LLM Red Teaming Explained: Testing AI Systems for Prompt Injection and Jailbreaks

As AI-powered products become widespread, traditional security testing methods are proving insufficient for identifying vulnerabilities unique to large language models. LLM red teaming is a structured practice where security teams probe AI systems with adversarial inputs to uncover risks such as prompt injection, jailbreaks, and data leakage before and after deployment. Unlike conventional penetration testing, LLM security assessment must account for probabilistic model behavior, meaning the same attack can succeed in some attempts and fail in others. Teams are advised to track Attack Success Rate across multiple test runs rather than relying on single pass/fail outcomes. Frameworks such as OWASP GenAI LLM Top 10 and MITRE ATLAS offer established taxonomies to help organize and prioritize these adversarial evaluations.