CVE-2021-29593 : Security Advisory and Response

TensorFlow, an open-source machine learning platform, is impacted by a vulnerability in its

BatchToSpaceNd

TFLite operator, leading to a division by zero error. Attackers can exploit this issue by manipulating a model to have a dimension of 'block' input as 0, causing a corresponding value in 'block_shape' to be 0. The vulnerability affects versions < 2.1.4, >= 2.2.0 and < 2.2.3, >= 2.3.0 and < 2.3.3, as well as >= 2.4.0 and < 2.4.2. The fix is scheduled for TensorFlow 2.5.0.

Understanding CVE-2021-29593

This section delves into the specifics of the CVE-2021-29593 vulnerability in TensorFlow.

What is CVE-2021-29593?

CVE-2021-29593 pertains to a division by zero error in TensorFlow's implementation of the

BatchToSpaceNd

TFLite operator, allowing attackers to create a malicious model.

The Impact of CVE-2021-29593

The vulnerability poses a low severity risk with an attack complexity of HIGH. It requires low privileges and no user interaction, impacting confidentiality and integrity to a limited extent.

Technical Details of CVE-2021-29593

Providing insights into the technical aspects of CVE-2021-29593 affecting TensorFlow.

Vulnerability Description

The specific vulnerability involves a division by zero error in the

BatchToSpaceNd

TFLite operator in TensorFlow.

Affected Systems and Versions

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

Exploitation Mechanism

Attackers can exploit this vulnerability by manipulating a model's 'block' input to have a zero dimension, triggering a corresponding issue in 'block_shape'.

Mitigation and Prevention

Exploring measures to address and mitigate the CVE-2021-29593 vulnerability in TensorFlow.

Immediate Steps to Take

To mitigate the risk, users should be cautious when processing inputs and validate dimension values accordingly.

Long-Term Security Practices

Implementing secure coding practices and regular security audits can help prevent similar vulnerabilities in the future.

Patching and Updates

It is crucial to update to TensorFlow 2.5.0 once the fix is released and ensure that all affected versions are patched promptly to mitigate the risk of exploitation.