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AI safety evaluation

The gap between open-weight AI models, whose underlying parameters are published for anyone to download and run, and closed frontier systems kept behind an API has narrowed sharply over the past year. Several open releases now perform competitively with commercial frontier models on standard benchmarks, a development widely celebrated for democratising access to powerful AI tools. Less discussed is a parallel and more troubling trend: the safety evaluation and red-teaming infrastructure built to assess these models before release has not kept pace with how quickly their capabilities are advancing.

• Independent research groups have shown that safety fine-tuning in several open models can be substantially undone with a modest amount of additional training, at relatively low cost.
• Benchmark suites used to justify release decisions rarely include dedicated tests for dual-use capability, such as assistance with chemical or biological weapon design.
• Some developers now publish model cards disclosing known limitations, but disclosure practices vary widely and are not independently audited in most cases.
• Third-party red-teaming before release remains inconsistent, with some labs commissioning extensive external testing and others relying primarily on internal review.