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LinkedIn Learning

Security Risks in AI and Machine Learning: Categorizing Attacks and Failure Modes

via LinkedIn Learning

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Overview

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This course explores building robust, resilient ML systems by addressing intentional adversary attacks and unintentional failures.

Syllabus

Introduction
  • Machine learning security concerns
1. AI Foundations
  • How AI systems can fail and how to protect them
  • Why AI security matters
  • Attacks vs. unintentional failure modes
  • ML security frameworks
  • Security goals for ML: CIA
2. Intentional Failure Modes and Attacks
  • Perturbation attacks and malicious input
  • Poisoning attacks
  • Reprogramming
  • 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 and Intrinsic Design Flaws
  • Reward hacking
  • Side effects and misalignment
  • Distributional shifts and incomplete testing
  • Overfitting and underfitting
  • Data bias considerations
4. Building Resilient AI
  • Effective techniques for building resilience in AI
  • Threat modeling AI
  • Dataset threat model
  • Adversarial testing and red teaming
  • API access and supporting components
  • Supply chain
Conclusion
  • Next steps in your AI security journey

Taught by

Diana Kelley

Reviews

4.6 rating at LinkedIn Learning based on 32 ratings

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