Learn about CVE-2022-29212, affecting TensorFlow versions < 2.6.4, >= 2.7.0rc0 & < 2.7.2, >= 2.8.0rc0 & < 2.8.1, and >= 2.9.0rc0 & < 2.9.0. Understand the impact of this vulnerability and effective mitigation strategies.
CVE-2022-29212, also known as 'Core dump when loading TFLite models with quantization in TensorFlow,' affects TensorFlow versions prior to 2.9.0, 2.8.1, 2.7.2, and 2.6.4. It can cause TFLite models to crash when loaded due to an issue during quantization. Learn more about this vulnerability and how to mitigate it.
Understanding CVE-2022-29212
This section provides an overview of the vulnerability and its impact.
What is CVE-2022-29212?
TensorFlow versions prior to 2.9.0, 2.8.1, 2.7.2, and 2.6.4 are susceptible to a vulnerability where TFLite models crash when loaded in the TFLite interpreter due to incorrect assumptions during quantization.
The Impact of CVE-2022-29212
The vulnerability can lead to a core dump when loading TFLite models with quantization, affecting the availability of TensorFlow for machine learning tasks.
Technical Details of CVE-2022-29212
This section delves into the specifics of the vulnerability.
Vulnerability Description
During quantization, the scale of values could be greater than 1, causing the
TFLITE_CHECK_LT
assertion to trigger and crash the process when loading affected TFLite models.
Affected Systems and Versions
TensorFlow versions prior to 2.9.0, 2.8.1, 2.7.2, and 2.6.4 are impacted by this vulnerability in TFLite models using the TFLite model converter.
Exploitation Mechanism
The issue arises from incorrect assumptions about sub-unit scaling during quantization, leading to the triggering of assertions and process termination.
Mitigation and Prevention
This section outlines steps to mitigate and prevent exploitation of the CVE-2022-29212 vulnerability.
Immediate Steps to Take
Users are advised to update TensorFlow to versions 2.9.0, 2.8.1, 2.7.2, or 2.6.4 containing a patch to address the issue.
Long-Term Security Practices
Developers should follow best practices in input validation and code testing to prevent similar vulnerabilities in the future.
Patching and Updates
Regularly update TensorFlow to the latest versions to ensure that critical security patches are applied.