96 citations · 159 across the 11 of their papers we have counts for
4 papers · 1 filter
GPT-Red: Automated Red Teaming via Self-Play at Scale
Eric Wallace, Christopher A. Choquette-Choo, Nikhil Kandpal +15
We introduce \textbf{GPT-Red}, an automated red-teaming agent that is trained to discover novel prompt injection attacks against frontier LLMs. The goal of this model is to evaluat…
User Inference Attacks on Large Language Models
Nikhil Kandpal, Krishna Pillutla, Alina Oprea +3
Fine-tuning is a common and effective method for tailoring large language models (LLMs) to specialized tasks and applications. In this paper, we study the privacy implications of f…
Backdoor Attacks for In-Context Learning with Language Models
Nikhil Kandpal, Matthew Jagielski, Florian Tramèr +1
Because state-of-the-art language models are expensive to train, most practitioners must make use of one of the few publicly available language models or language model APIs. This…
Deduplicating Training Data Mitigates Privacy Risks in Language Models
Nikhil Kandpal, Eric Wallace, Colin Raffel
Past work has shown that large language models are susceptible to privacy attacks, where adversaries generate sequences from a trained model and detect which sequences are memorize…