GUARD: Adaptive Role-play and Jailbreak Diagnostics for LLM Compliance Testing
Researchers have introduced GUARD, a novel testing framework designed to bridge the gap between high-level government ethics guidelines and actionable compliance verification for Large Language Models (LLMs). As LLMs become integral to various sectors, concerns about harmful outputs have grown. GUARD operationalizes abstract ethical demands by automatically generating specific questions that potentially violate these guidelines. It employs adaptive role-play to assess direct adherence and integrates a module called GUARD-JD, which uses jailbreak diagnostics to identify scenarios where safety mechanisms might be bypassed. The method produces comprehensive compliance reports highlighting violations. Empirical validation was conducted on eight prominent LLMs, including GPT-4, Llama-3, and Claude-3.7, demonstrating its effectiveness in detecting non-compliance. Furthermore, the study shows that GUARD-JD can extend its diagnostic capabilities to vision-language models like Gemini-1.5. This development aims to promote the creation of more reliable and trustworthy AI applications by providing developers with concrete tools to test and ensure alignment with regulatory standards.
Wire timeline
GUARD: Adaptive Role-play and Jailbreak Diagnostics for LLM Compliance Testing
Researchers have introduced GUARD, a novel testing framework designed to bridge the gap between high-level government ethics guidelines and actionable compliance verification for Large Language Models (LLMs). As LLMs become integral to various sectors, concerns about harmful outputs have grown. GUARD operationalizes abstract ethical demands by automatically generating specific questions that potentially violate these guidelines. It employs adaptive role-play to assess direct adherence and integrates a module called GUARD-JD, which uses jailbreak diagnostics to identify scenarios where safety mechanisms might be bypassed. The method produces comprehensive compliance reports highlighting violations. Empirical validation was conducted on eight prominent LLMs, including GPT-4, Llama-3, and Claude-3.7, demonstrating its effectiveness in detecting non-compliance. Furthermore, the study shows that GUARD-JD can extend its diagnostic capabilities to vision-language models like Gemini-1.5. This development aims to promote the creation of more reliable and trustworthy AI applications by providing developers with concrete tools to test and ensure alignment with regulatory standards.
cs.AI updates on arXiv.org