Prompt injection attacks pose a significant security risk as LLMs are deployed in more applications. This article outlines an engineering pipeline for detecting and mitigating such attacks, including input sanitization, anomaly detection, and layered defense strategies. The approach moves beyond theoretical discussion to provide actionable patterns for developers building LLM-based products. Key techniques include monitoring for suspicious patterns, rate limiting, and context-aware filtering. As the threat landscape evolves, having a robust detection and response mechanism is essential for maintaining trust and safety in AI systems. This content is highly relevant for any team deploying LLMs in production.
A practical guide to building detection and defense pipelines for prompt injection attacks in LLM applications.