3 papers
cs.AI2026
FATHOMS-RAG: A Framework for the Assessment of Thinking and Observation in Multimodal Systems that use Retrieval Augmented Generation
Samuel Hildebrand, Curtis Taylor, Sean Oesch +5
Retrieval-augmented generation (RAG) has emerged as a promising paradigm for improving factual accuracy in large language models (LLMs). We introduce a benchmark designed to evalua…
cs.CR2025
Living Off the LLM: How LLMs Will Change Adversary Tactics
Sean Oesch, Jack Hutchins, Luke Koch +1
In living off the land attacks, malicious actors use legitimate tools and processes already present on a system to avoid detection. In this paper, we explore how the on-device LLMs…
cs.CR2025
Attacks and Defenses Against LLM Fingerprinting
Kevin Kurian, Ethan Holland, Sean Oesch
As large language models are increasingly deployed in sensitive environments, fingerprinting attacks pose significant privacy and security risks. We present a study of LLM fingerpr…