CVE-2021-29566 Explained : Impact and Mitigation

A detailed overview of CVE-2021-29566, outlining the vulnerability discovered in TensorFlow and its impact, along with mitigation strategies.

Understanding CVE-2021-29566

In this section, we will delve into the specifics of the CVE-2021-29566 vulnerability within TensorFlow.

What is CVE-2021-29566?

TensorFlow, a widely used machine learning platform, is susceptible to a heap out-of-bounds (OOB) access vulnerability. By providing invalid arguments to

tf.raw_ops.Dilation2DBackpropInput

, an attacker can write beyond the boundaries of heap-allocated arrays.

The Impact of CVE-2021-29566

The impact of this vulnerability is rated as low with a CVSS base score of 2.5. Although no specific confidentiality or integrity impact is reported, it poses a significant risk due to the potential for attackers to write outside designated memory areas.

Technical Details of CVE-2021-29566

This section explores the technical aspects of CVE-2021-29566, including how the vulnerability manifests.

Vulnerability Description

The issue arises from the lack of validation before writing to output arrays, specifically

h_in_max

and

w_in_max

, allowing unauthorized write access outside valid memory ranges.

Affected Systems and Versions

The versions of TensorFlow affected include < 2.1.4, >= 2.2.0, < 2.2.3, >= 2.3.0, < 2.3.3, and >= 2.4.0, < 2.4.2. Users utilizing these versions are urged to take immediate action.

Exploitation Mechanism

Attack complexity is marked as HIGH, with a local attack vector and low availability impact. Attackers with low privileges can exploit this vulnerability without user interaction.

Mitigation and Prevention

In this section, we provide recommendations on mitigating the risks associated with CVE-2021-29566.

Immediate Steps to Take

Users are advised to update TensorFlow to version 2.5.0 or apply patches available for versions 2.4.2, 2.3.3, 2.2.3, and 2.1.4. Additionally, exercise caution with user inputs to prevent invalid arguments.

Long-Term Security Practices

Implement code reviews, input validation mechanisms, and stay informed about security advisories related to TensorFlow to proactively address future vulnerabilities.

Patching and Updates

Regularly monitor for security updates and apply patches promptly. Stay vigilant against emerging threats and ensure the TensorFlow environment is safeguarded at all times.