Introduction
Machine Learning (ML) is now used in many sensitive areas. This creates new security risks that go beyond traditional IT security. This is exactly what the OWASP ML Top 10 is all about.
What It’s About
The list describes the most critical vulnerabilities for systems using Machine Learning. These include manipulated inputs, poisoned training data, model theft, and attacks on outputs. It is crucial that not only the model itself is protected, but the entire surrounding environment as well.
Examples
Even small changes to inputs can deceive a model. Similarly, incorrect or intentionally manipulated training data can lead to wrong decisions later on. Additionally, there is a risk that a model can be reconstructed through information gathering via numerous queries.
Conclusion
Security in Machine Learning is not a one-off measure. Clean data, trustworthy models, and secure operations are essential. The OWASP ML Top 10 helps to better understand these risks and address them systematically.
by Rian Friedt / Offensive Security Specialist