Removable and/or Repeated Units Emerge in Overparametrized Deep Neural Networks. - Stephen Casper; Xavier Boix; Vanessa D'Amario; Ling Guo; Martin Schrimpf; Kasper Vinken; Gabriel Kreiman (CoRR, 2019)

Probing Neural Dialog Models for Conversational Understanding. - Abdelrhman Saleh; Tovly Deutsch; Stephen Casper; Yonatan Belinkov; Stuart M. Shieber (CoRR, 2020)

The Achilles Heel Hypothesis: Pitfalls for AI Systems via Decision Theoretic Adversaries. - Stephen Casper (CoRR, 2020)

Frivolous Units: Wider Networks Are Not Really That Wide. - Stephen Casper; Xavier Boix; Vanessa D'Amario; Ling Guo; Martin Schrimpf; Kasper Vinken; Gabriel Kreiman (AAAI, 2021)

Clusterability in Neural Networks. - Daniel Filan; Stephen Casper; Shlomi Hod; Cody Wild; Andrew Critch; Stuart Russell 001 (CoRR, 2021)

One Thing to Fool them All: Generating Interpretable, Universal, and Physically-Realizable Adversarial Features. - Stephen Casper; Max Nadeau; Gabriel Kreiman (CoRR, 2021)

Detecting Modularity in Deep Neural Networks. - Shlomi Hod; Stephen Casper; Daniel Filan; Cody Wild; Andrew Critch; Stuart Russell 001 (CoRR, 2021)

Robust Feature-Level Adversaries are Interpretability Tools. - Stephen Casper; Max Nadeau; Dylan Hadfield-Menell; Gabriel Kreiman (NeurIPS, 2022)

Diagnostics for Deep Neural Networks with Automated Copy/Paste Attacks. - Stephen Casper; Kaivalya Hariharan; Dylan Hadfield-Menell (CoRR, 2022)

White-Box Adversarial Policies in Deep Reinforcement Learning. - Stephen Casper; Dylan Hadfield-Menell; Gabriel Kreiman (SafeAI@AAAI, 2023)

Cognitive Dissonance: Why Do Language Model Outputs Disagree with Internal Representations of Truthfulness? - Kevin Liu; Stephen Casper; Dylan Hadfield-Menell; Jacob Andreas (EMNLP, 2023)

Red Teaming Deep Neural Networks with Feature Synthesis Tools. - Stephen Casper; Tong Bu; Yuxiao Li; Jiawei Li; Kevin Zhang; Kaivalya Hariharan; Dylan Hadfield-Menell (NeurIPS, 2023)

Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks. - Tilman Räuker; Anson Ho; Stephen Casper; Dylan Hadfield-Menell (SaTML, 2023)

Benchmarking Interpretability Tools for Deep Neural Networks. - Stephen Casper; Yuxiao Li; Jiawei Li; Tong Bu; Kevin Zhang; Dylan Hadfield-Menell (CoRR, 2023)

Explore, Establish, Exploit: Red Teaming Language Models from Scratch. - Stephen Casper; Jason Lin; Joe Kwon; Gatlen Culp; Dylan Hadfield-Menell (CoRR, 2023)

Measuring the Success of Diffusion Models at Imitating Human Artists. - Stephen Casper; Zifan Guo; Shreya Mogulothu; Zachary Marinov; Chinmay Deshpande; Rui-Jie Yew; Zheng Dai; Dylan Hadfield-Menell (CoRR, 2023)

Scalable and Transferable Black-Box Jailbreaks for Language Models via Persona Modulation. - Rusheb Shah; Quentin Feuillade-Montixi; Soroush Pour; Arush Tagade; Stephen Casper; Javier Rando (CoRR, 2023)

Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback. - Stephen Casper; Xander Davies; Claudia Shi; Thomas Krendl Gilbert; Jérémy Scheurer; Javier Rando; Rachel Freedman; Tomasz Korbak; David Lindner; Pedro Freire; Tony Tong Wang; Samuel Marks; et al. (Trans. Mach. Learn. Res., 2023)

Black-Box Access is Insufficient for Rigorous AI Audits. - Stephen Casper; Carson Ezell; Charlotte Siegmann; Noam Kolt; Taylor Lynn Curtis; Benjamin Bucknall; Andreas A. Haupt; Kevin Wei; Jérémy Scheurer; Marius Hobbhahn; Lee Sharkey; Satyapriya Krishna; et al. (FAccT, 2024)

Rethinking Machine Unlearning for Large Language Models. - Sijia Liu 001; Yuanshun Yao; Jinghan Jia; Stephen Casper; Nathalie Baracaldo; Peter Hase; Xiaojun Xu; Yuguang Yao; Hang Li 001; Kush R. Varshney; Mohit Bansal; Sanmi Koyejo; et al. (CoRR, 2024)

Eight Methods to Evaluate Robust Unlearning in LLMs. - Aengus Lynch; Phillip Guo; Aidan Ewart; Stephen Casper; Dylan Hadfield-Menell (CoRR, 2024)

The SaTML '24 CNN Interpretability Competition: New Innovations for Concept-Level Interpretability. - Stephen Casper; Jieun Yun; Joonhyuk Baek; Yeseong Jung; Minhwan Kim; Kiwan Kwon; Saerom Park; Hayden Moore; David Shriver; Marissa Connor; Keltin Grimes; Angus Nicolson; et al. (CoRR, 2024)

Foundational Challenges in Assuring Alignment and Safety of Large Language Models. - Usman Anwar; Abulhair Saparov; Javier Rando; Daniel Paleka; Miles Turpin; Peter Hase; Ekdeep Singh Lubana; Erik Jenner; Stephen Casper; Oliver Sourbut; Benjamin L. Edelman; Zhaowei Zhang; et al. (CoRR, 2024)

Open Problems in Technical AI Governance. - Anka Reuel; Ben Bucknall; Stephen Casper; Timothy Fist; Lisa Soder; Onni Aarne; Lewis Hammond; Lujain Ibrahim; Alan Chan; Peter Wills; Markus Anderljung; Ben Garfinkel; et al. (CoRR, 2024)

Targeted Latent Adversarial Training Improves Robustness to Persistent Harmful Behaviors in LLMs. - Abhay Sheshadri; Aidan Ewart; Phillip Guo; Aengus Lynch; Cindy Wu; Vivek Hebbar; Henry Sleight; Asa Cooper Stickland; Ethan Perez; Dylan Hadfield-Menell; Stephen Casper (CoRR, 2024)

The AI Risk Repository: A Comprehensive Meta-Review, Database, and Taxonomy of Risks From Artificial Intelligence. - Peter Slattery; Alexander K. Saeri; Emily A. C. Grundy; Jess Graham; Michael Noetel; Risto Uuk; James Dao; Soroush Pour; Stephen Casper; Neil Thompson (CoRR, 2024)

Multilevel Interpretability Of Artificial Neural Networks: Leveraging Framework And Methods From Neuroscience. - Zhonghao He; Jascha Achterberg; Katie Collins; Kevin K. Nejad; Danyal Akarca; Yinzhu Yang; Wes Gurnee; Ilia Sucholutsky; Yuhan Tang; Rebeca Ianov; George Ogden; Chole Li; et al. (CoRR, 2024)

What Features in Prompts Jailbreak LLMs? Investigating the Mechanisms Behind Attacks. - Nathalie Maria Kirch; Severin Field; Stephen Casper (CoRR, 2024)

International Scientific Report on the Safety of Advanced AI (Interim Report). - Yoshua Bengio; Sören Mindermann; Daniel Privitera; Tamay Besiroglu; Rishi Bommasani; Stephen Casper; Yejin Choi 001; Danielle Goldfarb; Hoda Heidari; Leila Khalatbari; Shayne Longpre; Vasilios Mavroudis; et al. (CoRR, 2024)

Obfuscated Activations Bypass LLM Latent-Space Defenses. - Luke Bailey; Alex Serrano; Abhay Sheshadri; Mikhail Seleznyov; Jordan Taylor; Erik Jenner; Jacob Hilton; Stephen Casper; Carlos Guestrin; Scott Emmons (CoRR, 2024)

Foundational Challenges in Assuring Alignment and Safety of Large Language Models. - Usman Anwar; Abulhair Saparov; Javier Rando; Daniel Paleka; Miles Turpin; Peter Hase; Ekdeep Singh Lubana; Erik Jenner; Stephen Casper; Oliver Sourbut; Benjamin L. Edelman; Zhaowei Zhang; et al. (Trans. Mach. Learn. Res., 2024)

Randomness, Not Representation: The Unreliability of Evaluating Cultural Alignment in LLMs. - Ariba Khan; Stephen Casper; Dylan Hadfield-Menell (FAccT, 2025)

The Reality of AI and Biorisk. - Aidan Peppin; Anka Reuel; Stephen Casper; Elliot Jones; Andrew Strait; Usman Anwar; Anurag Agrawal; Sayash Kapoor; Sanmi Koyejo; Marie Pellat; Rishi Bommasani; Nick Frosst; et al. (FAccT, 2025)

Rethinking machine unlearning for large language models. - Sijia Liu 001; Yuanshun Yao; Jinghan Jia; Stephen Casper; Nathalie Baracaldo; Peter Hase; Yuguang Yao; Chris Yuhao Liu; Xiaojun Xu; Hang Li 001; Kush R. Varshney; Mohit Bansal; et al. (Nat. Mac. Intell., 2025)

Defending Against Unforeseen Failure Modes with Latent Adversarial Training. - Stephen Casper; Lennart Schulze; Oam Patel; Dylan Hadfield-Menell (Trans. Mach. Learn. Res., 2025)

Model Tampering Attacks Enable More Rigorous Evaluations of LLM Capabilities. - Zora Che; Stephen Casper; Robert Kirk; Anirudh Satheesh; Stewart Slocum; Lev E. McKinney; Rohit Gandikota; Aidan Ewart; Domenic Rosati; Zichu Wu; Zikui Cai; Bilal Chughtai; et al. (Trans. Mach. Learn. Res., 2025)

Open Problems in Technical AI Governance. - Anka Reuel; Benjamin Bucknall; Stephen Casper; Timothy Fist; Lisa Soder; Onni Aarne; Lewis Hammond; Lujain Ibrahim; Alan Chan; Peter Wills; Markus Anderljung; Ben Garfinkel; et al. (Trans. Mach. Learn. Res., 2025)

Latent Adversarial Training Improves Robustness to Persistent Harmful Behaviors in LLMs. - Abhay Sheshadri; Aidan Ewart; Phillip Guo; Aengus Lynch; Cindy Wu; Vivek Hebbar; Henry Sleight; Asa Cooper Stickland; Ethan Perez; Dylan Hadfield-Menell; Stephen Casper (Trans. Mach. Learn. Res., 2025)

Open Problems in Machine Unlearning for AI Safety. - Fazl Barez; Tingchen Fu; Ameya Prabhu; Stephen Casper; Amartya Sanyal; Adel Bibi; Aidan O'Gara; Robert Kirk; Ben Bucknall; Timothy Fist; Luke Ong; Philip Torr 001; et al. (CoRR, 2025)

Open Problems in Mechanistic Interpretability. - Lee Sharkey; Bilal Chughtai; Joshua Batson; Jack Lindsey; Jeff Wu 003; Lucius Bushnaq; Nicholas Goldowsky-Dill; Stefan Heimersheim; Alejandro Ortega; Joseph Isaac Bloom; Stella Biderman; Adrià Garriga-Alonso; et al. (CoRR, 2025)

International AI Safety Report. - Yoshua Bengio; Sören Mindermann; Daniel Privitera; Tamay Besiroglu; Rishi Bommasani; Stephen Casper; Yejin Choi 001; Philip Fox; Ben Garfinkel; Danielle Goldfarb; Hoda Heidari; Anson Ho; et al. (CoRR, 2025)

The AI Agent Index. - Stephen Casper; Luke Bailey; Rosco Hunter; Carson Ezell; Emma Cabalé; Michael Gerovitch; Stewart Slocum; Kevin Wei; Nikola Jurkovic; Ariba Khan; Phillip J. K. Christoffersen; A. Pinar Ozisik; et al. (CoRR, 2025)

Pitfalls of Evidence-Based AI Policy. - Stephen Casper; David Krueger 001; Dylan Hadfield-Menell (CoRR, 2025)

Adversarial Alignment for LLMs Requires Simpler, Reproducible, and More Measurable Objectives. - Leo Schwinn; Yan Scholten; Tom Wollschläger; Sophie Xhonneux; Stephen Casper; Stephan Günnemann; Gauthier Gidel (CoRR, 2025)

Practical Principles for AI Cost and Compute Accounting. - Stephen Casper; Luke Bailey; Tim Schreier (CoRR, 2025)

Audit Cards: Contextualizing AI Evaluations. - Leon Staufer; Mick Yang; Anka Reuel; Stephen Casper (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)

STACK: Adversarial Attacks on LLM Safeguard Pipelines. - Ian R. McKenzie; Oskar Hollinsworth; Tom Tseng; Xander Davies; Stephen Casper; Aaron D. Tucker; Robert Kirk; Adam Gleave (CoRR, 2025)

Deep Ignorance: Filtering Pretraining Data Builds Tamper-Resistant Safeguards into Open-Weight LLMs. - Kyle O'Brien; Stephen Casper; Quentin Anthony; Tomek Korbak; Robert Kirk; Xander Davies; Ishan Mishra; Geoffrey Irving; Yarin Gal; Stella Biderman (CoRR, 2025)