CVE-2021-37679 : Exploit Details and Defense Strategies

A vulnerability has been discovered in TensorFlow that allows for a Heap Out-Of-Bounds issue when nesting

tf.map_fn

with

RaggedTensor

s. This article provides an overview of CVE-2021-37679, its impact, technical details, and mitigation strategies.

Understanding CVE-2021-37679

This section delves into the details of the vulnerability affecting TensorFlow.

What is CVE-2021-37679?

TensorFlow, an open-source machine learning platform, is susceptible to a heap-based out-of-bounds read vulnerability due to nesting

tf.map_fn

within another

tf.map_fn

call. The issue arises when converting a

Variant

tensor to a

RaggedTensor

, leading to memory leak and potential data loss.

The Impact of CVE-2021-37679

The vulnerability poses a high severity risk, with a CVSS base score of 7.1. An attacker could exploit this flaw to access sensitive information, compromise data integrity, and escalate privileges.

Technical Details of CVE-2021-37679

This section provides insights into the technical aspects of the vulnerability.

Vulnerability Description

The vulnerability enables an attacker to leak memory information by manipulating

RaggedTensor

conversion. Nesting

tf.map_fn

functions amplifies the risk of data loss and unauthorized memory access.

Affected Systems and Versions

The vulnerability affects TensorFlow versions >= 2.3.4 and < 2.5.1, including versions 2.4.0 to 2.4.3. Users operating on these versions are advised to apply immediate security measures.

Exploitation Mechanism

By exploiting the faulty conversion from

Variant

to

RaggedTensor

, threat actors can extract sensitive data and potentially disrupt system operations.

Mitigation and Prevention

This section outlines steps to mitigate the risks associated with CVE-2021-37679.

Immediate Steps to Take

Users are recommended to update TensorFlow to the patched versions (2.5.1, 2.4.3, 2.3.4) to eliminate the vulnerability. Additionally, review and restrict access to critical system resources to prevent unauthorized exploitation.

Long-Term Security Practices

Implementing secure coding practices, regular security audits, and user privilege management can fortify systems against similar vulnerabilities in the future.

Patching and Updates

Stay informed about security advisories and updates from the TensorFlow team to ensure timely application of patches and enhancements.