15 citations · 27 across the 16 of their papers we have counts for
7 papers · 1 filter
Patronus: Safeguarding Text-to-Image Models against White-Box Adversaries
Xinfeng Li, Shengyuan Pang, Jialin Wu +5
Text-to-image (T2I) models, though exhibiting remarkable creativity in image generation, can be exploited to produce unsafe images. Existing safety measures, e.g., content moderati…
Membership Inference Attacks on Sequence Models
Lorenzo Rossi, Michael Aerni, Jie Zhang +1
Sequence models, such as Large Language Models (LLMs) and autoregressive image generators, have a tendency to memorize and inadvertently leak sensitive information. While this tend…
Does Training with Synthetic Data Truly Protect Privacy?
Yunpeng Zhao, Jie Zhang
As synthetic data becomes increasingly popular in machine learning tasks, numerous methods--without formal differential privacy guarantees--use synthetic data for training. These m…
Blind Baselines Beat Membership Inference Attacks for Foundation Models
Debeshee Das, Jie Zhang, Florian Tramèr
Membership inference (MI) attacks try to determine if a data sample was used to train a machine learning model. For foundation models trained on unknown Web data, MI attacks are of…
AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents
Edoardo Debenedetti, Jie Zhang, Mislav Balunović +3
AI agents aim to solve complex tasks by combining text-based reasoning with external tool calls. Unfortunately, AI agents are vulnerable to prompt injection attacks where data retu…
Evaluations of Machine Learning Privacy Defenses are Misleading
Michael Aerni, Jie Zhang, Florian Tramèr
Empirical defenses for machine learning privacy forgo the provable guarantees of differential privacy in the hope of achieving higher utility while resisting realistic adversaries.…