Prioritize information access risk for LLM safety evaluations
Focus LLM safety evaluations on increased access to existing dangerous information first.
We prioritized increased access to information on known threats.
Information access is the most immediate risk because current AI systems excel at synthesizing existing language information. Evaluating increased novelty is a secondary concern.
When evaluating LLM safety for bioweapons, first test how well it helps users find and synthesize information on known dangerous pathogens, rather than focusing on generating entirely novel threats.
When assessing AI risks, prioritize evaluating how LLMs increase access to existing dangerous information. This is the most immediate concern, given current AI strengths in synthesizing language data.
Do not solely focus on hypothetical novel threats if the immediate risk of information synthesis is higher.
- Building an early warning system for LLM-aided biological threat creation ↗OpenAI · OpenAI News · Jan 31, 2024 · paraphrased
First seen on Subvolts Jan 31, 2024. Updated Sep 11, 2026. Spotted a problem? Tell us.
- Building an early warning system for LLM-aided biological threat creationOpenAI · OpenAI News · Jan 31, 2024
The Playbook: reusable how-tos, prompts, configs and tips pulled out of the news.
- WorkflowAgentHands workflow for generating embodied XR agent gesturesSteps to generate synchronized, spatially grounded hand gestures for XR agents based on LLM responses. · 15 min
- HeuristicAuxiliary environment signals improve LLM authorization accuracyProviding LLMs with auxiliary environment signals can substantially improve their accuracy in authorization decisions. · 30 min
- ToolMoMHa for multi-objective LLM harness optimizationOptimizes LLM harnesses for accuracy, safety, and token cost using a joint-reward proposer. · 120 min
- HeuristicDecompose LLM vulnerability discovery into sub-tasksBreak down LLM vulnerability discovery into distinct, measurable steps for better evaluation. · 5 min
- HeuristicHarness design is a multi-objective problemConsider accuracy, safety, and token cost together when designing LLM harnesses. · 30 min
- Anti-patternRelying on gameable benchmarks for LLM vulnerability discoveryAvoid LLM benchmarks that are susceptible to data contamination or score recall against unknowable sets. · 5 min
Questions people ask
- Which AI tools does this work with?
- Any current model. It was seen with in the sources.
- Where did this come from?
- OpenAI (OpenAI News). Subvolts collected, normalized and credited it; the link above opens the original.