CVE-2021-29514 : Exploit Details and Defense Strategies

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a heap buffer overflow in the

RaggedBincount

operation, potentially causing a read from outside the bounds of the tensor buffer. The issue affects TensorFlow versions prior to 2.3.3 and between 2.4.0 and 2.4.2. The vulnerability is classified as CWE-787: Out-of-bounds Write with a CVSS base score of 2.5.

Understanding CVE-2021-29514

This section explains the impact, technical details, and mitigation steps related to the heap out of bounds write vulnerability in TensorFlow's

RaggedBinCount

.

What is CVE-2021-29514?

The vulnerability allows an attacker to exploit the

splits

argument of

RaggedBincount

to trigger a heap buffer overflow, potentially leading to reading beyond the allocated buffer.

The Impact of CVE-2021-29514

The vulnerability can result in unauthorized reads from sensitive memory locations, which may compromise data integrity and system stability.

Technical Details of CVE-2021-29514

The technical aspects include a description of the vulnerability, affected systems and versions, and the exploitation mechanism.

Vulnerability Description

The issue arises from an improper handling of user input in the

splits

argument, leading to a heap out-of-bounds write error.

Affected Systems and Versions

TensorFlow versions earlier than 2.3.3 and versions from 2.4.0 to 2.4.2 are vulnerable to this exploit.

Exploitation Mechanism

By setting the

splits(0)

value maliciously, an attacker can bypass the loop execution and trigger an unauthorized write operation.

Mitigation and Prevention

To address CVE-2021-29514, immediate steps need to be taken along with adopting long-term security practices and applying relevant patches and updates.

Immediate Steps to Take

Users should update TensorFlow to version 2.5.0 or apply the necessary patches available for versions 2.4.2 and 2.3.3 to mitigate the vulnerability.

Long-Term Security Practices

Implement secure coding practices, conduct regular security audits, and stay informed about potential vulnerabilities in software dependencies.

Patching and Updates

Regularly check for security updates from TensorFlow and apply them promptly to ensure protection against known exploits.