title: RiOSWorld: Benchmarking the Risk of Multimodal Compter-Use Agents
summary: With the rapid development of multimodal large language models (MLLMs), they
  are increasingly deployed as autonomous computer-use agents capable of
  accomplishing complex computer tasks. However, a pressing issue arises: Can the
  safety risk principles designed and aligned for general MLLMs in dialogue
  scenarios be effectively transferred to real-world computer-use scenarios?
  Existing research on evaluating the safety risks of MLLM-based computer-use
  agents suffers from several limitations: it either lacks realistic interactive
  environments, or narrowly focuses on one or a few specific risk types. These
  limitations ignore the complexity, variability, and diversity of real-world
  environments, thereby restricting comprehensive risk evaluation for
  computer-use agents. To this end, we introduce RiOSWorld, a benchmark
  designed to evaluate the potential risks of MLLM-based agents during real-world
  computer manipulations. Our benchmark includes 492 risky tasks spanning various
  computer applications, involving web, social media, multimedia, os, email, and
  office software. We categorize these risks into two major classes based on
  their risk source: (i) User-originated risks and (ii) Environmental risks. For
  the evaluation, we evaluate safety risks from two perspectives: (i) Risk goal
  intention and (ii) Risk goal completion. Extensive experiments with multimodal
  agents on RiOSWorld demonstrate that current computer-use agents
  confront significant safety risks in real-world scenarios. Our findings
  highlight the necessity and urgency of safety alignment for computer-use agents
  in real-world computer manipulation, providing valuable insights for developing
  trustworthy computer-use agents. Our benchmark is publicly available at
  https://yjyddq.github.io/RiOSWorld.github.io/.
paper_authors._id: 6840035d7ed0da422d1fda21
paper_authors.name: Jingyi Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-04T08:53:32.008Z
paper_authors._id: 64f73a44102fbfb26410962e
paper_authors.avatarUrl: /avatars/328302a495de6a4418be835456d1d3c6.svg
paper_authors.fullname: jingyi Yang
paper_authors.type: user
paper_authors.user: JY-Young
paper_authors._id: 6840035d7ed0da422d1fda22
paper_authors.name: Shuai Shao
paper_authors._id: 6840035d7ed0da422d1fda23
paper_authors.name: Dongrui Liu
paper_authors._id: 6840035d7ed0da422d1fda24
paper_authors.name: Jing Shao
paper_title: RiOSWorld: Benchmarking the Risk of Multimodal Compter-Use Agents
paper_summary: With the rapid development of multimodal large language models (MLLMs), they
  are increasingly deployed as autonomous computer-use agents capable of
  accomplishing complex computer tasks. However, a pressing issue arises: Can the
  safety risk principles designed and aligned for general MLLMs in dialogue
  scenarios be effectively transferred to real-world computer-use scenarios?
  Existing research on evaluating the safety risks of MLLM-based computer-use
  agents suffers from several limitations: it either lacks realistic interactive
  environments, or narrowly focuses on one or a few specific risk types. These
  limitations ignore the complexity, variability, and diversity of real-world
  environments, thereby restricting comprehensive risk evaluation for
  computer-use agents. To this end, we introduce RiOSWorld, a benchmark
  designed to evaluate the potential risks of MLLM-based agents during real-world
  computer manipulations. Our benchmark includes 492 risky tasks spanning various
  computer applications, involving web, social media, multimedia, os, email, and
  office software. We categorize these risks into two major classes based on
  their risk source: (i) User-originated risks and (ii) Environmental risks. For
  the evaluation, we evaluate safety risks from two perspectives: (i) Risk goal
  intention and (ii) Risk goal completion. Extensive experiments with multimodal
  agents on RiOSWorld demonstrate that current computer-use agents
  confront significant safety risks in real-world scenarios. Our findings
  highlight the necessity and urgency of safety alignment for computer-use agents
  in real-world computer manipulation, providing valuable insights for developing
  trustworthy computer-use agents. Our benchmark is publicly available at
  https://yjyddq.github.io/RiOSWorld.github.io/.
paper_ai_summary: RIOSWorld is a benchmark for evaluating safety risks of multimodal large language models in real-world computer tasks, revealing significant risks that necessitate safety alignment.
paper_ai_keywords: multimodal large language models autonomous computer-use agents safety risk principles RiOSWorld risky tasks computer applications user-originated risks environmental risks risk goal intention risk goal completion