CVE-2021-29594 : Exploit Details and Defense Strategies
Learn about CVE-2021-29594 where TensorFlow's TFLite's convolution code allows division by zero. Understand the impact, affected versions, and mitigation steps.
TensorFlow is an end-to-end open source platform for machine learning. This vulnerability exists in TFLite's convolution code, where the divisor controlled by the user is not checked to be non-zero. The affected versions include TensorFlow < 2.1.4, >= 2.2.0 and < 2.2.3, >= 2.3.0 and < 2.3.3, and >= 2.4.0 and < 2.4.2. The impact is rated as low with a base score of 2.5 in the CVSSv3.1 metrics. Immediate action and patching are recommended to mitigate this vulnerability.
Understanding CVE-2021-29594
This section explores the details of CVE-2021-29594 within TensorFlow's TFLite's convolution code.
What is CVE-2021-29594?
TensorFlow's TFLite's convolution code contains a vulnerability that allows multiple divisions where the divisor is under user control and not validated to be non-zero.
The Impact of CVE-2021-29594
The impact is considered low with a base score of 2.5 according to CVSSv3.1 metrics. Confidentiality and integrity impacts are none, with low privileges required for exploitation.
Technical Details of CVE-2021-29594
This section delves into the technical aspects of the vulnerability.
Vulnerability Description
The vulnerability arises from unchecked user-controlled divisors in TFLite's convolution code.
Affected Systems and Versions
The affected versions include TensorFlow < 2.1.4, >= 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 exploitation involves manipulating user-controlled divisors to trigger potentially harmful division.
Mitigation and Prevention
This section focuses on mitigation strategies to address CVE-2021-29594.
Immediate Steps to Take
Immediate actions include updating to TensorFlow 2.5.0, or applying patches for TensorFlow 2.4.2, 2.3.3, 2.2.3, and 2.1.4.
Long-Term Security Practices
Implementing secure coding practices and regular security audits can help prevent similar vulnerabilities in the future.
Patching and Updates
Regularly monitor and apply security patches released by TensorFlow to stay protected against known vulnerabilities.