CVE-2021-29577 : Vulnerability Insights and Analysis

TensorFlow is an end-to-end open source platform for machine learning. The vulnerability lies in the implementation of

tf.raw_ops.AvgPool3DGrad

which is prone to a heap buffer overflow due to an unchecked assumption in tensor dimensions. TensorFlow versions affected include < 2.1.4, >= 2.2.0, < 2.2.3, >= 2.3.0, < 2.3.3, and >= 2.4.0, < 2.4.2. The CVSS base score for this vulnerability is 2.5 (Low severity).

Understanding CVE-2021-29577

This section delves into the nature of the vulnerability, its impact, technical details, and mitigation strategies.

What is CVE-2021-29577?

CVE-2021-29577 is a heap buffer overflow vulnerability in TensorFlow's implementation of

AvgPool3DGrad

, allowing for potential exploitation by attackers to overrun the buffer.

The Impact of CVE-2021-29577

The vulnerability's impact is measured as low severity with a CVSS base score of 2.5, posing a risk of local attack vector with high attack complexity but low availability impact.

Technical Details of CVE-2021-29577

The vulnerability details, affected systems and versions, and exploitation mechanism are crucial for understanding and addressing the issue.

Vulnerability Description

The vulnerability stems from the unchecked assumption regarding tensor dimensions, enabling a heap buffer overflow, thereby opening doors for potential security breaches.

Affected Systems and Versions

TensorFlow versions < 2.1.4, >= 2.2.0, < 2.2.3, >= 2.3.0, < 2.3.3, and >= 2.4.0, < 2.4.2 are all susceptible to this heap buffer overflow vulnerability.

Exploitation Mechanism

Attackers can exploit this vulnerability by leveraging the unchecked tensor dimension assumption in

AvgPool3DGrad

to overflow the buffer, potentially causing a security breach.

Mitigation and Prevention

Taking immediate steps, following long-term security practices, and keeping systems updated are essential to mitigating the risks associated with CVE-2021-29577.

Immediate Steps to Take

Ensure prompt patching of affected TensorFlow versions and consider security updates to prevent potential exploitation of the heap buffer overflow vulnerability.

Long-Term Security Practices

Adopt robust security practices such as regular security audits, code reviews, and maintaining awareness of potential vulnerabilities in machine learning platforms.

Patching and Updates

Stay informed about TensorFlow's 2.5.0 release, which addresses this vulnerability, and prioritize the patching of affected versions 2.4.2, 2.3.3, 2.2.3, and 2.1.4 to safeguard systems against exploitation.