CVE-2021-29529 : 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

tf.raw_ops.QuantizedResizeBilinear

by manipulating input values, leading to a potential security risk. The issue arises from a float rounding error in accessing image elements.

Understanding CVE-2021-29529

This CVE identifies a vulnerability in TensorFlow that allows attackers to exploit a heap buffer overflow by manipulating input values.

What is CVE-2021-29529?

The vulnerability in TensorFlow can be exploited through the manipulation of input values, causing a heap buffer overflow by leading to float rounding errors. This overflow occurs due to an off-by-one error in accessing image elements.

The Impact of CVE-2021-29529

The impact of this CVE is assessed as having a low base severity but with a high attack complexity. While the confidentiality and integrity impact are rated as none, attackers with low privileges could trigger a heap buffer overflow, potentially causing system instability or crashes.

Technical Details of CVE-2021-29529

This section provides further technical insights into the vulnerability.

Vulnerability Description

The vulnerability is caused by a heap buffer overflow resulting from float rounding errors in the

tf.raw_ops.QuantizedResizeBilinear

function, which mishandles input values, leading to an off-by-one error.

Affected Systems and Versions

The impacted versions of TensorFlow include < 2.1.4, >= 2.2.0, < 2.2.3, >= 2.3.0, < 2.3.3, and >= 2.4.0, < 2.4.2.

Exploitation Mechanism

Attackers manipulate input values to trigger float rounding errors, causing an off-by-one error in accessing image elements, ultimately leading to a heap buffer overflow.

Mitigation and Prevention

To address and prevent this vulnerability, consider the following measures:

Immediate Steps to Take

Update TensorFlow to version 2.5.0 to apply the fix for this vulnerability.
For versions still within support, such as TensorFlow 2.4.2, 2.3.3, 2.2.3, and 2.1.4, apply the fix by cherrypicking the commit that addresses this issue.

Long-Term Security Practices

Regularly update TensorFlow to ensure you are running the latest secure version.
Conduct security audits on your TensorFlow implementations to identify and address any potential vulnerabilities.

Patching and Updates

Stay informed about security advisories and patches released by TensorFlow to promptly address any security vulnerabilities and apply recommended updates.