2 citations · 2 across the 11 of their papers we have counts for
14 papers
LoRango: It Takes Two LoRAs to Unlock Hidden Behaviors in Diffusion Models
Jin Wei, Rundong Li, Ruihao Yang +10
Users commonly combine multiple Low-Rank Adaptation (LoRA) adapters to personalize images with different subjects, styles, and visual attributes. Yet inspecting adapters individual…
Detecting and Localizing Segment-Level Poisoning in Multi-Source LLM-Agent Inputs
Xue Tan, Changhui Wang, Sanrui Yang +6
Modern large language model (LLM) agents often construct prompts by aggregating retrieved passages, user reviews, and documents from multiple external sources. This paradigm expose…
ViTeGate: Visual-Textual Triggered Knowledge Poisoning for Vision-Language Retrieval-Augmented Generation
Xue Tan, Xuandi Zeng, Yu Shao +5
Modern Vision-Language Retrieval-Augmented Generation (VLRAG) systems augment Large Vision-Language Models (LVLMs) with retrieved visual and textual evidence, enabling responses gr…
PixCrypt: Fast Fine-Grained FHE with Range-Aware Caching
Chao Wang, Shubing Yang, Xiaoyan Sun +3
Many analytics tasks require secure computation over encrypted data. In particular, fine-grained data such as pixel-level images require higher precision, as every pixel can direct…
A Trustworthy Watermarking Framework for LLM-Generated Food Safety Content
Zhongli Fang, Yiran Chen, Lingyun Zhang +4
Large language models are transforming many industries with their text generation abilities. However, their outputs can be easily tampered with, creating serious risks in critical…
Measuring the Wrong Thing: Internal Harmfulness Scores Anti-Rank Successful Jailbreaks
Mingyu Luo, Ming Deng, Zilang Qiu +8
Internal safety scores judge a prompt before any text is generated, and they are validated by how well they separate harmful prompts from benign ones. That separation is then read…
When Agents Go Rogue: Activation-Based Detection of Malicious Behaviors in Multi-Agent Systems
Haowen Xu, Xue Tan, Lei Ma +6
While enabling effective collaboration on complex tasks, LLM-based Multi-Agent Systems (MAS) face critical security challenges due to vulnerabilities at the agent and interaction l…
PRA-RAG: Provably Robust Aggregation in Retrieval-Augmented Generation against Retrieval Corruption
Xue Tan, Yi Zheng, Chang Huo +7
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by incorporating external knowledge, effectively mitigating their inherent knowledge limitations. However…
Generalizable and Interpretable RF Fingerprinting with Shapelet-Enhanced Large Language Models
Tianya Zhao, Junqing Zhang, Haowen Xu +3
Deep neural networks (DNNs) have achieved remarkable success in radio frequency (RF) fingerprinting for wireless device authentication. However, their practical deployment faces tw…
EMPalm: Exfiltrating Palm Biometric Data via Electromagnetic Side-Channel
Haowen Xu, Tianya Zhao, Xuyu Wang +4
Palm recognition has emerged as a dominant biometric authentication technology in critical infrastructure. These systems operate in either single-modal form, using palmprint or pal…
RevPRAG: Revealing Poisoning Attacks in Retrieval-Augmented Generation through LLM Activation Analysis
Xue Tan, Hao Luan, Mingyu Luo +3
Retrieval-Augmented Generation (RAG) enriches the input to LLMs by retrieving information from the relevant knowledge database, enabling them to produce responses that are more acc…
Enabling Practical and Privacy-Preserving Image Processing
Chao Wang, Shubing Yang, Xiaoyan Sun +2
Fully Homomorphic Encryption (FHE) enables computations on encrypted data, preserving confidentiality without the need for decryption. However, FHE is often hindered by significant…
HuntFUZZ: Enhancing Error Handling Testing through Clustering Based Fuzzing
Jin Wei, Ping Chen, Jun Dai +4
Testing a program's capability to effectively handling errors is a significant challenge, given that program errors are relatively uncommon. To solve this, Software Fault Injection…
SQLaser: Detecting DBMS Logic Bugs with Clause-Guided Fuzzing
Jin Wei, Ping Chen, Kangjie Lu +2
Database Management Systems (DBMSs) are vital components in modern data-driven systems. Their complexity often leads to logic bugs, which are implementation errors within the DBMSs…