CVE-2021-29512 : Vulnerability Insights and Analysis

TensorFlow is an end-to-end open source platform for machine learning. A vulnerability in the

RaggedBincount

operation could allow an attacker to trigger a heap buffer overflow by manipulating the

splits

argument. This could lead to reading from outside the bounds of the

splits

tensor buffer. The issue affects TensorFlow versions >=2.3.0 and < 2.3.3, as well as >= 2.4.0 and < 2.4.2. The fix for this vulnerability will be included in TensorFlow 2.5.0. More information can be found in the provided references.

Understanding CVE-2021-29512

This section provides insights into the nature and impact of the vulnerability.

What is CVE-2021-29512?

The CVE-2021-29512 pertains to a heap buffer overflow in the

RaggedBincount

operation in TensorFlow, allowing an attacker to corrupt memory and potentially execute arbitrary code.

The Impact of CVE-2021-29512

The vulnerability could be exploited to trigger a heap buffer overflow, compromising the integrity of the affected system and potentially leading to unauthorized access or denial of service attacks.

Technical Details of CVE-2021-29512

This section delves into the technical aspects of the vulnerability.

Vulnerability Description

The vulnerability arises due to improper handling of the

splits

argument in the

RaggedBincount

operation, enabling an attacker to overflow the heap buffer and read outside the designated memory space.

Affected Systems and Versions

The issue impacts TensorFlow versions >=2.3.0 and < 2.3.3, as well as >= 2.4.0 and < 2.4.2, making systems running these versions vulnerable to exploitation.

Exploitation Mechanism

By manipulating the

splits

argument, an attacker can induce a heap buffer overflow, leading to potential memory corruption and unauthorized access.

Mitigation and Prevention

This section outlines steps to address the vulnerability and enhance system security.

Immediate Steps to Take

It is recommended to update TensorFlow to version 2.5.0 to mitigate the vulnerability. Additionally, users should apply patches for TensorFlow 2.4.2 and 2.3.3 to safeguard against exploitation.

Long-Term Security Practices

To prevent such vulnerabilities in the future, developers should prioritize secure coding practices, input validation, and routine security audits.

Patching and Updates

Regularly apply software updates and security patches provided by TensorFlow to mitigate known vulnerabilities and enhance system resilience.