Introduction
Machine learning security concerns
()
What you should know
()
1. Machine Learning Foundations
How systems can fail and how to protect them
()
Why does ML security matter
()
Attacks vs. unintentional failure modes
()
Security goals for ML: CIA
()
2. Intentional Failure Modes/Attacks
Perturbation attacks and AUPs
()
Poisoning attacks
()
Reprogramming neural nets
()
Physical domain (3D adversarial objects)
()
Supply chain attacks
()
Model inversion
()
System manipulation
()
Membership inference and model stealing
()
Backdoors and existing exploits
()
3. Unintentional Failure Modes/Intrinsic Design Flaws
Reward hacking
()
Side effects in reinforcement learning
()
Distributional shifts and incomplete testing
()
Overfitting/underfitting
()
Data bias considerations
()
4. Building Resilient ML
Effective techniques for building resilience in ML
()
ML dataset hygiene
()
ML adversarial training
()
ML access control to APIs
()
Ex_Files_ML_and_AI_Security_Risk_Categorizing_Attacks.zip
(33 KB)