title: SafeArena: Evaluating the Safety of Autonomous Web Agents
summary: LLM-based agents are becoming increasingly proficient at solving web-based
  tasks. With this capability comes a greater risk of misuse for malicious
  purposes, such as posting misinformation in an online forum or selling illicit
  substances on a website. To evaluate these risks, we propose SafeArena, the
  first benchmark to focus on the deliberate misuse of web agents. SafeArena
  comprises 250 safe and 250 harmful tasks across four websites. We classify the
  harmful tasks into five harm categories -- misinformation, illegal activity,
  harassment, cybercrime, and social bias, designed to assess realistic misuses
  of web agents. We evaluate leading LLM-based web agents, including GPT-4o,
  Claude-3.5 Sonnet, Qwen-2-VL 72B, and Llama-3.2 90B, on our benchmark. To
  systematically assess their susceptibility to harmful tasks, we introduce the
  Agent Risk Assessment framework that categorizes agent behavior across four
  risk levels. We find agents are surprisingly compliant with malicious requests,
  with GPT-4o and Qwen-2 completing 34.7% and 27.3% of harmful requests,
  respectively. Our findings highlight the urgent need for safety alignment
  procedures for web agents. Our benchmark is available here:
  https://safearena.github.io
paper_authors._id: 67cf1071c6e7e15557f3814b
paper_authors.name: Ada Defne Tur
paper_authors._id: 67cf1071c6e7e15557f3814c
paper_authors.name: Nicholas Meade
paper_authors._id: 67cf1071c6e7e15557f3814d
paper_authors.name: Xing Han Lù
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-11T08:23:09.831Z
paper_authors._id: 5fa9ff3ea13e063b8b2b60cb
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1633380224986-5fa9ff3ea13e063b8b2b60cb.jpeg
paper_authors.fullname: Xing Han Lu
paper_authors.type: user
paper_authors.user: xhluca
paper_authors._id: 67cf1071c6e7e15557f3814e
paper_authors.name: Alejandra Zambrano
paper_authors._id: 67cf1071c6e7e15557f3814f
paper_authors.name: Arkil Patel
paper_authors._id: 67cf1071c6e7e15557f38150
paper_authors.name: Esin Durmus
paper_authors._id: 67cf1071c6e7e15557f38151
paper_authors.name: Spandana Gella
paper_authors._id: 67cf1071c6e7e15557f38152
paper_authors.name: Karolina Stańczak
paper_authors._id: 67cf1071c6e7e15557f38153
paper_authors.name: Siva Reddy
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-03-10T16:16:51.311Z
paper_authors._id: 624734dc4c731bb6bfab8af7
paper_authors.avatarUrl: /avatars/6b250b58710a3287b85e4733c1824558.svg
paper_authors.fullname: Siva Reddy
paper_authors.type: user
paper_authors.user: sivareddyg
paper_title: SafeArena: Evaluating the Safety of Autonomous Web Agents
paper_summary: LLM-based agents are becoming increasingly proficient at solving web-based
  tasks. With this capability comes a greater risk of misuse for malicious
  purposes, such as posting misinformation in an online forum or selling illicit
  substances on a website. To evaluate these risks, we propose SafeArena, the
  first benchmark to focus on the deliberate misuse of web agents. SafeArena
  comprises 250 safe and 250 harmful tasks across four websites. We classify the
  harmful tasks into five harm categories -- misinformation, illegal activity,
  harassment, cybercrime, and social bias, designed to assess realistic misuses
  of web agents. We evaluate leading LLM-based web agents, including GPT-4o,
  Claude-3.5 Sonnet, Qwen-2-VL 72B, and Llama-3.2 90B, on our benchmark. To
  systematically assess their susceptibility to harmful tasks, we introduce the
  Agent Risk Assessment framework that categorizes agent behavior across four
  risk levels. We find agents are surprisingly compliant with malicious requests,
  with GPT-4o and Qwen-2 completing 34.7% and 27.3% of harmful requests,
  respectively. Our findings highlight the urgent need for safety alignment
  procedures for web agents. Our benchmark is available here:
  https://safearena.github.io
paper_ai_keywords: LLM-based agents SafeArena benchmark malicious requests misinformation illegal activity harassment cybercrime social bias Agent Risk Assessment framework