LLM security is one of the fastest-growing fields in cybersecurity. As organizations deploy AI agents, chatbots, and RAG systems, the demand for professionals who understand LLM-specific vulnerabilities far outpaces supply. The good news: you can start learning for free today. Here's a practical roadmap for getting started.
Step 1: Understand how LLMs work
Before you can attack or defend an LLM system, you need to understand the basics: how tokenization works, what temperature and top-p control, how context windows function, and why LLMs are fundamentally next-token predictors, not reasoning engines.
You don't need a machine learning degree. Focus on practical understanding: what goes into the prompt, how the model processes it, and what comes out. PromptTrace's How LLMs Work module covers exactly this - no math required.
Step 2: Learn the attack surface
The LLM attack surface is different from traditional application security. Key areas to study:
- System prompts: How developers instruct LLMs, and how attackers extract or override them. Learn more →
- RAG (Retrieval-Augmented Generation): How external data gets injected into prompts, creating indirect injection surfaces. Learn more →
- Tool calling: How LLMs invoke external functions, and how attackers exploit trust boundaries. Learn more →
- Defenses: Filtering, guardrails, instruction hierarchy, and their limitations. Learn more →
Step 3: Practice on real models
Theory only gets you so far. You need hands-on practice against real LLMs - not simulations or regex-based challenges. This is where you develop the intuition for how models actually respond to adversarial input.
PromptTrace's free labs give you exactly this: real LLMs with specific security objectives and full visibility into the prompt stack via the Context Trace. The Gauntlet then tests your skills across 15 levels of progressively harder defenses - all completely free.
Step 4: Study the frameworks
Three essential frameworks for LLM and AI agent security professionals:
- OWASP Top 10 for LLM Applications - The definitive list of LLM vulnerabilities, maintained by the security community.
- OWASP Top 10 for Agentic AI - Covers risks specific to AI agents with tool access, including privilege compromise, RAG poisoning, and uncontrolled code execution.
- MITRE ATLAS - A knowledge base of adversarial tactics and techniques for ML systems, modeled after the ATT&CK framework.
Step 5: Build and break
The fastest path to expertise is building LLM applications and then trying to break them. Deploy a simple RAG chatbot, add tool calling, implement defenses - then attack your own system. This dual perspective (builder + attacker) is what makes great AI security professionals. Every vulnerability you discover in your own system teaches you something papers can't.