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NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Rails

About

NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems. Guardrails (or rails for short) are a specific way of controlling the output of an LLM, such as not talking about topics considered harmful, following a predefined dialogue path, using a particular language style, and more. There are several mechanisms that allow LLM providers and developers to add guardrails that are embedded into a specific model at training, e.g. using model alignment. Differently, using a runtime inspired from dialogue management, NeMo Guardrails allows developers to add programmable rails to LLM applications - these are user-defined, independent of the underlying LLM, and interpretable. Our initial results show that the proposed approach can be used with several LLM providers to develop controllable and safe LLM applications using programmable rails.

Traian Rebedea, Razvan Dinu, Makesh Sreedhar, Christopher Parisien, Jonathan Cohen• 2023

Related benchmarks

TaskDatasetResultRank
Safety DetectionAgentHazard Strongest
Accuracy28.94
56
Agent Safety EvaluationToolEmu
Safety76
36
Agent Safety EvaluationAgent-SafetyBench aggregated clean and five attack types
UBR40.79
30
Agent Safety EvaluationAgentHarm Benign Requests
Safety Score53
27
Agent Safety EvaluationAgentHarm Libra
Score66
27
Agent Safety EvaluationAgentHarm Harmful Requests
Score13
27
Agentic Safety and Utility EvaluationPowerSeeking Bench
Safety Score0.84
24
Safety DetectionATBench-500
Accuracy49.9
14
Jailbreak DefenseSafety Guardrail Evaluation Set
Char Noise Robustness0.00e+0
6
Injection DetectionSynthetic Educational AI Programming Tutor (holdout)
Bypass Rate0.00e+0
5
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