SecAppDev 2026 Faculty
Katharine Jarmul
Founder, Probably Private
Katharine Jarmul focuses her work and research on privacy and security in data science, deep learning and AI. She is author of the well received O'Reilly book Practical Data Privacy (O'Reilly 2023) and has more than 10 years experience in machine learning/AI where she has helped build large scale AI systems with privacy and security built in.
Don't miss out on SecAppDev!
Grab your seat nowAttacking and Defending LLMs
One-day workshop by Katharine Jarmul in room Lemaire
This workshop gives you hands-on experience with attacking large language models (LLMs) using a range of prompt-based strategies. You will actively explore how these attacks work in practice and what their impact is on real systems. The workshop also gives you insight into defensive techniques, and shows how architectural choices, testing approaches, and security observability can be used to strengthen applications built with generative models.
Learning goal: Practical strategies, best practices, and tools to improve the security posture of modern AI systems.
How to (still) trick AI: Adversarial ML for Today
Introductory lecture by Katharine Jarmul
There's many known (and still being discovered) attack vectors against deep learning models. In this session, we'll walk through some of the history of adversarial ML and deep learning and find what's changed and what's stayed the same.
Key takeaway: AI/DL models are inherently nondeterministic and have other properties that allow for old, new and interesting attacks.
Privacy Attacks on Deep Learning Systems
Advanced lecture by Katharine Jarmul
In this session, you'll dive into how this creates interesting vectors for privacy attacks on AI/ML systems. You'll also be introduced to what types of interventions might work to address such issues.
Key takeaway: Information exfiltration due to memorization is an interesting attack vector for today's AI/deep learning models.