LLM Prompt Injection: Attacks and Defenses
LLM applications are under active attack right now—and most teams lack the defensive playbook. This course walks you through real prompt injection vectors, how attackers exploit them, and the hardened approaches that actually work in production.
AIU.ac Verdict: Essential for anyone shipping LLM products, security engineers moving into AI, and platform teams responsible for model safety. Best suited to those with baseline LLM familiarity; doesn’t cover foundational transformer mechanics.
What This Course Covers
You’ll dissect prompt injection from both attacker and defender perspectives: indirect injections via retrieval systems, jailbreak patterns, token smuggling, and context window manipulation. The course maps real-world attack scenarios—supply chain poisoning, user-controlled inputs, multi-turn exploitation—against practical mitigations including input sanitisation, prompt structuring, and model-level guardrails.
Gavin Johnson-Lynn covers defensive architecture patterns: sandboxing, output validation, semantic consistency checks, and monitoring strategies. You’ll see hands-on examples of vulnerable implementations and how to retrofit security without crippling usability. The focus stays on what actually scales in production, not theoretical edge cases.
Who Is This Course For?
Ideal for:
- LLM Product Engineers: Building customer-facing applications with Claude, GPT, or open models. You need to ship safely and fast.
- Security Engineers Pivoting to AI: Your traditional threat modelling skills transfer here, but LLM attack surface is novel. This closes that gap quickly.
- Platform & Infrastructure Teams: Responsible for LLM governance, content moderation, or multi-tenant safety. Injection defence is your baseline requirement.
May not suit:
- LLM Researchers: This is applied security, not mechanistic interpretability or adversarial ML theory. You’ll find it tactical rather than foundational.
- Complete Beginners to AI: Assumes you understand how LLMs work at a user level. Start with a fundamentals course first.
Frequently Asked Questions
How long does LLM Prompt Injection: Attacks and Defenses take?
1 hour 7 minutes. Designed for busy engineers—you can complete it in a single focused session or break it into 20-minute segments.
Do I need prior security or LLM experience?
You should be comfortable with how LLMs work (prompting, basic architecture). Security background helps but isn’t required—the course teaches attack patterns from first principles.
Will this cover hands-on labs or just theory?
Pluralsight courses include interactive labs and sandboxes. Expect practical demonstrations of attacks and defensive implementations you can replicate.
Is this relevant if I’m using a managed LLM API like OpenAI?
Absolutely. API-based products are still vulnerable to prompt injection through user inputs and retrieval systems. The defences covered apply regardless of whether you host the model.
Course by Gavin Johnson-Lynn on Pluralsight. Duration: 1h 7m. Last verified by AIU.ac: March 2026.
