Course 24 - Machine Learning for Red Team Hackers | Episode 6: Security Vulnerabilities in Machine Learning Titelbild

Course 24 - Machine Learning for Red Team Hackers | Episode 6: Security Vulnerabilities in Machine Learning

Course 24 - Machine Learning for Red Team Hackers | Episode 6: Security Vulnerabilities in Machine Learning

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 In this lesson, you’ll learn about:
  • The major security threat categories in machine learning: model stealing, inversion, poisoning, and backdoors
  • How model stealing attacks replicate black-box models through API querying
  • Why attackers may clone models to reduce costs, bypass licensing, or craft offline adversarial examples
  • The concept of model inversion, where sensitive training data (e.g., faces or private attributes) can be partially reconstructed from learned weights
  • Why deterministic model parameters can unintentionally leak information
  • How data poisoning attacks manipulate training datasets to degrade accuracy or shift decision boundaries
  • The difference between availability attacks (general performance drop) and targeted poisoning (specific misclassification goals)
  • Why some architectures—such as CNN-based systems—can appear statistically robust yet remain strategically vulnerable
  • How backdoor (trojan) attacks embed hidden triggers during training or model updates
  • Why backdoors are difficult to detect due to normal performance under standard conditions
Defensive & Mitigation Strategies This episode also highlights why ML systems must be secured across their lifecycle:
  • Restrict and monitor API query rates to reduce model extraction risk
  • Apply differential privacy and regularization to limit inversion leakage
  • Validate training datasets with integrity checks and anomaly detection
  • Use robust training techniques and adversarial testing to evaluate resilience
  • Perform model auditing and trigger scanning to detect backdoors
  • Secure the supply chain for datasets, pretrained models, and updates


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