LLM Red Teaming deliberately challenges large language models with adversarial, unexpected, and high-risk inputs to uncover vulnerabilities and unsafe behaviour. It helps identify weaknesses before they can impact real users, systems, or business operations.
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Probe the model for potential exposure of confidential, private, or sensitive information. Identifying leakage risks helps strengthen privacy controls and data protection.
Challenge the model with ambiguous, misleading, or difficult questions to detect unsupported or inaccurate responses. This evaluates how reliably the model handles uncertainty and factual limitations.
Use adversarial prompts and multi-step scenarios to test whether the model can be pushed beyond its intended behaviour. This reveals how consistently the model maintains its safety boundaries under pressure.