Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embedding Space. - Leo Schwinn; David Dobre; Sophie Xhonneux; Gauthier Gidel; Stephan Günnemann (NeurIPS, 2024)

Efficient Adversarial Training in LLMs with Continuous Attacks. - Sophie Xhonneux; Alessandro Sordoni; Stephan Günnemann; Gauthier Gidel; Leo Schwinn (NeurIPS, 2024)

In-Context Learning Can Re-learn Forbidden Tasks. - Sophie Xhonneux; David Dobre; Jian Tang; Gauthier Gidel; Dhanya Sridhar (CoRR, 2024)

Asynchronous RLHF: Faster and More Efficient Off-Policy RL for Language Models. - Michael Noukhovitch; Shengyi Huang; Sophie Xhonneux; Arian Hosseini; Rishabh Agarwal; Aaron C. Courville (ICLR, 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)

A generative approach to LLM harmfulness detection with special red flag tokens. - Sophie Xhonneux; David Dobre; Mehrnaz Mohfakhami; Leo Schwinn; Gauthier Gidel (CoRR, 2025)

LLM-Safety Evaluations Lack Robustness. - Tim Beyer; Sophie Xhonneux; Simon Geisler; Gauthier Gidel; Leo Schwinn; Stephan Günnemann (CoRR, 2025)

Model Collapse Is Not a Bug but a Feature in Machine Unlearning for LLMs. - Yan Scholten; Sophie Xhonneux; Leo Schwinn; Stephan Günnemann (CoRR, 2025)