Introduction to AI Security: What You Need to Know

What is AI Security?

Artificial intelligence systems introduce a new class of security vulnerabilities distinct from traditional software. Where classic security focuses on code execution and data exfiltration, AI security concerns itself with model behavior — how an adversary can manipulate what the model does.

Key Threat Categories

1. Adversarial Examples

Carefully crafted inputs that cause a model to misclassify or behave unexpectedly. Imperceptible to humans, but devastating to automated pipelines.

2. Model Poisoning

Injecting malicious data into training sets to corrupt the model’s learned behavior — a supply chain attack on machine learning.

3. Prompt Injection

Malicious instructions embedded in user input that hijack an LLM’s behavior, bypassing system prompts and safety guardrails.

Why It Matters

As AI systems become decision-making infrastructure — credit scoring, medical diagnosis, security tooling — adversarial robustness becomes a safety-critical concern, not an academic one.