Day 01 of learning Adversarial AI 📚
📚 Day 01/30 of Learning Adversarial AI
📚 Introduction to AI Security
Artificial Intelligence systems introduce new security risks because they are built around data, models, and automated decision-making. Traditional software follows fixed rules written by programmers, but AI systems learn patterns from large datasets.
This means attackers can manipulate the data, the model, or the environment where the AI operates. Because of this learning behavior, AI systems create new attack surfaces that did not exist in traditional software systems.
In traditional cybersecurity, attackers exploit software bugs or weak authentication. In AI security, attackers target the learning process itself. For example, they may manipulate training data (data poisoning) or create adversarial inputs that fool models with small changes.
Real-world examples include stickers on road signs fooling autonomous vehicles and specially designed glasses bypassing facial recognition systems.
📚 ML Pipeline Attack Surface
Machine learning systems are not just models. They consist of multiple stages, and each stage can be attacked.
📌 Data Collection
Attackers may inject malicious data into datasets, causing models to learn incorrect patterns.
📌 Data Preprocessing
Manipulating preprocessing pipelines can silently alter data distribution or inject hidden triggers.
📌 Model Training
Backdoor attacks can be introduced so models behave normally but fail under specific conditions.
📌 Model Deployment
Attackers may steal model behavior through repeated queries, known as model extraction.
📌 Inference
Adversarial examples can trick models by slightly modifying input data while keeping it visually unchanged.
📌 Conclusion
Understanding these attack surfaces is the first step in adversarial AI. Every stage of the ML pipeline can be a potential vulnerability, from data collection to final predictions.
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