CVE-2022-23559 : Exploit Details and Defense Strategies
Discover the details of CVE-2022-23559, an integer overflow vulnerability in Tensorflow Lite (TFLite) affecting versions >= 2.5.3. Learn about its impact, affected systems, and mitigation steps.
Tensorflow is an Open Source Machine Learning Framework. An attacker can craft a TFLite model that would cause an integer overflow in embedding lookup operations. Learn more about CVE-2022-23559 below.
Understanding CVE-2022-23559
This section provides insights into the nature and impact of the vulnerability.
What is CVE-2022-23559?
CVE-2022-23559 involves an integer overflow vulnerability in Tensorflow Lite (TFLite) that could lead to heap out-of-bounds read/write scenarios. A malicious actor can exploit this by manipulating certain user-provided values, triggering overflows during multiplication.
The Impact of CVE-2022-23559
The impact of this vulnerability is rated high, with a CVSS base score of 8.8. It can result in confidentiality, integrity, and availability impacts on affected systems running vulnerable Tensorflow versions.
Technical Details of CVE-2022-23559
Delve deeper into the technical aspects of CVE-2022-23559 to understand its implications.
Vulnerability Description
The vulnerability arises from an integer overflow in embedding lookup operations in TFLite, allowing for heap out-of-bounds read/write, posing a significant security risk.
Affected Systems and Versions
Tensorflow versions >= 2.7.0 and < 2.7.1, >= 2.6.0 and < 2.6.3, and < 2.5.3 are confirmed to be affected by this vulnerability, requiring immediate attention.
Exploitation Mechanism
By crafting a TFLite model to manipulate 'embedding_size' and 'lookup_size' values, threat actors can exploit this vulnerability to trigger overflows and potentially gain unauthorized access.
Mitigation and Prevention
Explore the necessary steps to mitigate and prevent the exploitation of CVE-2022-23559.
Immediate Steps to Take
Users are strongly advised to upgrade to a patched version of Tensorflow to address this vulnerability and enhance system security.
Long-Term Security Practices
Implementing secure coding practices and regularly updating software can help forestall future security threats and vulnerabilities.
Patching and Updates
Stay informed about security patches and updates released by Tensorflow to promptly address any potential security flaws.