Lessons Learned from Building & Defending LLM Applications

Challenges in Securing LLM Applications

Deploying large language models into production introduces new vulnerability classes alongside traditional application security challenges. Securing AI applications requires moving beyond theoretical threat models to address real-world operational edge cases.

Vulnerability Patterns and Defense Strategies

Critical risks in LLM architectures include untrusted context injection, unauthorized tool execution, and insecure data retrieval pipelines. Implementing defensive guardrails, rigorous input validation, and output filtering strengthens application robustness against adversarial exploitation.