4 papers
Trojan Hippo: Weaponizing Agent Memory for Data Exfiltration
Debeshee Das, Julien Piet, Darya Kaviani +3
Memory systems enable otherwise-stateless LLM agents to persist user information across sessions, but also introduce a new attack surface. We characterize the Trojan Hippo attack,…
CommandSans: Securing AI Agents with Surgical Precision Prompt Sanitization
Debeshee Das, Luca Beurer-Kellner, Marc Fischer +1
The increasing adoption of LLM agents with access to numerous tools and sensitive data significantly widens the attack surface for indirect prompt injections. Due to the context-de…
Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data
Jie Zhang, Debeshee Das, Gautam Kamath +1
We consider the problem of a training data proof, where a data creator or owner wants to demonstrate to a third party that some machine learning model was trained on their data. Tr…
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…