CVE-2021-29569 : Exploit Details and Defense Strategies

A vulnerability has been discovered in TensorFlow which can allow an attacker to read outside the bounds of heap allocated data by supplying specially crafted inputs. This can lead to a security risk in affected versions of TensorFlow. The following article provides an in-depth analysis of CVE-2021-29569.

Understanding CVE-2021-29569

This section delves into the details of the vulnerability including its description, impact, affected systems, and mitigation steps.

What is CVE-2021-29569?

CVE-2021-29569 is a vulnerability in the TensorFlow platform that allows an attacker to read outside the bounds of heap allocated data by providing specially crafted inputs.

The Impact of CVE-2021-29569

The vulnerability poses a low severity risk with high attack complexity. It requires low privileges and user interaction. The affected systems include 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.

Technical Details of CVE-2021-29569

This section provides technical details related to the vulnerability.

Vulnerability Description

The vulnerability arises from the implementation of

tf.raw_ops.MaxPoolGradWithArgmax

, which can cause reads outside the bounds of heap allocated data due to assumptions made regarding the input tensors. The fix for this issue will be incorporated in TensorFlow version 2.5.0.

Affected Systems and Versions

Affected versions of TensorFlow include those 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.

Exploitation Mechanism

The vulnerability can be exploited by providing specially crafted inputs to the affected TensorFlow functions, leading to unauthorized access to memory.

Mitigation and Prevention

This section outlines steps to mitigate the risks associated with CVE-2021-29569.

Immediate Steps to Take

Users are advised to update their TensorFlow installations to version 2.5.0 or later to address this vulnerability. Additionally, it is recommended to apply patches provided by the TensorFlow team.

Long-Term Security Practices

To enhance security, users should follow best practices such as input validation, secure coding, and staying updated with the latest security advisories.

Patching and Updates

Regularly check for security updates from TensorFlow and apply patches promptly to ensure your system is protected against known vulnerabilities.