CVE-2021-37654 : Exploit Details and Defense Strategies

TensorFlow, an open-source platform for machine learning, is susceptible to an out-of-bounds read vulnerability in versions >= 2.3.4 and < 2.5.1. Attackers can exploit this issue to trigger a crash or read data outside the bounds of heap allocated buffer. The impact includes high availability and confidentiality risks with a CVSS base score of 7.3.

Understanding CVE-2021-37654

This CVE highlights a security vulnerability in TensorFlow that allows attackers to trigger a crash or read data beyond the allocated memory boundaries.

What is CVE-2021-37654?

In affected versions of TensorFlow, a flaw in the

ResourceGather

operation can lead to crashes due to mishandling of input tensor dimensions.

The Impact of CVE-2021-37654

The vulnerability's impact is rated as high, with potential consequences including availability and confidentiality risks due to the unauthorized data access.

Technical Details of CVE-2021-37654

The issue stems from a failure to validate user-provided

batch_dims

values against the input tensor's rank, leading to out-of-bounds read operations.

Vulnerability Description

The vulnerability allows attackers to access data outside the allocated memory buffer by manipulating the

tensor

dimensions.

Affected Systems and Versions

Versions >= 2.3.4 and < 2.5.1 of TensorFlow are impacted by this vulnerability.

Exploitation Mechanism

Attackers can exploit the flaw through

ResourceGather

operation calls, triggering a crash or unauthorized access to memory.

Mitigation and Prevention

To mitigate the risks associated with CVE-2021-37654, immediate steps and long-term security practices are essential.

Immediate Steps to Take

Users are advised to update TensorFlow to the patched versions, including TensorFlow 2.6.0, 2.5.1, 2.4.3, and 2.3.4, which address the security vulnerability.

Long-Term Security Practices

Implementing secure coding practices, regular security audits, and staying informed about software updates are key for maintaining a secure machine learning environment.

Patching and Updates

Regularly applying security patches and staying up-to-date with the latest TensorFlow releases is crucial to prevent exploitation of known vulnerabilities in the platform.