1 citations · 2 across the 11 of their papers we have counts for
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A Finger on the Scale: Covert Policy Steering through Agentic Skills
Jiarui Li, Jiahao Chen, Chunyi Zhou +5
Reusable agent skills extend large language model (LLM) agents with task procedures, tool-use guidance, and output constraints. Yet these skills also act as externalized behavioral…
The Shape of Ownership: Verifying LLM Provenance through Semantic Structures
Zhongrui Sun, Jiahao Chen, Oubo Ma +4
As large language models (LLMs) are increasingly redistributed, adapted, and served behind opaque APIs, model ownership can no longer be established reliably by inspecting model in…
Lilith: Backdoor Generalization under Training-Inference Trigger Shift
Zhou Feng, Jiahao Chen, Chunyi Zhou +6
Machine-learning services increasingly rely on public data, third-party providers, and outsourced training, creating opportunities for data-poisoning attacks that implant persisten…
Unveiling the Security Risks of Federated Learning in the Wild: From Research to Practice
Jiahao Chen, Zhiming Zhao, Yuwen Pu +4
Federated learning (FL) has attracted substantial attention in both academia and industry, yet its practical security posture remains poorly understood. In particular, a large body…
Auditing M-LLMs for Privacy Risks: A Synthetic Benchmark and Evaluation Framework
Junhao Li, Jiahao Chen, Zhou Feng +1
Recent advances in multi-modal Large Language Models (M-LLMs) have demonstrated a powerful ability to synthesize implicit information from disparate sources, including images and t…
FreeTalk:A plug-and-play and black-box defense against speech synthesis attacks
Yuwen Pu, Zhou Feng, Chunyi Zhou +4
Recently, speech assistant and speech verification have been used in many fields, which brings much benefit and convenience for us. However, when we enjoy these speech applications…
Poison in the Well: Feature Embedding Disruption in Backdoor Attacks
Zhou Feng, Jiahao Chen, Chunyi Zhou +3
Backdoor attacks embed malicious triggers into training data, enabling attackers to manipulate neural network behavior during inference while maintaining high accuracy on benign in…
Enhancing Adversarial Transferability with Adversarial Weight Tuning
Jiahao Chen, Zhou Feng, Rui Zeng +6
Deep neural networks (DNNs) are vulnerable to adversarial examples (AEs) that mislead the model while appearing benign to human observers. A critical concern is the transferability…
Mellivora Capensis: A Backdoor-Free Training Framework on the Poisoned Dataset without Auxiliary Data
Yuwen Pu, Jiahao Chen, Chunyi Zhou +4
The efficacy of deep learning models is profoundly influenced by the quality of their training data. Given the considerations of data diversity, data scale, and annotation expenses…