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cs.CR2025

Can LLM Infer Risk Information From MCP Server System Logs?

Jiayi Fu, Yuansen Zhang, Yinggui Wang

Large Language Models (LLMs) demonstrate strong capabilities in solving complex tasks when integrated with external tools. The Model Context Protocol (MCP) has become a standard in…

cs.CR2025

CEE: An Inference-Time Jailbreak Defense for Embodied Intelligence via Subspace Concept Rotation

Jirui Yang, Zheyu Lin, Zhihui Lu +6

Large language models (LLMs) are widely used for task understanding and action planning in embodied intelligence (EI) systems, but their adoption substantially increases vulnerabil…

cs.CR2024

Ditto: Quantization-aware Secure Inference of Transformers upon MPC

Haoqi Wu, Wenjing Fang, Yancheng Zheng +4

Due to the rising privacy concerns on sensitive client data and trained models like Transformers, secure multi-party computation (MPC) techniques are employed to enable secure infe…

cs.CR2023

StegGuard: Fingerprinting Self-supervised Pre-trained Encoders via Secrets Embeder and Extractor

Xingdong Ren, Tianxing Zhang, Hanzhou Wu +3

In this work, we propose StegGuard, a novel fingerprinting mechanism to verify the ownership of the suspect pre-trained encoder using steganography. A critical perspective in StegG…

cs.CR2023

You Can Backdoor Personalized Federated Learning

Tiandi Ye, Cen Chen, Yinggui Wang +2

Existing research primarily focuses on backdoor attacks and defenses within the generic federated learning scenario, where all clients collaborate to train a single global model. A…