GUARD: Guideline Upholding Test through Adaptive Role-play and Jailbreak Diagnostics for LLMs
Keywords: Large Language Models, Jailbreak, Red-teaming, Safety
Abstract: As Large Language Models (LLMs) become increasingly integral to various domains, their potential to generate harmful responses has prompted significant societal and regulatory concerns. In response, governments, including the European Union, have issued ethics guidelines to promote the development of trustworthy AI. However, these guidelines are typically high-level demands for model developers and testers.
There remains a gap in translating these broad requirements into actionable testing questions to verify LLM compliance.
To address this challenge, we introduce GUARD (\textbf{G}uideline \textbf{U}pholding Test through \textbf{A}daptive \textbf{R}ole-play and Jailbreak \textbf{D}iagnostics), a testing method designed to operationalize guidelines into specific guideline-violating questions that assess LLM adherence. To implement this, GUARD assigns LLMs to play different roles, enabling the collaborative and automated generation of guideline-violating questions based on government-issued guidelines, thereby testing whether responses comply with these guidelines. When responses directly violate guidelines, GUARD reports inconsistencies. Furthermore, for responses that do not directly violate guidelines, GUARD integrates the concept of ``jailbreaks'' to diagnostics, named GUARD-JD, which creates scenarios that provoke unethical or guideline-violating responses, effectively identifying potential scenarios that could bypass built-in safety mechanisms. Our method finally culminates in a comprehensive compliance report, delineating the extent of adherence and highlighting any violations.
We have empirically validated the effectiveness of GUARD on seven LLMs, including Vicuna-13B, LongChat-7B, Llama2-7B, Llama-3-8B, GPT-3.5, GPT-4, and Claude-3.5, by testing compliance under three government-issued guidelines and conducting jailbreak diagnostics. Additionally, GUARD-JD can transfer jailbreak diagnostics to vision-language models (MiniGPT-v2 and Gemini Vision Pro), demonstrating its versatility and providing valuable insights for the development of safer, more reliable LLM-based applications across diverse modalities.
Primary Area: alignment, fairness, safety, privacy, and societal considerations
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Submission Number: 1190
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