CVE-2021-29516 Explained : Impact and Mitigation

A null pointer dereference vulnerability has been identified in TensorFlow, impacting versions prior to 2.1.4 and between 2.2.0 to 2.4.2. This vulnerability could allow an attacker to trigger a null pointer dereference by providing an invalid ragged tensor argument.

Understanding CVE-2021-29516

This section dives into the details of the CVE-2021-29516 vulnerability in TensorFlow.

What is CVE-2021-29516?

TensorFlow, a popular open-source platform for machine learning, is susceptible to a null pointer dereference when calling

tf.raw_ops.RaggedTensorToVariant

with an invalid ragged tensor argument.

The Impact of CVE-2021-29516

The vulnerability results in a null pointer dereference due to the lack of validation for the ragged tensor's non-empty requirement. This could be exploited by an attacker to trigger a null pointer dereference, potentially leading to a denial of service or other security risks.

Technical Details of CVE-2021-29516

Let's explore the technical aspects of the CVE-2021-29516 vulnerability.

Vulnerability Description

The issue stems from the

RaggedTensorToVariant

operation, where an invalid ragged tensor argument can result in dereferencing a null pointer, posing a security risk.

Affected Systems and Versions

Versions prior to 2.1.4
Versions between 2.2.0 to 2.4.2

Exploitation Mechanism

By supplying an invalid ragged tensor to

tf.raw_ops.RaggedTensorToVariant

, attackers can exploit this vulnerability to cause a null pointer dereference.

Mitigation and Prevention

Learn how to mitigate the risks associated with CVE-2021-29516.

Immediate Steps to Take

It is recommended to update TensorFlow to version 2.5.0, which includes a fix for this vulnerability. Alternatively, patches have been applied to versions 2.4.2, 2.3.3, 2.2.3, and 2.1.4 to address the issue.

Long-Term Security Practices

Implement security best practices such as input validation and code review to prevent similar vulnerabilities in the future.

Patching and Updates

Regularly update TensorFlow to the latest version to ensure that known vulnerabilities are mitigated.