LLM Safety Benchmarks

LLM: Large Language Model

This field measures how reliably language models recognize and handle risks such as harmful requests, jailbreaks, bias, privacy violations, and unsafe tool use. Benchmarks use curated and expert-annotated scenarios, adversarial red-teaming, multi-turn and agent simulations, and increasingly multilingual, multimodal, and domain-specific tests. Researchers also compare refusal and moderation methods, use repeated sampling and statistical techniques to estimate reliability, and assess both harmful compliance and over-refusal. A central challenge is making results comparable and trustworthy while ensuring tests reflect changing, context-dependent behavior in real deployments.

Latest papers

284 papers44 papers in the last four weeks, up 7% on the four weeks before. 0.3% of all new papers.

Measuring Mid-2025 LLM-Assistance on Novice Performance in Biology
Feb 18, 2026Shen Zhou Hong, Alex Kleinman, Alyssa Mathiowetz +9AI-Assisted Scientific Research Human-in-the-Loop AI
Active Site, Cambridge, MA 02142, United States · Independent · Model Evaluation and Threat Research, Inc., Covina, CA 91723, United States
Measuring and Eliminating Refusals in Military Large Language Models
Feb 18, 2026Jack FitzGerald, Dylan Bates, Aristotelis Lazaridis +17Refusal Behavior in Language Models LLM Refusal Behavior
EdgeRunner AI.
Intent Laundering: AI Safety Datasets Are Not What They Seem
Feb 17, 2026Shahriar Golchin, Marc WetterLLM Safety Evaluation LLM Safety Benchmarks
Applied Machine Learning Research
SalamahBench: Dialect and Category Level Safety Evaluation of Arabic Language Models
Feb 3, 2026Omar Abdelnasser, Fatemah Alharbi, Khaled Khasawneh +2LLM Safety Benchmarks Multilingual Language Model Evaluation
Compumacy for Artificial Intelligence Solutions, Cairo, Egypt · Computer Science Department, College of Computer Science and Engineering, Taibah University, Yanbu 46522, KSA · Electrical and Computer Engineering Department, George Mason University, VA, USA +2
Health-ORSC-Bench: A Benchmark for Measuring Over-Refusal and Safety Completion in Health Context
Jan 25, 2026Zhihao Zhang, Liting Huang, Guanghao Wu +3LLM Safety Benchmarks Language Model Safety Evaluation
Macquarie University1 · UTS2 · MBZUAI3 +1
RubRIX: Rubric-Driven Risk Mitigation in Caregiver-AI Interactions
Jan 19, 2026Drishti Goel, Jeongah Lee, Qiuyue Joy Zhong +5Language Model Safety Evaluation LLM Safety Evaluation
University of Illinois Urbana-Champaign · University of Massachusetts Amherst · OSF HealthCare +1
Safety Is Not Universal: The Selective Safety Trap in LLM Alignment
Jan 7, 2026Iago Alves Brito, Walcy Santos Rezende Rios, Julia Soares Dollis +2LLM Safety Benchmarks LLM Alignment
Advanced Knowledge Center for Immersive Technologies (AKCIT) Federal University of Goiás (UFG)
The Instability of Safety: How Random Seeds and Temperature Expose Inconsistent LLM Refusal Behavior
Dec 12, 2025Erik LarsenLanguage Model Safety Evaluation LLM Refusal Behavior
MENTOR: A Metacognition-Driven Self-Evolution Framework for Uncovering and Mitigating Implicit Domain Risks in LLMs
Nov 10, 2025Liang Shan, Kaicheng Shen, Wen Wu +8LLM Safety LLM Safety Alignment
School of Computer Science and Technology, East China Normal University · Shanghai AI Lab, Shanghai Innovation Institute
SafetyFlow: An Agent-Flow System for Automated LLM Safety Benchmarking
Aug 21, 2025Xiangyang Zhu, Yuan Tian, Chunyi Li +3LLM Safety Benchmarks Synthetic Benchmark Generation
Shanghai AI Lab · East China Normal University
Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming
Jul 30, 2025Jiazhen Pan, Bailiang Jian, Paul Hager +20LLM Safety Benchmarks Language Model Safety Evaluation
Cherise · School of Computation, Information and Technology, Technical University of Munich (TUM), Munich, Germany · Department of Engineering Science, University of Oxford, Oxford, United Kingdom +9
LLMail-Inject: A Dataset from a Realistic Adaptive Prompt Injection Challenge
Jun 11, 2025Sahar Abdelnabi, Aideen Fay, Ahmed Salem +22LLM Security Indirect Prompt Injection
Microsoft · ISTA · Trend Micro +5
ReasoningShield: Safety Detection over Reasoning Traces of Large Reasoning Models
May 22, 2025Changyi Li, Jiayi Wang, Xudong Pan +2Language Model Safety Evaluation LLM Safety Benchmarks
Fudan University · Shanghai Innovation Institute
CASE-Bench: Context-Aware SafEty Benchmark for Large Language Models
Jan 24, 2025Guangzhi Sun, Xiao Zhan, Shutong Feng +2LLM Safety Benchmarks Language Model Safety Evaluation
Trinity College, University of Cambridge, Cambridge, United Kingdom · Department of Informatics, King’s College London, United Kingdom · Institut für Informatik, Heinrich-Heine-Universität Düsseldorf, Germany +1

Feb 18, 2026J/K move · Enter open · S save

Measuring Mid-2025 LLM-Assistance on Novice Performance in Biology

Shen Zhou Hong, Alex Kleinman, Alyssa Mathiowetz, Adam Howes+8

Active Site, Cambridge, MA 02142, United States · Independent · Model Evaluation and Threat Research, Inc., Covina, CA 91723, United States

AI-Assisted Scientific Research Human-in-the-Loop AI LLM Safety Benchmarks

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Large language models (LLMs) perform strongly on biological benchmarks, raising concerns that they may help novice actors acquire dual-use laboratory skills. Yet, whether this translates to improved human performance in the physical laboratory remains unclear. To address this, we conducted a pre-registered, investigator-blinded, randomized controlled trial (June-August 2025; n = 153) evaluating whether LLMs improve novice performance in tasks that collectively model a viral reverse genetics workflow. We observed no significant difference in the primary endpoint of workflow completion (5.2% LLM vs. 6.6% Internet; P = 0.759), nor in the success rate of individual tasks. However, the LLM arm had numerically higher success rates in four of the five tasks, most notably for the cell culture task (68.8% LLM vs. 55.3% Internet; P = 0.059). Post-hoc Bayesian modeling of pooled data estimates an approximate 1.4-fold increase (95% CrI 0.74-2.62) in success for a "typical" reverse genetics task under LLM assistance. Ordinal regression modelling suggests that participants in the LLM arm were more likely to progress through intermediate steps across all tasks (posterior probability of a positive effect: 81%-96%). Overall, mid-2025 LLMs did not substantially increase novice completion of complex laboratory procedures but were associated with a modest performance benefit. These results reveal a gap between in silico benchmarks and real-world utility, underscoring the need for physical-world validation of AI biosecurity assessments as model capabilities and user proficiency evolve.