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cs.CR2026

Label-Budgeted Attribute Inference from Protected Features in Edge-Cloud Split Multimodal LLMs

Junguang Yao, Peichun Hua, Hanxiu Zhang +1

Edge-cloud split inference runs the early layers of a multimodal large language model (MLLM) on a device and sends intermediate features to a server to complete the task. While kee…

cs.CR2026

Backdoor in the Loop: Compromising Agentic Search via Malicious Retrievers

Beining Xu, Peichun Hua, Yunming Xiao

Agentic retrieval-augmented generation (RAG) interleaves reasoning with repeated retrieval, giving the retriever influence over both the evidence an agent observes and its subseque…

cs.CR2026

Spruce: Scalable Private Outsourced Retrieval Using Compact Embeddings

Peichun Hua, Yunming Xiao

Retrieval-Augmented Generation (RAG) has made dense retrieval over large document collections a standard building block. Organizations increasingly outsource vector indexes to untr…

cs.CR2026

Pointing the Way, Hiding the Destination: Practical Private Dense Retrieval at Scale

Peichun Hua, Danyang Chen, Junan Zhang +5

Hosted retrieval-augmented generation (RAG) and semantic search allow users to query valuable provider-held corpora, raising two competing demands: to hide each query and chosen re…

cs.CR2026

Behavioral Skill Reconstruction: Reconstructing Hidden Functionality from LLM Agent Skills

Peichun Hua, Haoxuan Xu, Mengyuan Li

Closed source agent skills may encode proprietary instructions, scripts, constants, and data. Providers may offer their capabilities as services while keeping the underlying packag…

cs.CR2026

A Unified Open-Set Framework for Scalable PUF-Based Authentication of Heterogeneous IoT Devices

Xin Wang, Peichun Hua, Chip Hong Chang +2

As modern cyber systems scale to include large populations of heterogeneous IoT devices, securing them against impersonation and forgery is a critical cybersecurity challenge. Phys…

cs.CR2025

Rethinking Jailbreak Detection of Large Vision Language Models with Representational Contrastive Scoring

Peichun Hua, Hao Li, Shanghao Shi +2

Large Vision-Language Models (LVLMs) are vulnerable to a growing array of multimodal jailbreak attacks, necessitating defenses that are both generalizable to novel threats and effi…