23 citations · 34 across the 12 of their papers we have counts for
13 papers
AutoResearch AI: Towards AI-Powered Research Automation for Scientific Discovery
Guiyao Tie, Jiawen Shi, Dingjie Song +20
Scientific research is being reshaped by AI systems that move beyond isolated assistance toward longer-horizon workflows spanning literature grounding, hypothesis generation, exper…
BadSkill: Backdoor Attacks on Agent Skills via Model-in-Skill Poisoning
Guiyao Tie, Jiawen Shi, Pan Zhou +1
Agent ecosystems increasingly rely on installable skills to extend functionality, and some skills bundle learned model artifacts as part of their execution logic. This creates a su…
Merge Hijacking: Backdoor Attacks to Model Merging of Large Language Models
Zenghui Yuan, Yangming Xu, Jiawen Shi +2
Model merging for Large Language Models (LLMs) directly fuses the parameters of different models finetuned on various tasks, creating a unified model for multi-domain tasks. Howeve…
SafeAgent: Safeguarding LLM Agents via an Automated Risk Simulator
Xueyang Zhou, Weidong Wang, Lin Lu +7
Large Language Model (LLM)-based agents are increasingly deployed in real-world applications such as "digital assistants, autonomous customer service, and decision-support systems"…
Prompt Injection Attack to Tool Selection in LLM Agents
Jiawen Shi, Zenghui Yuan, Guiyao Tie +3
Tool selection is a key component of LLM agents. A popular approach follows a two-step process - \emph{retrieval} and \emph{selection} - to pick the most appropriate tool from a to…
BadToken: Token-level Backdoor Attacks to Multi-modal Large Language Models
Zenghui Yuan, Jiawen Shi, Pan Zhou +2
Multi-modal large language models (MLLMs) extend large language models (LLMs) to process multi-modal information, enabling them to generate responses to image-text inputs. MLLMs ha…
Poisoned-MRAG: Knowledge Poisoning Attacks to Multimodal Retrieval Augmented Generation
Yinuo Liu, Zenghui Yuan, Guiyao Tie +4
Multimodal retrieval-augmented generation (RAG) enhances the visual reasoning capability of vision-language models (VLMs) by dynamically accessing information from external knowled…
On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective
Yue Huang, Chujie Gao, Siyuan Wu +63
Generative Foundation Models (GenFMs) have emerged as transformative tools. However, their widespread adoption raises critical concerns regarding trustworthiness across dimensions.…
Self-Cognition in Large Language Models: An Exploratory Study
Dongping Chen, Jiawen Shi, Yao Wan +3
While Large Language Models (LLMs) have achieved remarkable success across various applications, they also raise concerns regarding self-cognition. In this paper, we perform a pion…
AutoJailbreak: Exploring Jailbreak Attacks and Defenses through a Dependency Lens
Lin Lu, Hai Yan, Zenghui Yuan +4
Jailbreak attacks in large language models (LLMs) entail inducing the models to generate content that breaches ethical and legal norm through the use of malicious prompts, posing a…
Optimization-based Prompt Injection Attack to LLM-as-a-Judge
Jiawen Shi, Zenghui Yuan, Yinuo Liu +4
LLM-as-a-Judge uses a large language model (LLM) to select the best response from a set of candidates for a given question. LLM-as-a-Judge has many applications such as LLM-powered…
MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use
Yue Huang, Jiawen Shi, Yuan Li +8
Large language models (LLMs) have garnered significant attention due to their impressive natural language processing (NLP) capabilities. Recently, many studies have focused on the…
BadGPT: Exploring Security Vulnerabilities of ChatGPT via Backdoor Attacks to InstructGPT
Jiawen Shi, Yixin Liu, Pan Zhou +1
Recently, ChatGPT has gained significant attention in research due to its ability to interact with humans effectively. The core idea behind this model is reinforcement learning (RL…