Matthew Jagielski

This work formalizes poison selection as oracle-budgeted set optimization and introduces SAILS (Set-level Audit-Informed Iterative Learned Selection), which learns a set scorer from a few hundred finetune-and-evaluate runs, ranks millions of candidate sets, and audits only a small shortlist.

LLMs are not just passive observers; they can uncover novel cryptographic vulnerabilities that challenge our current understanding of digital security.

Web agents can now safely interact with complex environments while avoiding prompt injection attacks by masking untrusted content without ever reading it.