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cs.AI2026

AgentDoG 1.5: A Lightweight and Scalable Alignment Framework for AI Agent Safety and Security

Dongrui Liu, Yu Li, Zhonghao Yang +47

Modern open-world agents such as OpenClaw exhibit powerful cross-environment execution capabilities yet introduce broad new safety risk sources. Meanwhile, advanced frontier AI mod…

cs.AI2026

Steering the Verifiability of Multimodal AI Hallucinations

Jianhong Pang, Ruoxi Cheng, Ziyi Ye +4

AI applications driven by multimodal large language models (MLLMs) are prone to hallucinations and pose considerable risks to human users. Crucially, such hallucinations are not eq…

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

What Does a Sharing Question Add? Auditing LLM Survey Scores for Misinformation

Zonghuan Xu, Xiang Zheng, Yutao Wu +1

Evaluating misinformation requires distinguishing whether readers believe content from whether they would share it. Asking large language models (LLMs) both questions yields two sc…

cs.AI2026

Attention in Space: Functional Roles of VLM Heads for Spatial Reasoning

Xueqi Ma, Shuo Yang, Yanbei Jiang +6

Despite remarkable advances in large Vision-Language Models (VLMs), spatial reasoning remains a persistent challenge. In this work, we investigate how attention heads within VLMs c…

cs.AI2026

OOD-MMSafe: Advancing MLLM Safety from Harmful Intent to Hidden Consequences

Ming Wen, Kun Yang, Jingyu Zhang +4

While safety alignment for Multimodal Large Language Models (MLLMs) has gained significant attention, current paradigms primarily target malicious intent or situational violations.…

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…

cs.AI2026

Just Ask: Curious Code Agents Reveal System Prompts in Frontier LLMs

Xiang Zheng, Yutao Wu, Hanxun Huang +5

Autonomous code agents built on large language models are reshaping software and AI development through tool use, long-horizon reasoning, and self-directed interaction. However, th…