LLM Security in Practice
Large Language Models (LLMs) like GPT-4, Claude, Mistral, and open-source alternatives are transforming the way we build applications. They’re powering chatbots, copilots, retrieval systems, autonomous agents, and enterprise search — quickly becoming central to everything from productivity tools to customer-facing platforms.
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..
Course Content
- Module 1: Introduction to LLM Application Security –> 7 lectures • 35min.
- Module 2: LLM01:2025 – Prompt Injection –> 7 lectures • 37min.
- Module 3: LLM02:2025 – Sensitive Information Disclosure –> 6 lectures • 37min.
- Module 4: LLM03:2025 – Supply Chain –> 6 lectures • 30min.
- Module 5: LLM04:2025 – Data and Model Poisoning –> 6 lectures • 27min.
- Module 6: LLM05:2025 – Improper Output Handling –> 5 lectures • 23min.
- Module 7: LLM06:2025 – Excessive Agency –> 6 lectures • 26min.
- Module 8: LLM07:2025 – System Prompt Leakage –> 6 lectures • 28min.
- Module 9: LLM08:2025 – Vector and Embedding Weaknesses –> 6 lectures • 32min.
- Module 10: LLM09:2025 – Misinformation –> 6 lectures • 30min.
- Module 11: LLM10:2025 – Unbounded Consumption –> 6 lectures • 34min.
- Module 12: Best Practices and Future Trends in LLM Security –> 6 lectures • 32min.
Requirements
Large Language Models (LLMs) like GPT-4, Claude, Mistral, and open-source alternatives are transforming the way we build applications. They’re powering chatbots, copilots, retrieval systems, autonomous agents, and enterprise search — quickly becoming central to everything from productivity tools to customer-facing platforms.
But with that innovation comes a new generation of risks — subtle, high-impact vulnerabilities that don’t exist in traditional software architectures. We’re entering a world where inputs look like language, exploits hide inside documents, and attackers don’t need code access to compromise your system.
This course is built around the OWASP Top 10 for LLM Applications (2025) — the most comprehensive and community-vetted security framework for generative AI systems available today.
Whether you’re working with OpenAI’s APIs, Anthropic’s Claude, open-source LLMs via Hugging Face, or building proprietary models in-house, this course will teach you how to secure your LLM-based architecture from design through deployment.
You’ll go deep into the vulnerabilities that matter most:
- How prompt injection attacks hijack model behavior with just a few well-placed words.
- How data and model poisoning slip through fine-tuning pipelines or vector stores.
- How sensitive information leaks, not through bugs, but through prediction.
- How models can be tricked into using tools, calling APIs, or consuming resources far beyond what you intended.
- And how LLM systems can be scraped, cloned, or manipulated without ever touching your backend.
But more importantly — you’ll learn how to stop these risks before they start.
This isn’t a high-level overview or a dry list of threats. It’s a practical, story-driven, security-focused deep dive into how modern LLM apps fail — and how to build ones that don’t.