1 citations · 1 across the 4 of their papers we have counts for
5 papers
Self-Propagating Misalignment in LLM Agents, and Why Auditing or Disabling Memory Is Not Enough
Debeshee Das, Jacqueline Tay, Bruce Tsai +2
Memory poisoning attacks on LLM agents typically assume an external adversary who plants content in the agent's persistent memory to steer its behavior. We instead study, with no a…
Trojan Hippo Bench: A Dynamic Benchmark for Persistent Memory Attacks and Defenses in LLM Agents
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. The Trojan Hippo attack is a class of per…
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…