1 citations · 1 across the 16 of their papers we have counts for
17 papers
SaFeR-Steer: Evolving Multi-Turn MLLMs via Synthetic Bootstrapping and Feedback Dynamics
Haolong Hu, Hanyu Li, Tiancheng He +6
MLLMs are increasingly deployed in multi-turn settings, where attackers can escalate unsafe intent through the evolving visual-text history and exploit long-context safety decay. Y…
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…
CIA: Inferring the Communication Topology from LLM-based Multi-Agent Systems
Yongxuan Wu, Xixun Lin, He Zhang +5
LLM-based Multi-Agent Systems (MAS) have demonstrated remarkable capabilities in solving complex tasks. Central to MAS is the communication topology which governs how agents exchan…
AlphaEval: Evaluating Agents in Production
Pengrui Lu, Bingyu Xu, Wenjun Zhang +24
The rapid deployment of AI agents in commercial settings has outpaced the development of evaluation methodologies that reflect production realities. Existing benchmarks measure age…
LARFT: Closing the Cognition-Action Gap for Length Instruction Following in Large Language Models
Wei Zhang, Lintong Du, Yuanhe Zhang +4
Despite the strong performance of Large Language Models (LLMs) on complex instruction-following tasks, precise control of output length remains a persistent challenge. Existing met…
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…
AI Deception: Risks, Dynamics, and Controls
Boyuan Chen, Sitong Fang, Jiaming Ji +56
As intelligence increases, so does its shadow. AI deception, in which systems induce false beliefs to secure self-beneficial outcomes, has evolved from a speculative concern to an…
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…
Hidden in the Noise: Unveiling Backdoors in Audio LLMs Alignment through Latent Acoustic Pattern Triggers
Liang Lin, Miao Yu, Kaiwen Luo +9
As Audio Large Language Models (ALLMs) emerge as powerful tools for speech processing, their safety implications demand urgent attention. While considerable research has explored t…
Jailbreaking Large Language Diffusion Models: Revealing Hidden Safety Flaws in Diffusion-Based Text Generation
Yuanhe Zhang, Fangzhou Xie, Zhenhong Zhou +4
Large Language Diffusion Models (LLDMs) exhibit comparable performance to LLMs while offering distinct advantages in inference speed and mathematical reasoning tasks.The precise an…
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 Survey on the Safety and Security Threats of Computer-Using Agents: JARVIS or Ultron?
Ada Chen, Yongjiang Wu, Junyuan Zhang +6
Recently, AI-driven interactions with computing devices have advanced from basic prototype tools to sophisticated, LLM-based systems that emulate human-like operations in graphical…
A Vision for Auto Research with LLM Agents
Chengwei Liu, Chong Wang, Jiayue Cao +16
This paper introduces Agent-Based Auto Research, a structured multi-agent framework designed to automate, coordinate, and optimize the full lifecycle of scientific research. Levera…
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…
CORBA: Contagious Recursive Blocking Attacks on Multi-Agent Systems Based on Large Language Models
Zhenhong Zhou, Zherui Li, Jie Zhang +4
Large Language Model-based Multi-Agent Systems (LLM-MASs) have demonstrated remarkable real-world capabilities, effectively collaborating to complete complex tasks. While these sys…
DemonAgent: Dynamically Encrypted Multi-Backdoor Implantation Attack on LLM-based Agent
Pengyu Zhu, Zhenhong Zhou, Yuanhe Zhang +3
As LLM-based agents become increasingly prevalent, backdoors can be implanted into agents through user queries or environment feedback, raising critical concerns regarding safety v…