CVE-2021-29553 : Security Advisory and Response

TensorFlow is an open-source platform for machine learning. The vulnerability allows an attacker to read data outside the bounds of a heap-allocated buffer in

tf.raw_ops.QuantizeAndDequantizeV3

due to a lack of validation on user-supplied input attributes. The issue affects TensorFlow versions less than 2.1.4, and versions between 2.2.0 to 2.4.2. Read on to understand the impact, technical details, and mitigation steps.

Understanding CVE-2021-29553

This section provides insights into the vulnerability discovered in TensorFlow.

What is CVE-2021-29553?

CVE-2021-29553 is a vulnerability in TensorFlow that allows an attacker to read data outside the bounds of a heap-allocated buffer in

tf.raw_ops.QuantizeAndDequantizeV3

due to the lack of input validation.

The Impact of CVE-2021-29553

The impact of this vulnerability is considered low, with a base score of 2.5 in the CVSSv3.1 scoring system. While it requires low privileges and has low availability impact, the attack complexity is rated as high due to the local vector.

Technical Details of CVE-2021-29553

Explore the technical aspects of the vulnerability to understand its implications.

Vulnerability Description

The vulnerability arises from improper validation of user input attributes, allowing an attacker to read data beyond the allocated buffer.

Affected Systems and Versions

The vulnerability affects TensorFlow versions less than 2.1.4, as well as versions between 2.2.0 to 2.4.2.

Exploitation Mechanism

The issue occurs due to the unchecked use of a user-supplied input attribute to index an array, leading to data leakage.

Mitigation and Prevention

Learn about the steps to mitigate the risks posed by CVE-2021-29553 and prevent potential exploitation.

Immediate Steps to Take

Users are advised to update their TensorFlow installations to version 2.5.0, which includes the necessary patches to address the vulnerability.

Long-Term Security Practices

To enhance security, it is recommended to follow best practices such as regular security updates, secure coding practices, and monitoring for vulnerabilities.

Patching and Updates

Keep your TensorFlow installations up to date with the latest patches and security updates to protect against known vulnerabilities.