2 citations · 5 across the 16 of their papers we have counts for
5 papers · 1 filter
Learning to Detect Language Model Training Data via Active Reconstruction
Junjie Oscar Yin, John X. Morris, Vitaly Shmatikov +2
Detecting LLM training data is generally framed as a membership inference attack (MIA) problem. However, conventional MIAs operate passively on fixed model weights, using log-likel…
Breaking and Fixing Defenses Against Control-Flow Hijacking in Multi-Agent Systems
Rishi Jha, Harold Triedman, Justin Wagle +1
Control-flow hijacking attacks manipulate orchestration mechanisms in multi-agent systems into performing unsafe actions that compromise the system and exfiltrate sensitive informa…
MillStone: How Open-Minded Are LLMs?
Harold Triedman, Vitaly Shmatikov
Large language models equipped with Web search, information retrieval tools, and other agentic capabilities are beginning to supplant traditional search engines. As users start to…
Harnessing the Universal Geometry of Embeddings
Rishi Jha, Collin Zhang, Vitaly Shmatikov +1
We introduce the first method for translating text embeddings from one vector space to another without any paired data, encoders, or predefined sets of matches. Our unsupervised ap…
Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Research
A. Feder Cooper, Christopher A. Choquette-Choo, Miranda Bogen +34
"Machine unlearning" is a popular proposed solution for mitigating the existence of content in an AI model that is problematic for legal or moral reasons, including privacy, copyri…