MMLU — Measuring Massive Multitask Language Understanding
The official code and data for MMLU (ICLR 2021), a benchmark of roughly 16,000 multiple-choice questions spanning 57 subjects — from elementary math and US history to law, medicine, and computer science.
It measures a model's broad world knowledge and problem-solving ability in zero- and few-shot settings and remains one of the most widely reported LLM benchmarks.
MIT licensed and lightweight, making it easy to run and integrate into evaluation pipelines.
✓ Security: Safe · 100100/100 · grade Ascanned 15h ago
✓ no compromise signals3 risk-surface · 5/20 OWASP controls flagged
Compromise signals — malicious or tampered code (leaked secrets, backdoors, a dropped executable) — reduce the score, and known dependency CVEs carry a bounded penalty (they warrant review but never QUARANTINE — update the dependency to clear). Other dangerous-by-capability traits are risk surface, expected for some capabilities. Every finding is mapped to its OWASP control below.
Data card · low confidence (static)
license: detected
8 files
Findings mapped to the OWASP Top 10 for LLM Applications (2025) and the OWASP Machine Learning Security Top 10. Expand any flagged control for the exact findings — compromise reduces the score; expected/risk-surface do not.
OWASP Top 10 for LLM Applications
⚠LLM05Improper Output Handlingmedium
Code that pipes model/user output into shell, eval, SQL or paths unsafely.