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cs.AI2026

HazardAuditor: From Executable Threats to Safer Computer-Use Agents

Yunhao Feng, Ruixiao Lin, Ming Wen +5

Computer-use agents increasingly interact with browsers, terminals, file systems, and external services, introducing safety risks that emerge through runtime behavior rather than g…

cs.AI2026

UI-Venus-2 Technical Report

Venus Team, Zhuohan Cai, Haoxing Chen +28

Multimodal GUI agents have emerged as a promising paradigm for digital task automation, yet transitioning from benchmark-oriented models to dependable real-world applications remai…

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

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

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

Xingjun Ma, Yixu Wang, Hengyuan Xu +18

The rapid evolution of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) has driven major gains in reasoning, perception, and generation across language and…