1.4k citations · 1.9k across the 18 of their papers we have counts for
18 papers
Representation Bending for Large Language Model Safety
Ashkan Yousefpour, Taeheon Kim, Ryan S. Kwon +7
Large Language Models (LLMs) have emerged as powerful tools, but their inherent safety risks - ranging from harmful content generation to broader societal harms - pose significant…
sudo rm -rf agentic_security
Sejin Lee, Jian Kim, Haon Park +3
Large Language Models (LLMs) are increasingly deployed as computer-use agents, autonomously performing tasks within real desktop or web environments. While this evolution greatly e…
M2S: Multi-turn to Single-turn jailbreak in Red Teaming for LLMs
Junwoo Ha, Hyunjun Kim, Sangyoon Yu +4
We introduce a novel framework for consolidating multi-turn adversarial ``jailbreak'' prompts into single-turn queries, significantly reducing the manual overhead required for adve…
ELITE: Enhanced Language-Image Toxicity Evaluation for Safety
Wonjun Lee, Doehyeon Lee, Eugene Choi +5
Current Vision Language Models (VLMs) remain vulnerable to malicious prompts that induce harmful outputs. Existing safety benchmarks for VLMs primarily rely on automated evaluation…
Large Language Models Still Exhibit Bias in Long Text
Wonje Jeung, Dongjae Jeon, Ashkan Yousefpour +1
Existing fairness benchmarks for large language models (LLMs) primarily focus on simple tasks, such as multiple-choice questions, overlooking biases that may arise in more complex…
Selective Vision is the Challenge for Visual Reasoning: A Benchmark for Visual Argument Understanding
Jiwan Chung, Sungjae Lee, Minseo Kim +4
Visual arguments, often used in advertising or social causes, rely on images to persuade viewers to do or believe something. Understanding these arguments requires selective vision…
Aligning Large Language Models by On-Policy Self-Judgment
Sangkyu Lee, Sungdong Kim, Ashkan Yousefpour +3
Existing approaches for aligning large language models with human preferences face a trade-off that requires a separate reward model (RM) for on-policy learning. In this paper, we…
Green Federated Learning
Ashkan Yousefpour, Shen Guo, Ashish Shenoy +7
The rapid progress of AI is fueled by increasingly large and computationally intensive machine learning models and datasets. As a consequence, the amount of compute used in trainin…
Reconciling Security and Communication Efficiency in Federated Learning
Karthik Prasad, Sayan Ghosh, Graham Cormode +3
Cross-device Federated Learning is an increasingly popular machine learning setting to train a model by leveraging a large population of client devices with high privacy and securi…
Papaya: Practical, Private, and Scalable Federated Learning
Dzmitry Huba, John Nguyen, Kshitiz Malik +11
Cross-device Federated Learning (FL) is a distributed learning paradigm with several challenges that differentiate it from traditional distributed learning, variability in the syst…
Opacus: User-Friendly Differential Privacy Library in PyTorch
Ashkan Yousefpour, Igor Shilov, Alexandre Sablayrolles +9
We introduce Opacus, a free, open-source PyTorch library for training deep learning models with differential privacy (hosted at opacus.ai). Opacus is designed for simplicity, flexi…
Federated Learning with Buffered Asynchronous Aggregation
John Nguyen, Kshitiz Malik, Hongyuan Zhan +4
Scalability and privacy are two critical concerns for cross-device federated learning (FL) systems. In this work, we identify that synchronous FL - synchronized aggregation of clie…
ResiliNet: Failure-Resilient Inference in Distributed Neural Networks
Ashkan Yousefpour, Brian Q. Nguyen, Siddartha Devic +5
Federated Learning aims to train distributed deep models without sharing the raw data with the centralized server. Similarly, in distributed inference of neural networks, by partit…
Guardians of the Deep Fog: Failure-Resilient DNN Inference from Edge to Cloud
Ashkan Yousefpour, Siddartha Devic, Brian Q. Nguyen +4
Partitioning and distributing deep neural networks (DNNs) over physical nodes such as edge, fog, or cloud nodes, could enhance sensor fusion, and reduce bandwidth and inference lat…
All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey
Ashkan Yousefpour, Caleb Fung, Tam Nguyen +5
With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billion…
On Reducing IoT Service Delay via Fog Offloading
Ashkan Yousefpour, Genya Ishigaki, Riti Gour +1
With the Internet of Things (IoT) becoming a major component of our daily life, understanding how to improve the quality of service (QoS) for IoT applications through fog computing…
A Privacy Scheme for Monitoring Devices in the Internet of Things
Zygmunt J. Haas, Ashkan Yousefpour
Sufficiently strong security and privacy mechanisms are prerequisite to amass the promising benefits of the IoT technology and to incorporate this technology into our daily lives.…
Instant Accident Reporting and Crowdsensed Road Condition Analytics for Smart Cities
Ashkan Yousefpour, Caleb Fung, Tam Nguyen +2
The following report contains information about a proposed technology by the authors, which consists of a device that sits inside of a vehicle and constantly monitors the car infor…