Side effects in reinforcement learning

Machine learning security concerns

What you should know

How systems can fail and how to protect them

Why does ML security matter

Attacks vs. unintentional failure modes

Security goals for ML: CIA

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

Reward hacking

Distributional shifts and incomplete testing

Overfitting/underfitting

Data bias considerations

Effective techniques for building resilience in ML

ML dataset hygiene

ML adversarial training

ML access control to APIs

Next steps