2 citations · 5 across the 16 of their papers we have counts for
7 papers · 1 filter
Deep-Research Agents Can Be Poisoned via User-Generated Content
Tingwei Zhang, Harold Triedman, Vitaly Shmatikov
Deep-research agents are an alternative to conventional Web search. They use multi-agent pipelines to issue multiple Web searches related to user queries, retrieve relevant content…
How to Steal Reasoning Without Reasoning Traces
Tingwei Zhang, John X. Morris, Vitaly Shmatikov
Many large language models (LLMs) use reasoning to generate responses but do not reveal their full reasoning traces (a.k.a. chains of thought), instead outputting only final answer…
Multi-Agent Systems Execute Arbitrary Malicious Code
Harold Triedman, Rishi Jha, Vitaly Shmatikov
Multi-agent systems coordinate LLM-based agents to perform tasks on users' behalf. In real-world applications, multi-agent systems will inevitably interact with untrusted inputs, s…
Rerouting LLM Routers
Avital Shafran, Roei Schuster, Thomas Ristenpart +1
LLM routers aim to balance quality and cost of generation by classifying queries and routing them to a cheaper or more expensive LLM depending on their complexity. Routers represen…
Adversarial Hubness in Multi-Modal Retrieval
Tingwei Zhang, Fnu Suya, Rishi Jha +2
Hubness is a phenomenon in high-dimensional vector spaces where a point from the natural distribution is unusually close to many other points. This is a well-known problem in infor…
Self-interpreting Adversarial Images
Tingwei Zhang, Collin Zhang, John X. Morris +2
We introduce a new type of indirect, cross-modal injection attacks against visual language models that enable creation of self-interpreting images. These images contain hidden "met…
Machine Against the RAG: Jamming Retrieval-Augmented Generation with Blocker Documents
Avital Shafran, Roei Schuster, Vitaly Shmatikov
Retrieval-augmented generation (RAG) systems respond to queries by retrieving relevant documents from a knowledge database and applying an LLM to the retrieved documents. We demons…