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.
Syllabus
- Introduction to LLM Application Security
- Prompt Injection
- Sensitive Information Disclosure
- Supply Chain
- Data and Model Poisoning
- Improper Output Handling
- Excessive Agency
- System Prompt Leakage
- Vector and Embedding Weaknesses
- Misinformation
- Unbounded Consumption
- Best Practices and Future Trends in LLM Security
