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

In this lesson, you’ll learn about:The major security threat categories in machine learning: model stealing, inversion, poisoning, and backdoorsHow model stealing attacks replicate black-box models through API queryingWhy attackers may clone models to reduce costs, bypass licensing, or craft offline adversarial examplesThe concept of model inversion, where sensitive training data (e.g., faces or private attributes) can be partially reconstructed from learned weightsWhy deterministic model parameters can unintentionally leak informationHow data poisoning attacks manipulate training datasets to degrade accuracy or shift decision boundariesThe 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 vulnerableHow backdoor (trojan) attacks embed hidden triggers during training or model updatesWhy backdoors are difficult to detect due to normal performance under standard conditionsDefensive & 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 riskApply differential privacy and regularization to limit inversion leakageValidate training datasets with integrity checks and anomaly detectionUse robust training techniques and adversarial testing to evaluate resiliencePerform model auditing and trigger scanning to detect backdoorsSecure the supply chain for datasets, pretrained models, and updatesYou can listen and download our episodes for free on more than 10 different platforms:https://linktr.ee/cybercode_academy