most citedUser Inference Attacks on Large Language Models

2 citations · 2 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CR2026

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…

cs.CR2026

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…

cs.CR2026

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…

cs.CR2026

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…

cs.CR2026

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…

cs.CR2023★ 2 cited

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…

cs.LG2023

Dropout Attacks

Andrew Yuan, Alina Oprea, Cheng Tan

Dropout is a common operator in deep learning, aiming to prevent overfitting by randomly dropping neurons during training. This paper introduces a new family of poisoning attacks a…