WMDP
Weapons of Mass Destruction (WMDP) is a multiple-choice benchmark on dual-use biology, chemistry, and cyber knowledge. It measures a model's capacity to enable malicious actors to design, synthesize, acquire, or use chemical, biological, radiological, or nuclear (CBRN) weapons.
Grok-4.1 Thinking from xAI currently leads the WMDP leaderboard with a score of 0.840 across 1 evaluated AI models.
What WMDP measures
WMDP is a text benchmark that evaluates large language models on safety, healthcare, biology, and chemistry tasks. LLM Stats tracks 1 model on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.8, with the leader reaching 0.8.
Compare leaders on the best AI for safety, best AI for healthcare, best AI for biology and best AI for chemistry leaderboards.
Publication
- Paper
- The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning
- Authors
- Nathaniel Li, Alexander Pan, Anjali Gopal, Summer Yue, and 53 others
- Published
- arXiv
- 2403.03218
Abstract
The White House Executive Order on Artificial Intelligence highlights the risks of large language models (LLMs) empowering malicious actors in developing biological, cyber, and chemical weapons. To measure these risks of malicious use, government institutions and major AI labs are developing evaluations for hazardous capabilities in LLMs. However, current evaluations are private, preventing further research into mitigating risk. Furthermore, they focus on only a few, highly specific pathways for malicious use. To fill these gaps, we publicly release the Weapons of Mass Destruction Proxy (WMDP) benchmark, a dataset of 3,668 multiple-choice questions that serve as a proxy measurement of hazardous knowledge in biosecurity, cybersecurity, and chemical security. WMDP was developed by a consortium of academics and technical consultants, and was stringently filtered to eliminate sensitive information prior to public release. WMDP serves two roles: first, as an evaluation for hazardous knowledge in LLMs, and second, as a benchmark for unlearning methods to remove such hazardous knowledge. To guide progress on unlearning, we develop RMU, a state-of-the-art unlearning method based on controlling model representations. RMU reduces model performance on WMDP while maintaining general capabilities in areas such as biology and computer science, suggesting that unlearning may be a concrete path towards reducing malicious use from LLMs. We release our benchmark and code publicly at https://wmdp.ai
Grok-4.1 Thinking leads with 84.0%.
Progress Over Time
Interactive timeline showing model performance evolution on WMDP
WMDP Leaderboard
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FAQ
Common questions about WMDP.
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