versions

Versions of TensorFlow prior to 2.1.4, 2.2.0 to 2.2.3, 2.3.0 to 2.3.3, and 2.4.0 to 2.4.2 are susceptible to this vulnerability.

Exploitation Mechanism

An attacker can exploit this vulnerability by crafting a malicious model with the

params->rank
parameter set to 0.

Mitigation and Prevention

Discover the steps to mitigate and prevent exploitation of CVE-2021-29598.

Immediate Steps to Take

Users should update their TensorFlow installations to version 2.5.0 to apply the necessary fix for CVE-2021-29598.

Long-Term Security Practices

Implement secure coding practices and regular security updates to prevent similar vulnerabilities in the future.

Patching and Updates

Stay informed about security patches and updates released by TensorFlow to address known vulnerabilities.

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