2 citations · 2 across the 6 of their papers we have counts for
8 papers
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…
Beyond Labeling Oracles: What does it mean to steal ML models?
Avital Shafran, Ilia Shumailov, Murat A. Erdogdu +1
Model extraction attacks are designed to steal trained models with only query access, as is often provided through APIs that ML-as-a-Service providers offer. Machine Learning (ML)…
Membership Inference Attacks are Easier on Difficult Problems
Avital Shafran, Shmuel Peleg, Yedid Hoshen
Membership inference attacks (MIA) try to detect if data samples were used to train a neural network model, e.g. to detect copyright abuses. We show that models with higher dimensi…
Crypto-Oriented Neural Architecture Design
Avital Shafran, Gil Segev, Shmuel Peleg +1
As neural networks revolutionize many applications, significant privacy conflicts between model users and providers emerge. The cryptography community developed a variety of techni…