3 papers
cs.LG2025
Cascade: Token-Sharded Private LLM Inference
Rahul Thomas, Louai Zahran, Erica Choi +3
As LLMs continue to increase in parameter size, the computational resources required to run them are available to fewer parties. Therefore, third-party inference services -- where…
cs.CR2025
An Attack to Break Permutation-Based Private Third-Party Inference Schemes for LLMs
Rahul Thomas, Louai Zahran, Erica Choi +3
Recent advances in Large Language Models (LLMs) have led to the widespread adoption of third-party inference services, raising critical privacy concerns. Existing methods of perfor…
cs.LG2024
vTune: Verifiable Fine-Tuning for LLMs Through Backdooring
Eva Zhang, Arka Pal, Akilesh Potti +1
As fine-tuning large language models (LLMs) becomes increasingly prevalent, users often rely on third-party services with limited visibility into their fine-tuning processes. This…