7 citations · 8 across the 6 of their papers we have counts for
9 papers
Text-Based Personas for Simulating User Privacy Decisions
Kassem Fawaz, Ren Yi, Octavian Suciu +4
The ability to simulate human privacy decisions has significant implications for aligning autonomous agents with individual intent and conducting cost-effective, large-scale privac…
Personalizing Agent Privacy Decisions via Logical Entailment
James Flemings, Ren Yi, Octavian Suciu +3
Personal large language model (LLM) agents increasingly perform tasks that require access to user data, raising concerns about appropriate data disclosure. We show that relying sol…
SNPeek: Side-Channel Analysis for Privacy Applications on Confidential VMs
Ruiyi Zhang, Albert Cheu, Adria Gascon +4
Confidential virtual machines (CVMs) based on trusted execution environments (TEEs) enable new privacy-preserving solutions. Yet, they leave side-channel leakage outside their thre…
Privacy Reasoning in Ambiguous Contexts
Ren Yi, Octavian Suciu, Adria Gascon +3
We study the ability of language models to reason about appropriate information disclosure - a central aspect of the evolving field of agentic privacy. Whereas previous works have…
Technical Report -- Expected Exploitability: Predicting the Development of Functional Vulnerability Exploits
Octavian Suciu, Connor Nelson, Zhuoer Lyu +2
Assessing the exploitability of software vulnerabilities at the time of disclosure is difficult and error-prone, as features extracted via technical analysis by existing metrics ar…
Exploring Adversarial Examples in Malware Detection
Octavian Suciu, Scott E. Coull, Jeffrey Johns
The convolutional neural network (CNN) architecture is increasingly being applied to new domains, such as malware detection, where it is able to learn malicious behavior from raw b…
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks
Ali Shafahi, W. Ronny Huang, Mahyar Najibi +4
Data poisoning is an attack on machine learning models wherein the attacker adds examples to the training set to manipulate the behavior of the model at test time. This paper explo…
Technical Report: When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attacks
Octavian Suciu, Radu Mărginean, Yiğitcan Kaya +2
Recent results suggest that attacks against supervised machine learning systems are quite effective, while defenses are easily bypassed by new attacks. However, the specifications…
Summoning Demons: The Pursuit of Exploitable Bugs in Machine Learning
Rock Stevens, Octavian Suciu, Andrew Ruef +3
Governments and businesses increasingly rely on data analytics and machine learning (ML) for improving their competitive edge in areas such as consumer satisfaction, threat intelli…