Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation. - Matthias Hein 001; Maksym Andriushchenko (NIPS, 2017)

Logit Pairing Methods Can Fool Gradient-Based Attacks. - Marius Mosbach; Maksym Andriushchenko; Thomas Alexander Trost; Matthias Hein 001; Dietrich Klakow (CoRR, 2018)

Provable Robustness of ReLU networks via Maximization of Linear Regions. - Francesco Croce; Maksym Andriushchenko; Matthias Hein 001 (AISTATS, 2019)

Why ReLU Networks Yield High-Confidence Predictions Far Away From the Training Data and How to Mitigate the Problem. - Matthias Hein 001; Maksym Andriushchenko; Julian Bitterwolf (CVPR, 2019)

Provably robust boosted decision stumps and trees against adversarial attacks. - Maksym Andriushchenko; Matthias Hein 001 (NeurIPS, 2019)

Square Attack: A Query-Efficient Black-Box Adversarial Attack via Random Search. - Maksym Andriushchenko; Francesco Croce; Nicolas Flammarion; Matthias Hein 001 (ECCV (23), 2020)

Understanding and Improving Fast Adversarial Training. - Maksym Andriushchenko; Nicolas Flammarion (NeurIPS, 2020)

RobustBench: a standardized adversarial robustness benchmark. - Francesco Croce; Maksym Andriushchenko; Vikash Sehwag; Nicolas Flammarion; Mung Chiang; Prateek Mittal; Matthias Hein 001 (CoRR, 2020)

On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines. - Marius Mosbach; Maksym Andriushchenko; Dietrich Klakow (ICLR, 2021)

RobustBench: a standardized adversarial robustness benchmark. - Francesco Croce; Maksym Andriushchenko; Vikash Sehwag; Edoardo Debenedetti; Nicolas Flammarion; Mung Chiang; Prateek Mittal; Matthias Hein 001 (NeurIPS Datasets and Benchmarks, 2021)

Sparse-RS: A Versatile Framework for Query-Efficient Sparse Black-Box Adversarial Attacks. - Francesco Croce; Maksym Andriushchenko; Naman D. Singh; Nicolas Flammarion; Matthias Hein 001 (AAAI, 2022)

ARIA: Adversarially Robust Image Attribution for Content Provenance. - Maksym Andriushchenko; Xiaoyang Rebecca Li; Geoffrey Oxholm; Thomas Gittings; Tu Bui; Nicolas Flammarion; John P. Collomosse (CVPR Workshops, 2022)

Towards Understanding Sharpness-Aware Minimization. - Maksym Andriushchenko; Nicolas Flammarion (ICML, 2022)

On the effectiveness of adversarial training against common corruptions. - Klim Kireev; Maksym Andriushchenko; Nicolas Flammarion (UAI, 2022)

A Modern Look at the Relationship between Sharpness and Generalization. - Maksym Andriushchenko; Francesco Croce; Maximilian Müller; Matthias Hein 001; Nicolas Flammarion (ICML, 2023)

SGD with Large Step Sizes Learns Sparse Features. - Maksym Andriushchenko; Aditya Vardhan Varre; Loucas Pillaud-Vivien; Nicolas Flammarion (ICML, 2023)

Sharpness-Aware Minimization Leads to Low-Rank Features. - Maksym Andriushchenko; Dara Bahri; Hossein Mobahi; Nicolas Flammarion (NeurIPS, 2023)

Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings. - Klim Kireev; Maksym Andriushchenko; Carmela Troncoso; Nicolas Flammarion (NeurIPS, 2023)

The Effects of Overparameterization on Sharpness-aware Minimization: An Empirical and Theoretical Analysis. - Sungbin Shin; Dongyeop Lee; Maksym Andriushchenko; Namhoon Lee (CoRR, 2023)

Scaling Compute Is Not All You Need for Adversarial Robustness. - Edoardo Debenedetti; Zishen Wan; Maksym Andriushchenko; Vikash Sehwag; Kshitij Bhardwaj; Bhavya Kailkhura (CoRR, 2023)

Layer-wise linear mode connectivity. - Linara Adilova; Maksym Andriushchenko; Michael Kamp; Asja Fischer; Martin Jaggi (ICLR, 2024)

Long Is More for Alignment: A Simple but Tough-to-Beat Baseline for Instruction Fine-Tuning. - Hao Zhao; Maksym Andriushchenko; Francesco Croce; Nicolas Flammarion (ICML, 2024)

JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models. - Patrick Chao; Edoardo Debenedetti; Alexander Robey; Maksym Andriushchenko; Francesco Croce; Vikash Sehwag; Edgar Dobriban; Nicolas Flammarion; George J. Pappas; Florian Tramèr; Hamed Hassani; Eric Wong 001 (NeurIPS, 2024)

Why Do We Need Weight Decay in Modern Deep Learning? - Francesco D'Angelo; Maksym Andriushchenko; Aditya Vardhan Varre; Nicolas Flammarion (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)

Competition Report: Finding Universal Jailbreak Backdoors in Aligned LLMs. - Javier Rando; Francesco Croce; Krystof Mitka; Stepan Shabalin; Maksym Andriushchenko; Nicolas Flammarion; Florian Tramèr (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)

Exploring Memorization and Copyright Violation in Frontier LLMs: A Study of the New York Times v. OpenAI 2023 Lawsuit. - Joshua Freeman; Chloe Rippe; Edoardo Debenedetti; Maksym Andriushchenko (CoRR, 2024)

Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks. - Maksym Andriushchenko; Francesco Croce; Nicolas Flammarion (ICLR, 2025)

Does Refusal Training in LLMs Generalize to the Past Tense? - Maksym Andriushchenko; Nicolas Flammarion (ICLR, 2025)

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)

Is In-Context Learning Sufficient for Instruction Following in LLMs? - Hao Zhao; Maksym Andriushchenko; Francesco Croce; Nicolas Flammarion (ICLR, 2025)

Critical Influence of Overparameterization on Sharpness-aware Minimization. - Sungbin Shin; Dongyeop Lee; Maksym Andriushchenko; Namhoon Lee (UAI, 2025)

Capability-Based Scaling Laws for LLM Red-Teaming. - Alexander Panfilov; Paul Kassianik; Maksym Andriushchenko; Jonas Geiping (CoRR, 2025)

Monitoring Decomposition Attacks in LLMs with Lightweight Sequential Monitors. - Chen Yueh-Han; Nitish Joshi; Yulin Chen; Maksym Andriushchenko; Rico Angell; He He 001 (CoRR, 2025)

OS-Harm: A Benchmark for Measuring Safety of Computer Use Agents. - Thomas Kuntz; Agatha Duzan; Hao Zhao; Francesco Croce; Zico Kolter; Nicolas Flammarion; Maksym Andriushchenko (CoRR, 2025)