February 21, 2026

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

16 minutes

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

You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cybercode_academy

...more

By CyberCode Academy

February 21, 2026

16 minutes

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

You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cybercode_academy

...more

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