Frontier-Assisted Single-Prompt Disposable Risk Assessment

Frontier-Assisted Single-Prompt Disposable Risk Assessment is a proposed lightweight method for comparing AI models’ general capabilities with the extent to which their responses practically enable harmful activities. A frontier model generates a fresh 20-case benchmark and retains a private rubric, after which identical prompts are given to target models and their unedited responses are returned to the original model for scoring before the benchmark is retired. The framework separates willingness to answer, underlying capability, and real-world enablement, emphasizing that harmful-sounding but inaccurate or unusable output should score lower than actionable, reliable assistance. It uses four 100-point assessments covering general-purpose ability, criminal enablement, sexual-exploitation enablement, and terrorism or violent-extremism enablement, while including benign controls to measure whether models remain helpful for legitimate adjacent requests. The proposal argues that disposable benchmarks may reduce contamination and remain adaptable as models improve, but acknowledges limitations including inconsistent test difficulty, author-judge coupling, changing hosted models, finite coverage, and the inability of text-based scores to predict actual real-world harm.

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