Comprehensive Analysis of LLM Guardrails Approaches Preventing Harmful Content and Jailbreak Attacks

LLMs (Large Language Models) have become increasingly important, with chatbots being widely used in commercial settings to assist employees and answer customer questions. To protect a company's reputation and ensure compliance, it's crucial that chatbots do not generate harmful content, even in the face of deliberate jailbreak attacks. Researchers propose various methods to secure LLMs, known as guardrails, to prevent harmful content generation and jailbreak attacks. This article aims to comprehensively analyze existing guardrail solutions and provide guidelines for selecting the optimal solution for specific scenarios. The study compared six different guardrail methods across three versions of LLMs (Mistral Large 24.02, Meta Llama 3-8B Instruct, Anthropic Claude 3.5 Sonnet), including two baseline approaches, two cloud-based solutions (AWS Guardrails, Azure AI Content Safety), and two other popular non-cloud solutions (NeMo by Nvidia and Llama Guard by Meta). Thirteen datasets were used for evaluation: ten representing harmful questions in jailbreak attacks and three with neutral prompts similar to harmful questions to check for excessive blocking. The best results were achieved by AWS Guardrails (averaged accuracy across models 96.8%) and NeMo (93.9%) The results clearly showed that using guardrails is essential when building commercial applications based on LLMs due to advancements in effective jailbreak attacks.