SafetyBench: Evaluating the Safety of Large Language Models
Structured LLM safety evaluation frameworks inform how agencies might assess AI tools before deployment - though this is an academic benchmark, not an APS-ready tool.
Key points
- SafetyBench is a bilingual benchmark assessing LLM safety across 7 risk categories using 11,435 multiple-choice questions.
- The MIT AI Risk Repository spotlights this as one of 28 frameworks cataloguing AI risks - useful for comparative evaluation work.
- A 2023 academic paper; this blog post adds no new findings beyond summarising the original arXiv publication.
Implications for Australian agencies
- Monitor Agencies developing AI procurement or evaluation criteria may want to monitor the MIT AI Risk Repository's framework catalogue as a reference collection for structured risk taxonomies.
Implications are AI-generated. Starting points, not advice — see methodology for how they're framed.
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Weekly digest, 9 February 2026
"SafetyBench: Evaluating the Safety of Large Language Models"
Source: MIT AI Risk Repository – Blog
Published: 13 February 2026
URL: https://airisk.mit.edu/blog/safetybench-evaluating-the-safety-of-large-language-models
The MIT AI Risk Repository's blog highlights SafetyBench, a 2023 bilingual (English/Chinese) benchmark for evaluating LLM safety across seven categories: offensiveness, bias, physical health, mental health, illegal activities, ethics and morality, and privacy. It uses 11,435 multiple-choice questions to assess over 25 models in zero-shot and few-shot settings. The blog entry is a brief summary of the underlying arXiv paper rather than new analysis, and is one of a series spotlighting frameworks catalogued in the Repository.
Implications for Australian agencies:
- [Monitor] Agencies developing AI procurement or evaluation criteria may want to monitor the MIT AI Risk Repository's framework catalogue as a reference collection for structured risk taxonomies.
Retrieved from SIMS, 18 July 2026.