3 citations · 3 across the 12 of their papers we have counts for
13 papers
Representation Transitions Reveal Emerging Safety Risks in Multi-Turn LLM Agents
Haoyu Wang, Wei Zhao, Yedi Zhang +2
Multi-turn attacks on agentic systems can compose individually permissible actions into harmful outcomes, challenging defenses that assess actions or states in isolation. We show t…
: Improving Agent Safety through Multi-Stage Defense
Zibo Xiao, Haoyu Wang, Jun Sun
Large Language Model (LLM) agents rely on multi-stage agentic workflows, with stages such as memory, planning, and tool execution, to accomplish complex tasks. However, risks may e…
Position: AI Safety Requires Effective Controllability
Yige Li, Yunhao Feng, Jun Sun
AI safety is still largely framed as alignment: training models to follow human preferences, safety policies, and normative constraints. That framing has improved the behavior of m…
SafeClaw-R: Towards Safe and Secure Multi-Agent Personal Assistants
Haoyu Wang, Zibo Xiao, Yedi Zhang +2
LLM-based multi-agent systems (MASs) are transforming personal productivity by autonomously executing complex, cross-platform tasks. Frameworks such as OpenClaw demonstrate the pot…
LLM-enabled Applications Require System-Level Threat Monitoring
Yedi Zhang, Haoyu Wang, Xianglin Yang +2
LLM-enabled applications are rapidly reshaping the software ecosystem by using large language models as core reasoning components for complex task execution. This paradigm shift, h…
ROMAN: Reward-Orchestrated Multi-Head Attention Network for Autonomous Driving System Testing
Jianlei Chi, Yuzhen Wu, Jiaxuan Hou +7
Automated Driving System (ADS) acts as the brain of autonomous vehicles, responsible for their safety and efficiency. Safe deployment requires thorough testing in diverse real-worl…
AIR: Improving Agent Safety through Incident Response
Zibo Xiao, Jun Sun, Junjie Chen
Large Language Model (LLM) agents are increasingly deployed in practice across a wide range of autonomous applications. Yet current safety mechanisms for LLM agents focus almost ex…
Developing a Strong CPS Defender: An Evolutionary Approach
Qingyuan Hu, Christopher M. Poskitt, Jun Sun +1
Cyber-physical systems (CPSs) are used extensively in critical infrastructure, underscoring the need for anomaly detection systems that are able to catch even the most motivated at…
Txt2Sce: Scenario Generation for Autonomous Driving System Testing Based on Textual Reports
Pin Ji, Yang Feng, Zongtai Li +4
With the rapid advancement of deep learning and related technologies, Autonomous Driving Systems (ADSs) have made significant progress and are gradually being widely applied in saf…
ProbGuard: Proactive Runtime Monitoring for LLM Agent Safety via Probabilistic Prediction
Haoyu Wang, Christopher M. Poskitt, Jiali Wei +1
Large Language Model (LLM) agents increasingly operate across domains such as robotics, virtual assistants, and web automation. However, their stochastic decision-making introduces…
AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agents
Haoyu Wang, Christopher M. Poskitt, Jun Sun
Agents built on LLMs are increasingly deployed across diverse domains, automating complex decision-making and task execution. However, their autonomy introduces safety risks, inclu…
FixDrive: Automatically Repairing Autonomous Vehicle Driving Behaviour for $0.08 per Violation
Yang Sun, Christopher M. Poskitt, Kun Wang +1
Autonomous Vehicles (AVs) are advancing rapidly, with Level-4 AVs already operating in real-world conditions. Current AVs, however, still lag behind human drivers in adaptability a…
Natural Adversaries: Fuzzing Autonomous Vehicles with Realistic Roadside Object Placements
Yang Sun, Haoyu Wang, Christopher M. Poskitt +1
The emergence of Autonomous Vehicles (AVs) has spurred research into testing the resilience of their perception systems, i.e., ensuring that they are not susceptible to critical mi…