activity
20222026
most citedAuditing Membership Leakages of Multi-Exit Networks

1 citations · 2 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CR2026

Capability-Routed Guard: Defending Large Reasoning Models Against Reasoning-Centric Jailbreaks

Yiyong Liu, Yixin Wu, Jun Sakuma

Large reasoning models (LRMs) expose a new safety failure mode: adversarial prompts can manipulate reasoning context, task decomposition, or capability interpretation so that harmf…

cs.CR2026

Trust Me, Import This: Dependency Steering Attacks via Malicious Agent Skills

Yiyong Liu, Chia-Yi Hsu, Chun-Ying Huang +3

LLM-powered coding agents increasingly make software supply chain decisions. They generate imports, recommend packages, and write installation commands. Prior work showed that thes…

cs.CR2025

SoK: Data Reconstruction Attacks Against Machine Learning Models: Definition, Metrics, and Benchmark

Rui Wen, Yiyong Liu, Michael Backes +1

Data reconstruction attacks, which aim to recover the training dataset of a target model with limited access, have gained increasing attention in recent years. However, there is cu…

cs.CR2024

Efficient Data-Free Model Stealing with Label Diversity

Yiyong Liu, Rui Wen, Michael Backes +1

Machine learning as a Service (MLaaS) allows users to query the machine learning model in an API manner, which provides an opportunity for users to enjoy the benefits brought by th…

cs.CR2023★ 1 cited

Transferable Availability Poisoning Attacks

Yiyong Liu, Michael Backes, Xiao Zhang

We consider availability data poisoning attacks, where an adversary aims to degrade the overall test accuracy of a machine learning model by crafting small perturbations to its tra…

cs.CR2022

Membership Inference Attacks by Exploiting Loss Trajectory

Yiyong Liu, Zhengyu Zhao, Michael Backes +1

Machine learning models are vulnerable to membership inference attacks in which an adversary aims to predict whether or not a particular sample was contained in the target model's…

cs.CR2022★ 1 cited

Auditing Membership Leakages of Multi-Exit Networks

Zheng Li, Yiyong Liu, Xinlei He +3

Relying on the fact that not all inputs require the same amount of computation to yield a confident prediction, multi-exit networks are gaining attention as a prominent approach fo…