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cs.AI2026

ShadowFPT: Backdooring Federated Prompt Tuning via Shadow Triggers

Kun Zhai, Teng Li, Yunhao Feng +1

Federated Prompt Tuning (FPT) adapts large vision--language models by freezing the pretrained backbone and optimizing only lightweight prompt parameters across clients. Although th…

cs.AI2026

SkillTrace: Multi-Trace Provenance Auditing for LLM-Agent Skill Reuse

Jialuo Chen, Minghe Wang, Lingqi Jiang +7

LLM-agent ecosystems are rapidly growing around reusable skills: mixed-modality packages of metadata, natural-language instructions, code, tools, references, and operational workfl…

cs.AI2026

Safety Testing LLM Agents at Scale: From Risk Discovery to Evidence-Grounded Verification

Yunhao Feng, Ruixiao Lin, Ming Wen +12

LLM agents increasingly perform autonomous actions through external tools, leading to complex and evolving safety risks. However, existing safety testing targets expert-designed sa…

cs.AI2026

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…

cs.AI2026

AgentHazard: A Benchmark for Evaluating Harmful Behavior in Computer-Use Agents

Yunhao Feng, Yifan Ding, Yingshui Tan +6

Computer-use agents extend language models from text generation to persistent action over tools, files, and execution environments. Unlike chat systems, they maintain state across…

cs.AI2026

BackdoorAgent: A Unified Framework for Backdoor Attacks on LLM-based Agents

Yunhao Feng, Yige Li, Yutao Wu +6

Large language model (LLM) agents execute tasks through multi-step workflows that combine planning, memory, and tool use. While this design enables autonomy, it also expands the at…