12 papers
: 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…
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…
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…
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…
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…