jang1563/frontier-safety-benchmark

The 'frontier-safety-benchmark' is a very early-stage project (7 days old, 0 stars) that targets a highly critical but increasingly crowded niche: AI safety evaluations for dual-use scientific knowledge. While the focus on 'contamination-awareness' (detecting if the model has already seen the test questions in its training set) is a valid and sophisticated research concern, the project currently lacks the institutional backing or community traction required to become a standard. It faces extreme 'frontier risk' as labs like OpenAI (via their Preparedness Framework) and Anthropic (via Responsible Scaling Policies) are building internal, high-fidelity versions of these exact benchmarks. Furthermore, institutional bodies like the UK AI Safety Institute (with their 'Inspect' framework) and MLCommons are rapidly consolidating the market for AI safety evaluations. Without a massive influx of unique, proprietary safety data or a formal partnership with a safety institute, this project is likely to be a transient research artifact rather than a persistent piece of infrastructure. Its defensibility is near-zero due to the lack of stars, forks, or a visible ecosystem.