Securing LLM Applications Through Targeted Pentesting

As LLM-based applications expand, traditional security testing methods fail to address model-specific vulnerabilities. The article explains how LLM pentesting focuses on risks such as prompt injection, data leakage, model manipulation, and unsafe outputs. It outlines testing approaches including adversarial inputs, context manipulation, and evaluation of guardrails across the AI pipeline. Technology leaders should care because untested LLM behavior can introduce unpredictable security and compliance risks that standard application security practices do not cover.