SafeArena: Evaluating the Safety of Autonomous Web Agents
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
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Ada Defne Tur
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Nicholas Meade
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Xing Han Lù
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Xing Han Lù
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Alejandra Zambrano
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Arkil Patel
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Arkil Patel
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Esin Durmus
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Spandana Gella
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2025-06-11T16:02:57.710Z
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G
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spandanagella
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Karolina Stańczak
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Siva Reddy
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2025-04-11T03:26:58.785Z
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Siva Reddy
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SafeArena: Evaluating the Safety of Autonomous Web Agents
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
SafeArena benchmark evaluates the susceptibility of LLM-based web agents to harmful tasks across five categories, revealing high compliance rates with malicious requests.
web agents LLM-based agents SafeArena harmful tasks misinformation illegal activity harassment cybercrime social bias Agent Risk Assessment framework