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LLM Evaluation, Benchmarks & Safety Guardrails
MMLU, HumanEval, LLM-as-a-Judge, NeMo Guardrails, and Prompt Injection defense
Deploying Generative AI into enterprise environments requires rigorous evaluation harnesses, automated benchmarking, and safety guardrails to defend against prompt injection.
Evaluation Frameworks & Safety Protocols
Standard Benchmarks: MMLU (Massive Multitask Language Understanding) tests broad academic knowledge; HumanEval tests code synthesis capability; GSM8K measures grade school math reasoning.
LLM-as-a-Judge: Using a state-of-the-art model (like GPT-4) to evaluate smaller task-specific models against rubrics covering correctness, tone, and conciseness.
Prompt Injection Defense: Direct prompt injection occurs when a user tries to override system instructions ("Ignore all previous rules..."). Indirect injection occurs when untrusted retrieved content contains malicious hidden directives. Defenses include input sanitization, dual-LLM architectures, and output guardrails.
Never give an autonomous agent unconstrained shell or database write access without human-in-the-loop approval or strict schema constraints.
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Test Yourself With Exercise
What is an indirect prompt injection attack in an LLM application?