2 citations · 2 across the 6 of their papers we have counts for
5 papers · 1 filter
Render Before Reading: Visual Rendering as a Prompt Injection Defense
Jie Zhang, Andrei Baroian, Jan N. van Rijn +2
Large language models are vulnerable to prompt injection attacks, where third-party adversarial content can hijack the model's behavior. In this paper, we study the role played by…
CryptanalysisBench: Can LLMs do Cryptanalysis?
Lukas Fluri, Avital Shafran, Nicholas Carlini +5
Cryptanalysis - the task of finding attacks against cryptographic schemes - sits at the intersection of mathematical reasoning and cybersecurity, two areas where LLMs have advanced…
Laundering AI Authority with Adversarial Examples
Jie Zhang, Pura Peetathawatchai, Florian Tramèr +1
Vision-language models (VLMs) are increasingly deployed as trusted authorities -- fact-checking images on social media, comparing products, and moderating content. Users implicitly…
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…
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…