20 papers
CryptanalysisBench: Can LLMs do Cryptanalysis?
Lukas Fluri, Avital Shafran, Nicholas Carlini +5
Cryptanalysis - the task of finding attacks against cryptographic schemes - sits at the intersection of mathematical reasoning and cybersecurity, two areas where LLMs have advanced…
The 2026 Singapore Consensus on Global AI Safety Research Priorities
Stephen Casper, Oskar Galeev, Yoshua Bengio +117
Frontier AI capabilities and autonomy are advancing rapidly. A growing number of real-world incidents make a trusted AI ecosystem essential to embracing AI with confidence. The 202…
Untrusted Content Masking for Web Agents with Security Guarantees
Kristina NikoliÄ, Egor Zverev, Javier Rando +3
Defenses that provide security guarantees against prompt injection attacks rely on strict isolation between trusted instructions and untrusted data. In text-based environments such…
TabPATE: Differentially Private Tabular In-Context Learning Without Public Data
Dariush Wahdany, Matthew Jagielski, Jesse C. Cresswell +2
Tabular foundation models enable accurate in-context learning (ICL) from small labeled datasets, but the private records placed in context can leak through model predictions. We fi…
Curation Leaks: Membership Inference Attacks against Data Curation for Machine Learning
Dariush Wahdany, Matthew Jagielski, Adam Dziedzic +1
In machine learning, curation is used to select the most valuable data for improving both model accuracy and computational efficiency. Recently, curation has also been explored as…
SoK: Data Minimization in Machine Learning
Robin Staab, Nikola JovanoviÄ, Kimberly Mai +4
Data minimization (DM) describes the principle of collecting only the data strictly necessary for a given task. It is a foundational principle across major data protection regulati…