Research map / Multi-agent systems and games / LLM agents
RiOSWorld: Benchmarking the Risk of Multimodal Computer-Use Agents
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Introduces RiOSWorld, a 492-task virtual-machine benchmark that measures safety risks (risk goal intention and completion) of multimodal computer-use agents across 13 environmental and user-originated risk categories, and benchmarks 10 MLLM agents.
Problem
Existing safety evaluations of MLLM-based agents either use QA-style or simplified environments or cover only one or two risk types, so they do not capture real-world computer-use risks.
Approach
An executable OSWorld-based virtual-machine benchmark with dynamic threats (phishing sites/emails, pop-ups, reCAPTCHA, fake login pages) and user-originated risky instructions, scored by rule-based risk-completion checks and a GPT-4o LLM-as-judge for risk intention.
Key results
- Average trajectory unsafe rate across 10 agents is 84.93% for risk goal intention and 59.64% for risk goal completion over 492 tasks.
- Phishing Web shows 99.2% average intention unsafe rate and 83.7% completion unsafe rate.
- Environmental risks average 89.12% intention and 60.29% completion; user-originated risks average 81.33% intention and 59.07% completion.
Surprising findings
- Agents often intend to take risky actions yet many risk goals are not completed, with 64.59% of intention-positive agents failing completion.
- Some agents complete risk goals without intending them, suggesting failures to prevent risk rather than deliberate misuse.
Limitations
- Judge relies on GPT-4o with scenario-specific prompts; no universal judge model is validated.
- Tasks are manually constructed and costly to scale (about 1440 man-hours).
- Some tasks have simplified risk-triggering conditions and dynamic risks are not always reproducible.
Experimental setup
Datasets: OSWorld task configurations
Benchmarks: RiOSWorld
Metrics: unsafe rate of trajectory (USR) for risk goal intention unsafe rate of trajectory (USR) for risk goal completion
Baselines: GPT-4o GPT-4o-mini GPT-4.1 Gemini-2.0-pro Gemini-2.5-pro Claude-3.5-Sonnet Claude-3.7-Sonnet Llama-3.2-90B-Vision-Instruct Qwen2-VL-72B-Instruct Qwen2.5-VL-72B-Instruct
Builds on: OSWorld
Closest papers by meaning
- Multimodal Situational Safety (ICLR 2025 · similarity 0.90)
- OpenAgentSafety: A Comprehensive Framework For Evaluating Real-World AI Agent Safety (ICLR 2026 · similarity 0.90)
- MLLMs Fail to Refuse when Using Tools Agentically (NeurIPS 2026 · similarity 0.89)
- LLaVAShield: Safeguarding Multimodal Multi-Turn Dialogues in Vision-Language Models (CVPR 2026 · similarity 0.88)
- OSWorld-MCP: Benchmarking MCP Tool Invocation In Computer-Use Agents (ICLR 2026 · similarity 0.88)
- IS-Bench: Evaluating Interactive Safety of VLM-Driven Embodied Agents in Daily Household Tasks (AAAI 2026 · similarity 0.87)
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