CVE-2021-37651 Explained : Impact and Mitigation

TensorFlow, an open-source machine learning platform, is vulnerable to a heap buffer overflow in the

FractionalAvgPoolGrad

function. This vulnerability allows attackers to access data outside the allocated buffers, leading to potential security risks.

Understanding CVE-2021-37651

This section provides insights into the impact and technical details of the vulnerability.

What is CVE-2021-37651?

In affected versions of TensorFlow, specifically >= 2.3.4, < 2.5.1, a flaw in the implementation of

tf.raw_ops.FractionalAvgPoolGrad

enables unauthorized access to data beyond the buffer boundaries. The issue stems from the failure to verify the input tensor's non-emptiness, allowing malicious actors to exploit this behavior.

The Impact of CVE-2021-37651

The vulnerability holds a CVSS base score of 7.1, indicating a high severity level. It poses a threat to data confidentiality, integrity, and system availability, with low privileges required for exploitation in a local context and no user interaction needed.

Technical Details of CVE-2021-37651

This section delves deeper into the vulnerability's technical aspects.

Vulnerability Description

The flaw in

FractionalAvgPoolGrad

allows attackers to trick the function into accessing data outside the allocated buffers, leading to a heap buffer overflow scenario.

Affected Systems and Versions

The issue impacts TensorFlow versions >= 2.3.4, < 2.5.1, including 2.4.0 to 2.4.3, and will be addressed in the upcoming TensorFlow 2.6.0 release.

Exploitation Mechanism

By manipulating the input tensor to be empty, malicious actors can construct an empty

EigenDoubleMatrixMap

and exploit the heap buffer overflow to access unauthorized data.

Mitigation and Prevention

This section outlines the steps to mitigate the risks associated with CVE-2021-37651.

Immediate Steps to Take

Users are advised to update their TensorFlow installations to versions that include the patched fix: TensorFlow 2.6.0, 2.5.1, 2.4.3, and 2.3.4.

Long-Term Security Practices

Practicing secure coding techniques, implementing input validation checks, and staying updated on security advisories can help prevent similar vulnerabilities in the future.

Patching and Updates

Regularly check for security updates from TensorFlow and apply patches promptly to safeguard systems from potential exploits.