CVE-2021-29603 : Security Advisory and Response

A specially crafted TFLite model could trigger an out-of-bounds write on heap in TensorFlow, potentially leading to security vulnerabilities. It impacts versions less than 2.1.4, as well as versions between 2.2.0 and 2.4.2.

Understanding CVE-2021-29603

This vulnerability affects TensorFlow, an open-source machine learning platform. The issue arises from a specific TFLite model triggering an out-of-bounds write on heap in the TFLite implementation of

ArgMin

/

ArgMax

.

What is CVE-2021-29603?

TensorFlow is susceptible to a crafted TFLite model that can trigger an out-of-bounds write on heap in the TFLite implementation of

ArgMin

/

ArgMax

. This can potentially lead to heap corruption and security risks.

The Impact of CVE-2021-29603

The vulnerability has a CVSS base score of 2.5 (Low). The attack complexity is rated as High, with a Local attack vector and Low availability impact. No confidentiality or integrity impacts are reported, and only low privileges are required for exploitation.

Technical Details of CVE-2021-29603

The vulnerability arises due to incorrect handling of

axis_value

in the TensorFlow code. It affects various versions of TensorFlow, specifically those less than 2.1.4 and between 2.2.0 to 2.4.2.

Vulnerability Description

A specially crafted TFLite model can lead to an out-of-bounds write on heap in the TFLite implementation of

ArgMin

/

ArgMax

, potentially causing heap corruption.

Affected Systems and Versions

Versions affected include TensorFlow less than 2.1.4, as well as versions between 2.2.0 and 2.4.2.

Exploitation Mechanism

By exploiting this vulnerability, an attacker could trigger an out-of-bounds write on heap, leading to potential security risks.

Mitigation and Prevention

It is crucial to take immediate steps to address this vulnerability in TensorFlow to ensure the security of your systems.

Immediate Steps to Take

Consider applying the necessary patches provided by TensorFlow to mitigate the risk of exploitation. Ensure that your TensorFlow installation is updated to a secure version to prevent any malicious activities.

Long-Term Security Practices

Incorporate secure coding practices and regularly update TensorFlow to the latest secure versions to mitigate potential vulnerabilities.

Patching and Updates

Stay informed about security advisories from TensorFlow, and apply patches promptly to address any known vulnerabilities and enhance the security of your systems.