Shouling Ji

FedGraph: Defending Federated Large Language Model Fine-Tuning Against Backdoor Attacks via Graph-Based Aggregation

Xi Chen, Chunyi Zhou, Rui Zeng +3

Federated fine-tuning of large language models (LLMs) allows multiple clients to collaboratively adapt models without sharing raw data, offering strong privacy benefits. However, t…

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…

Flipping Minds by Flipping Bits: Decision-Level Cognitive Hijacking of Large Language Models

Yu Yan, Jiahao Chen, Siqi Lu +6

Large Language Models (LLMs) have been widely applied in high-stakes decision-making scenarios such as corporate strategy, and users are increasingly relying on their outputs. Howe…

Safety in Self-Evolving LLM Agent Systems: Threats, Amplification, and Case Studies

Ruixiao Lin, Xinhao Deng, Qingming Li +12

Self-evolving LLM agent systems, which autonomously update their model parameters, memory, tools, and architectures, introduce a qualitatively new threat landscape in which adversa…

Understanding and Mitigating Prompt Leaking Attacks in Real-World LLM-Based Applications

Yong Yang, Chong Fu, Tong Zhang +6

Large language model (LLM)-based applications rely on system prompts to encode core logic and developer-defined constraints, making these prompts important intellectual property. H…

Customization under Fire: Plugin Poisoning in Text-to-Image Ecosystem

Jiahao Chen, Xing He, Yong Yang +6

The prosperity of text-to-image (T2I) models has fostered a vibrant share-and-play ecosystem centered on Low-Rank Adaptation (LoRA) plugins, which allow users to customize and shar…

Profiling for Pennies: Unveiling the Privacy Iceberg of LLM Agents

Jiahao Chen, Qi Zhang, Ruixiao Lin +7

Large Language Models (LLMs) have revolutionized how information are collected, aggregated, and reasoned. However, this enables a novel and accessible vector of privacy intrusion:…

IntraGuard: Committee-Side Defenses Against Review Outsourcing to Commercial Chatbots

Oubo Ma, Ruixiao Lin, Jiahao Chen +3

LLMs become increasingly capable, editorial boards and program committees are growing concerned about reviewers who fully outsource peer review to commercial chatbots. This concern…

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…

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…

IPIGuard: A Novel Tool Dependency Graph-Based Defense Against Indirect Prompt Injection in LLM Agents

Hengyu An, Jinghuai Zhang, Tianyu Du +4

Large language model (LLM) agents are widely deployed in real-world applications, where they leverage tools to retrieve and manipulate external data for complex tasks. However, whe…

LoRAShield: Data-Free Editing Alignment for Secure Personalized LoRA Sharing

Jiahao Chen, Junhao Li, Yiming Wang +6

The proliferation of Low-Rank Adaptation (LoRA) models has democratized personalized text-to-image generation, enabling users to share lightweight models (e.g., personal portraits)…

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…

"No Matter What You Do": Purifying GNN Models via Backdoor Unlearning

Jiale Zhang, Chengcheng Zhu, Bosen Rao +5

Recent studies have exposed that GNNs are vulnerable to several adversarial attacks, among which backdoor attack is one of the toughest. Similar to Deep Neural Networks (DNNs), bac…

CAMH: Advancing Model Hijacking Attack in Machine Learning

Xing He, Jiahao Chen, Yuwen Pu +5

In the burgeoning domain of machine learning, the reliance on third-party services for model training and the adoption of pre-trained models have surged. However, this reliance int…

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…

Rethinking the Vulnerabilities of Face Recognition Systems:From a Practical Perspective

Jiahao Chen, Zhiqiang Shen, Yuwen Pu +5

Face Recognition Systems (FRS) have increasingly integrated into critical applications, including surveillance and user authentication, highlighting their pivotal role in modern se…