2 citations · 2 across the 6 of their papers we have counts for
6 papers · 1 filter
The Privacy Fallacy of Crowdsourced Fine-Tuning: Extracting Proprietary Data via Topic-Based Poisoning
Sae Furukawa, Alina Oprea
Supervised fine-tuning (SFT) is widely used to adapt large language models to downstream tasks. Crowdsourcing user conversations is an established approach to collecting SFT data a…
Beyond Predictable Paths: Redefining AI Security Incident Reporting for Agents
Anastasia Pustozerova, Eugene Bagdasarian, Luca Beurer-Kellner +22
AI agents are being deployed rapidly, accompanied by a growing number of AI-specific attacks and corresponding incidents. As incident reporting becomes increasingly important for l…
Attacks and Mitigations for Distributed Governance of Agentic AI under Byzantine Adversaries
Matthew D. Laws, Alina Oprea, Cristina Nita-Rotaru
Agentic AI governance is a critical component of agentic AI infrastructure ensuring that agents follow their owner's communication and interaction policies, and providing protectio…
Toward a Principled Framework for Agent Safety Measurement
Shuyi Lin, Anshuman Suri, Alina Oprea +1
LLM agents emit actions, not just text, and once taken, those actions often cannot be undone. Yet today's agent-safety evaluations run greedy or a few sampled rollouts and report a…
Reconstruction of Personally Identifiable Information from Proprietary Data in Supervised Fine-Tuned Models
Sae Furukawa, Alina Oprea
Supervised Finetuning (SFT) has become one of the primary methods for adapting a large language model (LLM) with extensive pre-trained knowledge to domain-specific, instruction-fol…
User Inference Attacks on Large Language Models
Nikhil Kandpal, Krishna Pillutla, Alina Oprea +3
Fine-tuning is a common and effective method for tailoring large language models (LLMs) to specialized tasks and applications. In this paper, we study the privacy implications of f…