3 papers
cs.LG2026
Scaling Patterns in Adversarial Alignment: Evidence from Multi-LLM Jailbreak Experiments
Samuel Nathanson, Rebecca Williams, Cynthia Matuszek
Large language models (LLMs) increasingly operate in multi-agent and safety-critical settings, raising open questions about how their vulnerabilities scale when models interact adv…
cs.CR2025
AI Bill of Materials and Beyond: Systematizing Security Assurance through the AI Risk Scanning (AIRS) Framework
Samuel Nathanson, Alexander Lee, Catherine Chen Kieffer +7
Assurance for artificial intelligence (AI) systems remains fragmented across software supply-chain security, adversarial machine learning, and governance documentation. Existing tr…
cs.AI2025
Causal Masking on Spatial Data: An Information-Theoretic Case for Learning Spatial Datasets with Unimodal Language Models
Jared Junkin, Samuel Nathanson
Language models are traditionally designed around causal masking. In domains with spatial or relational structure, causal masking is often viewed as inappropriate, and sequential l…