What you'll learn:
- Understand the top 10 security risks in LLM-based applications, as defined by the OWASP LLM Top 10 (2025).
- Identify real-world vulnerabilities like prompt injection, model poisoning, and sensitive data exposure — and how they appear in production systems.
- Learn practical, system-level defense strategies to protect LLM apps from misuse, overuse, and targeted attacks.
- Gain hands-on knowledge of emerging threats such as agent-based misuse, vector database leaks, and embedding inversion.
- Explore best practices for secure prompt design, output filtering, plugin sandboxing, and rate limiting.
- Stay ahead of AI-related regulations, compliance challenges, and upcoming security frameworks.
- Build the mindset of a secure LLM architect — combining threat modeling, secure design, and proactive monitoring.
The New Language of Risk
The world of software has changed. We have moved from a world of rigid code to a world of fluid language. While Large Language Models (LLMs) like GPT-4, Claude, and Mistral are revolutionizing application architecture, they have introduced a shadow dimension of risk—vulnerabilities that traditional firewalls and scanners simply cannot see.
In this new reality, an "exploit" isn't a malicious script; it’s a carefully crafted sentence. An "injection" doesn't require a database flaw; it just requires a document with hidden intent. This course is your tactical guide to the 2026 OWASP Top 10 for LLM Applications, the definitive security framework for the Generative AI era.
Decoding the Failure Patterns of AI
This isn't a dry list of theoretical threats. It is a practical, narrative-driven autopsy of how modern AI systems actually break. We move beyond the hype to explore the high-impact vulnerabilities that are currently reshaping the threat landscape:
Prompt Injection (The New SQLi): You will witness how model behavior can be hijacked by "jailbreaks" and "indirect injections" hidden in third-party data.
Training Data Poisoning: Learn how an adversary can compromise a fine-tuning pipeline or a vector store to "program" your model with a secret backdoor.
Sensitive Information Disclosure: We explore how models "leak" data through prediction—not because of a bug, but because of how they were trained.
Insecure Output Handling: Discover what happens when a model is tricked into executing malicious code or calling sensitive APIs on behalf of an attacker.
Model Denial of Service: Learn how "heavy prompts" can bankrupt your token budget or crash your inference infrastructure.
Architecting the AI Fortress
Understanding the attack is only half the battle. This course focuses on defensive architecture, giving you the blueprints to build "Secure-by-Design" AI systems.
You will master the "Pro-Level" defensive stack:
The Guardrail Layer: Implementing robust input/output filtering that goes beyond simple blacklists.
RAG Security (Retrieval-Augmented Generation): Securing the "Search-and-Retrieve" loop to prevent data exfiltration and "hallucination-driven" exploits.
Agentic Governance: Designing autonomous agents that have strict "Least Privilege" access to your tools and APIs.
Model Provenance: Ensuring the integrity of your supply chain, from Hugging Face model weights to proprietary fine-tuning sets.
Practical, Story-Driven Mastery
Every module in this course is grounded in real-world "Account-Style" case studies. You won't just study a vulnerability; you will walk through the story of a breach—understanding the attacker’s decision points, the architect’s failed assumptions, and the specific controls that would have stopped the attack.
Whether you are building with OpenAI’s APIs, Anthropic’s Claude, or deploying proprietary models in-house, this course equips you with the mindset of an AI security specialist.
The Outcome
By the end of this journey, you won't see the OWASP Top 10 as a compliance hurdle. You will see it as a tactical map of the modern attack surface—and you will possess the specialized skills to design, deploy, and defend the intelligent systems of tomorrow.
The perimeter has shifted to the prompt. Are you ready to defend it?
