Adversarial Defenses for LLMs

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​In his talk, Samuel Simko from ETH Zurich will present his recent work on adversarial defenses for LLMs, developed with the Jinesis Lab (University of Toronto). The talk will cover a series of approaches, ranging from triplet-based contrastive learning defenses to honeypot-style defenses designed to avoid worst-case behavior. He will also discuss patterns observed in contest-winning manual jailbreaking prompts, ideas for tamper-resistant safeguards, and the current limits of attacks, defenses, and evaluation methodologies.

​Event Schedule
6:00 to 6:30 - Food and introductions
6:30 to 7:30 - Presentation and Q&A
7:30 to 9:00 - Open Discussions

​​​If you can't attend in person, join our live stream starting at 6:30 pm via this link.