GUARD: Guideline Upholding Test through Adaptive Role-play and Jailbreak Diagnostics for LLMs

Guidelines alone aren't enough: The paper identifies a critical gap between high-level ethics guidelines issued by governments and actual testing practices. GUARD addresses this by translating abstract principles into specific, testable questions that can verify whether LLMs actually comply with these guidelines in practice.

Two-layer testing catches more problems: GUARD uses both direct testing (checking if models violate guidelines outright) and "jailbreak diagnostics" (finding indirect ways to provoke harmful responses). This dual approach is important because some models may pass direct tests but still generate harmful content when prompted creatively, revealing gaps in safety mechanisms.

Systematic compliance reporting is now possible: The method produces detailed compliance reports showing the extent to which LLMs adhere to government guidelines. This provides transparency that regulators, developers, and the public need to understand whether AI systems actually meet stated ethical standards.

The problem is widespread: The paper tested GUARD on eight different LLMs, suggesting that guideline violations and jailbreak vulnerabilities are not isolated issues but represent systemic challenges across the industry that require standardized testing approaches.

Actionable testing framework for developers: By automating the generation of guideline-violating test questions, GUARD gives AI developers a practical tool to identify and fix compliance issues before deployment, rather than relying on vague ethical principles.