CVE-2021-37692 : Vulnerability Insights and Analysis
Discover the impact and technical details of CVE-2021-37692, a vulnerability in TensorFlow affecting versions >= 2.5.0 < 2.5.1. Learn about the mitigation strategies and immediate steps to secure your systems.
A detailed overview of CVE-2021-37692, a vulnerability in TensorFlow related to string tensors with mismatched dimensions triggering a segfault.
Understanding CVE-2021-37692
This section delves into the impact, technical details, and mitigation strategies concerning CVE-2021-37692.
What is CVE-2021-37692?
TensorFlow's vulnerability allows Go code, under certain conditions, to cause a segfault due to string tensor deallocation, affecting versions >= 2.5.0 and < 2.5.1.
The Impact of CVE-2021-37692
The vulnerability's CVSS score is 5.5 (Medium Severity) with high availability impact but no confidentiality or integrity impact. Privileges required are low, and the attack complexity is low.
Technical Details of CVE-2021-37692
This section explores the vulnerability description, affected systems, versions, and exploitation mechanism.
Vulnerability Description
When handling string tensors with mismatched dimensions, a segfault occurs during deallocation due to assumptions made during encoding, impacting TensorFlow versions >= 2.5.0 and < 2.5.1.
Affected Systems and Versions
The vulnerability affects TensorFlow versions >= 2.5.0 and < 2.5.1 that mishandle string tensor deallocation, leading to a segfault.
Exploitation Mechanism
Under specific conditions, Go code can trigger a segfault by mishandling string tensor deallocation in TensorFlow due to encoding assumptions.
Mitigation and Prevention
This section outlines the immediate steps to take, long-term security practices, and the importance of patching and updates.
Immediate Steps to Take
Developers should apply the patched fix from GitHub commit 8721ba96e5760c229217b594f6d2ba332beedf22. Users should upgrade to TensorFlow 2.6.0 or the forthcoming TensorFlow 2.5.1 patch containing the fix.
Long-Term Security Practices
Ensure input validation and thorough testing to prevent similar vulnerabilities. Follow TensorFlow's security advisories and implement secure coding practices.
Patching and Updates
Regularly check for updates from TensorFlow's official sources, apply security patches promptly, and maintain up-to-date versions to mitigate potential risks.