1 citations · 1 across the 16 of their papers we have counts for
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Hijacking Large Audio-Language Models via Context-Agnostic and Imperceptible Auditory Prompt Injection
Meng Chen, Kun Wang, Li Lu +2
Modern Large audio-language models (LALMs) power intelligent voice interactions by tightly integrating audio and text. This integration, however, expands the attack surface beyond…
MCPShield: A Security Cognition Layer for Adaptive Trust Calibration in Model Context Protocol Agents
Zhenhong Zhou, Yuanhe Zhang, Hongwei Cai +6
The Model Context Protocol (MCP) standardizes tool use for LLM-based agents and enable third-party servers. This openness introduces a security misalignment: agents implicitly trus…
Omni-Safety under Cross-Modality Conflict: Vulnerabilities, Dynamics Mechanisms and Efficient Alignment
Kun Wang, Zherui Li, Zhenhong Zhou +8
Omni-modal Large Language Models (OLLMs) greatly expand LLMs' multimodal capabilities but also introduce cross-modal safety risks. However, a systematic understanding of vulnerabil…
LeechHijack: Covert Computational Resource Exploitation in Intelligent Agent Systems
Yuanhe Zhang, Weiliu Wang, Zhenhong Zhou +5
Large Language Model (LLM)-based agents have demonstrated remarkable capabilities in reasoning, planning, and tool usage. The recently proposed Model Context Protocol (MCP) has eme…
Resource Consumption Red-Teaming for Large Vision-Language Models
Haoran Gao, Yuanhe Zhang, Zhenhong Zhou +7
Resource Consumption Attacks (RCAs) have emerged as a significant threat to the deployment of Large Language Models (LLMs). With the integration of vision modalities, additional at…
A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment
Kun Wang, Guibin Zhang, Zhenhong Zhou +100
The remarkable success of Large Language Models (LLMs) has illuminated a promising pathway toward achieving Artificial General Intelligence for both academic and industrial communi…