CVE-2021-29535 : What You Need to Know

TensorFlow is an end-to-end open source platform for machine learning. The vulnerability, tracked as CVE-2021-29535, allows an attacker to cause a heap buffer overflow in the

QuantizedMul

function by passing invalid thresholds for quantization. This occurs due to the assumption that certain arguments are always valid, leading to an overflow if any tensor is empty. The issue affects TensorFlow versions prior to 2.1.4, between 2.2.0 and 2.2.3, 2.3.0 and 2.3.3, as well as 2.4.0 and 2.4.2. The security impact is rated as LOW with a CVSS base score of 2.5.

Understanding CVE-2021-29535

This section provides insights into the impact and technical details of the identified vulnerability.

What is CVE-2021-29535?

CVE-2021-29535 refers to a heap buffer overflow in the

QuantizedMul

operation within TensorFlow, which can be exploited by malicious actors through the use of invalid quantization thresholds.

The Impact of CVE-2021-29535

The vulnerability poses a low-severity risk, requiring local access and low user interaction to trigger a heap buffer overflow within TensorFlow.

Technical Details of CVE-2021-29535

Here we delve into the specifics of the vulnerability reported within TensorFlow.

Vulnerability Description

The vulnerability arises from the incorrect handling of empty tensors in the

QuantizedMul

function, allowing attackers to trigger a heap buffer overflow.

Affected Systems and Versions

TensorFlow versions prior to 2.1.4, between 2.2.0 and 2.2.3, 2.3.0 and 2.3.3, and 2.4.0 and 2.4.2 are susceptible to this heap buffer overflow vulnerability.

Exploitation Mechanism

Attackers can exploit the vulnerability by passing invalid thresholds for quantization, causing the

QuantizedMul

function to access empty buffers and result in overflow.

Mitigation and Prevention

This section outlines steps to mitigate the risks associated with CVE-2021-29535.

Immediate Steps to Take

Users are advised to update their TensorFlow installations to versions 2.5.0 or apply specific patches provided by TensorFlow.

Long-Term Security Practices

Maintaining up-to-date software versions and following secure coding practices can help prevent similar heap buffer overflow vulnerabilities.

Patching and Updates

TensorFlow has released patches for versions 2.1.4, 2.2.3, 2.3.3, and 2.4.2 to address the heap buffer overflow in

QuantizedMul

. Users are encouraged to apply these patches promptly.