Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error Linear Units. - Dan Hendrycks; Kevin Gimpel (CoRR, 2016)

Generalizing and Improving Weight Initialization. - Dan Hendrycks; Kevin Gimpel (CoRR, 2016)

Visible Progress on Adversarial Images and a New Saliency Map. - Dan Hendrycks; Kevin Gimpel (CoRR, 2016)

A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks. - Dan Hendrycks; Kevin Gimpel (ICLR (Poster), 2017)

Early Methods for Detecting Adversarial Images. - Dan Hendrycks; Kevin Gimpel (ICLR (Workshop), 2017)

Open Category Detection with PAC Guarantees. - Si Liu; Risheek Garrepalli; Thomas G. Dietterich; Alan Fern; Dan Hendrycks (ICML, 2018)

Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise. - Dan Hendrycks; Mantas Mazeika; Duncan Wilson; Kevin Gimpel (NeurIPS, 2018)

Benchmarking Neural Network Robustness to Common Corruptions and Perturbations. - Dan Hendrycks; Thomas G. Dietterich (ICLR (Poster), 2019)

Deep Anomaly Detection with Outlier Exposure. - Dan Hendrycks; Mantas Mazeika; Thomas G. Dietterich (ICLR (Poster), 2019)

Using Pre-Training Can Improve Model Robustness and Uncertainty. - Dan Hendrycks; Kimin Lee; Mantas Mazeika (ICML, 2019)

Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty. - Dan Hendrycks; Mantas Mazeika; Saurav Kadavath; Dawn Song (NeurIPS, 2019)

Transfer of Adversarial Robustness Between Perturbation Types. - Daniel Kang; Yi Sun 010; Tom Brown; Dan Hendrycks; Jacob Steinhardt (CoRR, 2019)

Testing Robustness Against Unforeseen Adversaries. - Daniel Kang; Yi Sun 010; Dan Hendrycks; Tom Brown; Jacob Steinhardt (CoRR, 2019)

A Benchmark for Anomaly Segmentation. - Dan Hendrycks; Steven Basart; Mantas Mazeika; Mohammadreza Mostajabi; Jacob Steinhardt; Dawn Song (CoRR, 2019)

Pretrained Transformers Improve Out-of-Distribution Robustness. - Dan Hendrycks; Xiaoyuan Liu; Eric Wallace; Adam Dziedzic; Rishabh Krishnan; Dawn Song (ACL, 2020)

AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty. - Dan Hendrycks; Norman Mu; Ekin Dogus Cubuk; Barret Zoph; Justin Gilmer; Balaji Lakshminarayanan (ICLR, 2020)

Natural Adversarial Examples. - Dan Hendrycks; Kevin Zhao; Steven Basart; Jacob Steinhardt; Dawn Song (CVPR, 2021)

The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization. - Dan Hendrycks; Steven Basart; Norman Mu; Saurav Kadavath; Frank Wang; Evan Dorundo; Rahul Desai; Tyler Zhu; Samyak Parajuli; Mike Guo; Dawn Song; Jacob Steinhardt; et al. (ICCV, 2021)

Aligning AI With Shared Human Values. - Dan Hendrycks; Collin Burns; Steven Basart; Andrew Critch; Jerry Li 001; Dawn Song; Jacob Steinhardt (ICLR, 2021)

Measuring Massive Multitask Language Understanding. - Dan Hendrycks; Collin Burns; Steven Basart; Andy Zou; Mantas Mazeika; Dawn Song; Jacob Steinhardt (ICLR, 2021)

VisDA-2021 Competition: Universal Domain Adaptation to Improve Performance on Out-of-Distribution Data. - Dina Bashkirova; Dan Hendrycks; Donghyun Kim 006; Haojin Liao; Samarth Mishra; Chandramouli Rajagopalan; Kate Saenko; Kuniaki Saito; Burhan Ul Tayyab; Piotr Teterwak; Ben Usman (NeurIPS (Competition and Demos), 2021)

CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review. - Dan Hendrycks; Collin Burns; Anya Chen; Spencer Ball (NeurIPS Datasets and Benchmarks, 2021)

Measuring Mathematical Problem Solving With the MATH Dataset. - Dan Hendrycks; Collin Burns; Saurav Kadavath; Akul Arora; Steven Basart; Eric Tang; Dawn Song; Jacob Steinhardt (NeurIPS Datasets and Benchmarks, 2021)

Measuring Coding Challenge Competence With APPS. - Dan Hendrycks; Steven Basart; Saurav Kadavath; Mantas Mazeika; Akul Arora; Ethan Guo; Collin Burns; Samir Puranik; Horace He; Dawn Song; Jacob Steinhardt (NeurIPS Datasets and Benchmarks, 2021)

What Would Jiminy Cricket Do? Towards Agents That Behave Morally. - Dan Hendrycks; Mantas Mazeika; Andy Zou; Sahil Patel; Christine Zhu; Jesus Navarro; Dawn Song; Bo Li 026; Jacob Steinhardt (NeurIPS Datasets and Benchmarks, 2021)

The Trojan Detection Challenge. - Mantas Mazeika; Dan Hendrycks; Huichen Li; Xiaojun Xu; Sidney Q. Hough; Andy Zou; Arezoo Rajabi; Qi Yao; Zihao Wang; Jian Tian; Yao Tang; Di Tang 001; et al. (NeurIPS (Competition and Demos), 2021)

VisDA-2021 Competition Universal Domain Adaptation to Improve Performance on Out-of-Distribution Data. - Dina Bashkirova; Dan Hendrycks; Donghyun Kim 006; Samarth Mishra; Kate Saenko; Kuniaki Saito; Piotr Teterwak; Ben Usman (CoRR, 2021)

Unsolved Problems in ML Safety. - Dan Hendrycks; Nicholas Carlini; John Schulman; Jacob Steinhardt (CoRR, 2021)

Certified Adversarial Defenses Meet Out-of-Distribution Corruptions: Benchmarking Robustness and Simple Baselines. - Jiachen Sun; Akshay Mehra; Bhavya Kailkhura; Pin-Yu Chen; Dan Hendrycks; Jihun Hamm; Z. Morley Mao (CoRR, 2021)

PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures. - Dan Hendrycks; Andy Zou; Mantas Mazeika; Leonard Tang; Bo Li 026; Dawn Song; Jacob Steinhardt (CVPR, 2022)

A Spectral View of Randomized Smoothing Under Common Corruptions: Benchmarking and Improving Certified Robustness. - Jiachen Sun; Akshay Mehra; Bhavya Kailkhura; Pin-Yu Chen; Dan Hendrycks; Jihun Hamm; Z. Morley Mao (ECCV (4), 2022)

Scaling Out-of-Distribution Detection for Real-World Settings. - Dan Hendrycks; Steven Basart; Mantas Mazeika; Andy Zou; Joseph Kwon; Mohammadreza Mostajabi; Jacob Steinhardt; Dawn Song (ICML, 2022)

How Would The Viewer Feel? Estimating Wellbeing From Video Scenarios. - Mantas Mazeika; Eric Tang; Andy Zou; Steven Basart; Jun Shern Chan; Dawn Song; David A. Forsyth; Jacob Steinhardt; Dan Hendrycks (NeurIPS, 2022)

OpenOOD: Benchmarking Generalized Out-of-Distribution Detection. - Jingkang Yang; Pengyun Wang; Dejian Zou; Zitang Zhou; Kunyuan Ding; Wenxuan Peng; Haoqi Wang; Guangyao Chen; Bo Li 080; Yiyou Sun; Xuefeng Du; Kaiyang Zhou; et al. (NeurIPS, 2022)

Forecasting Future World Events With Neural Networks. - Andy Zou; Tristan Xiao; Ryan Jia; Joe Kwon; Mantas Mazeika; Richard Li; Dawn Song; Jacob Steinhardt; Owain Evans; Dan Hendrycks (NeurIPS, 2022)

X-Risk Analysis for AI Research. - Dan Hendrycks; Mantas Mazeika (CoRR, 2022)

Actionable Guidance for High-Consequence AI Risk Management: Towards Standards Addressing AI Catastrophic Risks. - Anthony M. Barrett; Dan Hendrycks; Jessica Newman; Brandie Nonnecke (CoRR, 2022)

PAC Guarantees and Effective Algorithms for Detecting Novel Categories. - Si Liu; Risheek Garrepalli; Dan Hendrycks; Alan Fern; Debashis Mondal; Thomas G. Dietterich (J. Mach. Learn. Res., 2022)

A Unified Survey on Anomaly, Novelty, Open-Set, and Out of-Distribution Detection: Solutions and Future Challenges. - Mohammadreza Salehi; Hossein Mirzaei; Dan Hendrycks; Yixuan Li 001; Mohammad Hossein Rohban; Mohammad Sabokrou (Trans. Mach. Learn. Res., 2022)

MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding. - Steven H. Wang; Antoine Scardigli; Leonard Tang; Wei Chen; Dimitry Levkin; Anya Chen; Spencer Ball; Thomas Woodside; Oliver Zhang; Dan Hendrycks (EMNLP, 2023)

Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the Machiavelli Benchmark. - Alexander Pan; Jun Shern Chan; Andy Zou; Nathaniel Li; Steven Basart; Thomas Woodside; Hanlin Zhang 002; Scott Emmons; Dan Hendrycks (ICML, 2023)

DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models. - Boxin Wang; Weixin Chen; Hengzhi Pei; Chulin Xie; Mintong Kang; Chenhui Zhang; Chejian Xu; Zidi Xiong; Ritik Dutta; Rylan Schaeffer; Sang T. Truong; Simran Arora; et al. (NeurIPS, 2023)

Natural Selection Favors AIs over Humans. - Dan Hendrycks (CoRR, 2023)

Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark. - Alexander Pan; Jun Shern Chan; Andy Zou; Nathaniel Li; Steven Basart; Thomas Woodside; Jonathan Ng; Hanlin Zhang 002; Scott Emmons; Dan Hendrycks (CoRR, 2023)

An Overview of Catastrophic AI Risks. - Dan Hendrycks; Mantas Mazeika; Thomas Woodside (CoRR, 2023)

Identifying and Mitigating the Security Risks of Generative AI. - Clark W. Barrett; Brad Boyd; Ellie Burzstein; Nicholas Carlini; Brad Chen; Jihye Choi; Amrita Roy Chowdhury 001; Mihai Christodorescu; Anupam Datta; Soheil Feizi; Kathleen Fisher; Tatsunori Hashimoto; et al. (CoRR, 2023)

Representation Engineering: A Top-Down Approach to AI Transparency. - Andy Zou; Long Phan; Sarah Li Chen; James Campbell; Phillip Guo; Richard Ren; Alexander Pan; Xuwang Yin; Mantas Mazeika; Ann-Kathrin Dombrowski; Shashwat Goel; Nathaniel Li; et al. (CoRR, 2023)

Can LLMs Follow Simple Rules? - Norman Mu; Sarah Li Chen; Zifan Wang 001; Sizhe Chen; David Karamardian; Lulwa Aljeraisy; Dan Hendrycks; David A. Wagner 001 (CoRR, 2023)

Identifying and Mitigating the Security Risks of Generative AI. - Clark W. Barrett; Brad Boyd; Elie Bursztein; Nicholas Carlini; Brad Chen; Jihye Choi; Amrita Roy Chowdhury 001; Mihai Christodorescu; Anupam Datta; Soheil Feizi; Kathleen Fisher; Tatsunori Hashimoto; et al. (Found. Trends Priv. Secur., 2023)

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. - Aarohi Srivastava; Abhinav Rastogi; Abhishek Rao; Abu Awal Md Shoeb; Abubakar Abid; Adam Fisch; Adam R. Brown; Adam Santoro; Aditya Gupta; Adrià Garriga-Alonso; Agnieszka Kluska; Aitor Lewkowycz; et al. (Trans. Mach. Learn. Res., 2023)

Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression. - Junyuan Hong; Jinhao Duan; Chenhui Zhang; Zhangheng Li; Chulin Xie; Kelsey Lieberman; James Diffenderfer; Brian R. Bartoldson; Ajay Kumar Jaiswal; Kaidi Xu; Bhavya Kailkhura; Dan Hendrycks; et al. (ICML, 2024)

The WMDP Benchmark: Measuring and Reducing Malicious Use with Unlearning. - Nathaniel Li; Alexander Pan; Anjali Gopal; Summer Yue; Daniel Berrios; Alice Gatti; Justin D. Li; Ann-Kathrin Dombrowski; Shashwat Goel; Gabriel Mukobi; Nathan Helm-Burger; Rassin Lababidi; et al. (ICML, 2024)

HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal. - Mantas Mazeika; Long Phan; Xuwang Yin; Andy Zou; Zifan Wang 001; Norman Mu; Elham Sakhaee; Nathaniel Li; Steven Basart; Bo Li 026; David A. Forsyth; Dan Hendrycks (ICML, 2024)

Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress? - Richard Ren; Steven Basart; Adam Khoja; Alice Gatti; Long Phan; Xuwang Yin; Mantas Mazeika; Alexander Pan; Gabriel Mukobi; Ryan H. Kim; Stephen Fitz; Dan Hendrycks (NeurIPS, 2024)

Improving Alignment and Robustness with Circuit Breakers. - Andy Zou; Long Phan; Justin Wang; Derek Duenas; Maxwell Lin; Maksym Andriushchenko; J. Zico Kolter; Matt Fredrikson; Dan Hendrycks (NeurIPS, 2024)

Uncovering Latent Human Wellbeing in Language Model Embeddings. - Pedro Freire; ChengCheng Tan; Adam Gleave; Dan Hendrycks; Scott Emmons (CoRR, 2024)

The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning. - Nathaniel Li; Alexander Pan; Anjali Gopal; Summer Yue; Daniel Berrios; Alice Gatti; Justin D. Li; Ann-Kathrin Dombrowski; Shashwat Goel; Long Phan; Gabriel Mukobi; Nathan Helm-Burger; et al. (CoRR, 2024)

Improving Alignment and Robustness with Circuit Breakers. - Andy Zou; Long Phan; Justin Wang; Derek Duenas; Maxwell Lin; Maksym Andriushchenko; Rowan Wang; Zico Kolter; Matt Fredrikson; Dan Hendrycks (CoRR, 2024)

AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents. - Maksym Andriushchenko; Alexandra Souly; Mateusz Dziemian; Derek Duenas; Maxwell Lin; Justin Wang; Dan Hendrycks; Andy Zou; Zico Kolter; Matt Fredrikson; Eric Winsor; Jerome Wynne; et al. (CoRR, 2024)

Introduction to AI Safety, Ethics, and Society. - Dan Hendrycks (CoRR, 2024)

AI deception: A survey of examples, risks, and potential solutions. - Peter S. Park; Simon Goldstein; Aidan O'Gara; Michael Chen; Dan Hendrycks (Patterns, 2024)

LLM-PBE: Assessing Data Privacy in Large Language Models. - Qinbin Li; Junyuan Hong; Chulin Xie; Jeffrey Tan; Rachel Xin; Junyi Hou; Xavier Yin; Zhun Wang; Dan Hendrycks; Zhangyang Wang; Bo Li 026; Bingsheng He; et al. (Proc. VLDB Endow., 2024)

AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents. - Maksym Andriushchenko; Alexandra Souly; Mateusz Dziemian; Derek Duenas; Maxwell Lin; Justin Wang; Dan Hendrycks; Andy Zou; J. Zico Kolter; Matt Fredrikson; Yarin Gal; Xander Davies (ICLR, 2025)

Tamper-Resistant Safeguards for Open-Weight LLMs. - Rishub Tamirisa; Bhrugu Bharathi; Long Phan; Andy Zhou; Alice Gatti; Tarun Suresh; Maxwell Lin; Justin Wang; Rowan Wang; Ron Arel; Andy Zou; Dawn Song; et al. (ICLR, 2025)

Utility Engineering: Analyzing and Controlling Emergent Value Systems in AIs. - Mantas Mazeika; Xuwang Yin; Rishub Tamirisa; Jaehyuk Lim; Bruce W. Lee; Richard Ren; Long Phan; Norman Mu; Adam Khoja; Oliver Zhang; Dan Hendrycks (CoRR, 2025)

EnigmaEval: A Benchmark of Long Multimodal Reasoning Challenges. - Clinton J. Wang; Dean Lee; Cristina Menghini; Johannes Mols; Jack Doughty; Adam Khoja; Jayson Lynch; Sean Hendryx; Summer Yue; Dan Hendrycks (CoRR, 2025)

Beyond Release: Access Considerations for Generative AI Systems. - Irene Solaiman; Rishi Bommasani; Dan Hendrycks; Ariel Herbert-Voss; Yacine Jernite; Aviya Skowron; Andrew Trask (CoRR, 2025)

The MASK Benchmark: Disentangling Honesty From Accuracy in AI Systems. - Richard Ren; Arunim Agarwal; Mantas Mazeika; Cristina Menghini; Robert Vacareanu; Brad Kenstler; Mick Yang; Isabelle Barrass; Alice Gatti; Xuwang Yin; Eduardo Trevino; Matias Geralnik; et al. (CoRR, 2025)

Superintelligence Strategy: Expert Version. - Dan Hendrycks; Eric Schmidt; Alexandr Wang (CoRR, 2025)

MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models. - Chejian Xu; Jiawei Zhang 002; Zhaorun Chen; Chulin Xie; Mintong Kang; Yujin Potter; Zhun Wang; Zhuowen Yuan; Alexander Xiong; Zidi Xiong; Chenhui Zhang; Lingzhi Yuan; et al. (CoRR, 2025)

Virology Capabilities Test (VCT): A Multimodal Virology Q&A Benchmark. - Jasper Götting; Pedro Medeiros; Jon G. Sanders; Nathaniel Li; Long Phan; Karam Elabd; Lennart Justen; Dan Hendrycks; Seth Donoughe (CoRR, 2025)

The Singapore Consensus on Global AI Safety Research Priorities. - Yoshua Bengio; Tegan Maharaj; Luke Ong; Stuart Russell 001; Dawn Song; Max Tegmark; Lan Xue; Ya-Qin Zhang; Stephen Casper; Wan Sie Lee; Sören Mindermann; Vanessa Wilfred; et al. (CoRR, 2025)

AI Risk-Management Standards Profile for General-Purpose AI (GPAI) and Foundation Models. - Anthony M. Barrett; Jessica Newman; Brandie Nonnecke; Nada Madkour; Dan Hendrycks; Evan R. Murphy; Krystal Jackson; Deepika Raman (CoRR, 2025)

Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety. - Tomek Korbak; Mikita Balesni; Elizabeth Barnes; Yoshua Bengio; Joe Benton; Joseph Bloom; Mark Chen 003; Alan Cooney; Allan Dafoe; Anca D. Dragan; Scott Emmons; Owain Evans; et al. (CoRR, 2025)

Security Challenges in AI Agent Deployment: Insights from a Large Scale Public Competition. - Andy Zou; Maxwell Lin; Eliot Krzysztof Jones; Micha Nowak; Mateusz Dziemian; Nick Winter; Alexander Grattan; Valent Nathanael; Ayla Croft; Xander Davies; Jai Patel; Robert Kirk; et al. (CoRR, 2025)

TextQuests: How Good are LLMs at Text-Based Video Games? - Long Phan; Mantas Mazeika; Andy Zou; Dan Hendrycks (CoRR, 2025)