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CVE-2021-29596 Explained : Impact and Mitigation

Learn about CVE-2021-29596, a vulnerability in TensorFlow's `EmbeddingLookup` TFLite operator that allows a division by zero error. Find out the impacted versions and recommended mitigation steps.

TensorFlow is an end-to-end open source platform for machine learning. The implementation of the

EmbeddingLookup
TFLite operator is vulnerable to a division by zero error. An attacker can craft a model such that the first dimension of the
value
input is 0. The fix will be included in TensorFlow 2.5.0. This vulnerability affects TensorFlow versions < 2.1.4, >= 2.2.0 and < 2.2.3, >= 2.3.0 and < 2.3.3, and >= 2.4.0 and < 2.4.2.

Understanding CVE-2021-29596

This CVE highlights a vulnerability in the

EmbeddingLookup
TFLite operator of TensorFlow that allows attackers to trigger a division by zero error.

What is CVE-2021-29596?

CVE-2021-29596 is a security vulnerability in TensorFlow's implementation of

EmbeddingLookup
TFLite operator that allows an attacker to exploit a division by zero error.

The Impact of CVE-2021-29596

The impact of this vulnerability is considered low, with an CVSS score of 2.5. However, it can be exploited by attackers with low privileges and locally.

Technical Details of CVE-2021-29596

This section provides detailed technical insights into the vulnerability.

Vulnerability Description

The vulnerability arises due to a division by zero error in the

EmbeddingLookup
TFLite operator of TensorFlow.

Affected Systems and Versions

TensorFlow versions affected by this vulnerability include < 2.1.4, >= 2.2.0 and < 2.2.3, >= 2.3.0 and < 2.3.3, and >= 2.4.0 and < 2.4.2.

Exploitation Mechanism

Attackers can exploit this vulnerability by crafting a model with the

value
input's first dimension set to 0.

Mitigation and Prevention

To address CVE-2021-29596, follow these mitigation and prevention strategies:

Immediate Steps to Take

        Upgrade to TensorFlow version 2.5.0 once the fix is included.

Long-Term Security Practices

        Regularly update TensorFlow to the latest versions to ensure patching against known vulnerabilities.

Patching and Updates

        Apply the necessary patches provided by TensorFlow for versions 2.4.2, 2.3.3, 2.2.3, and 2.1.4 to mitigate the risk of exploitation.

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