CVE-2021-29616 Explained : Impact and Mitigation

TensorFlow, a widely used open-source platform for machine learning, is vulnerable to a null pointer dereference flaw in Grappler's

TrySimplify

function. This vulnerability, tracked as CVE-2021-29616, affects multiple versions of TensorFlow, making it crucial for users to understand the impact and take necessary mitigation steps.

Understanding CVE-2021-29616

This section delves into the details of the vulnerability and its implications.

What is CVE-2021-29616?

TensorFlow's implementation of TrySimplify has undefined behavior, leading to dereferencing a null pointer in specific scenarios. This results in optimizing a node with no inputs, potentially leading to system compromise.

The Impact of CVE-2021-29616

The vulnerability's CVSS base score of 2.5 (Low) signifies its moderate severity. An attacker with local access can exploit this flaw to cause disruptions, although the impact on confidentiality and integrity is minimal.

Technical Details of CVE-2021-29616

In this section, we explore the technical aspects of the vulnerability.

Vulnerability Description

The vulnerability arises from erroneous null pointer dereference in TensorFlow's TrySimplify, allowing attackers to optimize nodes with no inputs.

Affected Systems and Versions

Multiple versions of TensorFlow are impacted, including versions prior to 2.1.4, 2.2.3, 2.3.3, and 2.4.2, urging users to update to secure releases.

Exploitation Mechanism

Attackers can leverage local access to trigger the null pointer dereference issue, exploiting the optimization process to compromise TensorFlow environments.

Mitigation and Prevention

This section outlines the necessary actions to mitigate the risks associated with CVE-2021-29616.

Immediate Steps to Take

Users are advised to update TensorFlow to version 2.5.0, the release that addresses this vulnerability. For older versions, cherrypicking the commit on 2.4.2, 2.3.3, 2.2.3, and 2.1.4 is essential to stay protected.

Long-Term Security Practices

Implementing secure coding practices, conducting regular security audits, and staying informed about software updates are key to enhancing long-term security.

Patching and Updates

Regularly applying security patches and keeping TensorFlow up to date with the latest releases is critical in preventing vulnerabilities and ensuring a secure ML environment.