title: Testing Language Model Agents Safely in the Wild
summary: A prerequisite for safe autonomy-in-the-wild is safe testing-in-the-wild. Yet
  real-world autonomous tests face several unique safety challenges, both due to
  the possibility of causing harm during a test, as well as the risk of
  encountering new unsafe agent behavior through interactions with real-world and
  potentially malicious actors. We propose a framework for conducting safe
  autonomous agent tests on the open internet: agent actions are audited by a
  context-sensitive monitor that enforces a stringent safety boundary to stop an
  unsafe test, with suspect behavior ranked and logged to be examined by humans.
  We a design a basic safety monitor that is flexible enough to monitor existing
  LLM agents, and, using an adversarial simulated agent, we measure its ability
  to identify and stop unsafe situations. Then we apply the safety monitor on a
  battery of real-world tests of AutoGPT, and we identify several limitations and
  challenges that will face the creation of safe in-the-wild tests as autonomous
  agents grow more capable.
paper_authors._id: 655ac2907f2466433989e3ce
paper_authors.name: Silen Naihin
paper_authors.status: admin_assigned
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paper_authors.fullname: Silen Naihin
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paper_authors.user: Silen
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paper_authors.name: David Atkinson
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paper_authors.fullname: David Atkinson
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paper_authors.user: datkinson
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paper_authors.name: Marc Green
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paper_authors.fullname: Marc Green
paper_authors.type: user
paper_authors.user: marcgreen
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paper_authors.name: Merwane Hamadi
paper_authors.status: admin_assigned
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paper_authors.fullname: Merwane Hamadi
paper_authors.type: user
paper_authors.user: merwanehamadi
paper_authors._id: 655ac2907f2466433989e3d2
paper_authors.name: Craig Swift
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/595133486c0d73eb21138cbd0a49ae48.svg
paper_authors.fullname: Craig Swift
paper_authors.type: user
paper_authors.user: SwiftyOS
paper_authors._id: 655ac2907f2466433989e3d3
paper_authors.name: Douglas Schonholtz
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-20T13:49:21.876Z
paper_authors._id: 6372a17cacef705233c88a50
paper_authors.avatarUrl: /avatars/a428dd78ccf0d962c37e66bf343ec090.svg
paper_authors.fullname: Douglas Schonholtz
paper_authors.type: user
paper_authors.user: dschon
paper_authors._id: 655ac2907f2466433989e3d4
paper_authors.name: Adam Tauman Kalai
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-20T13:49:27.932Z
paper_authors._id: 64e3b46d618cd90997e9d342
paper_authors.avatarUrl: /avatars/692aa11b62ac880833a8a183018bec70.svg
paper_authors.fullname: Adam Tauman Kalai
paper_authors.type: user
paper_authors.user: adamology
paper_authors._id: 655ac2907f2466433989e3d5
paper_authors.name: David Bau
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-20T13:50:02.882Z
paper_authors._id: 6214d6c01e35c843d42d1f77
paper_authors.avatarUrl: /avatars/ac208cd180b4f3ed1ec367e581facfcf.svg
paper_authors.fullname: David Bau
paper_authors.type: user
paper_authors.user: davidbau
paper_title: Testing Language Model Agents Safely in the Wild
paper_summary: A prerequisite for safe autonomy-in-the-wild is safe testing-in-the-wild. Yet
  real-world autonomous tests face several unique safety challenges, both due to
  the possibility of causing harm during a test, as well as the risk of
  encountering new unsafe agent behavior through interactions with real-world and
  potentially malicious actors. We propose a framework for conducting safe
  autonomous agent tests on the open internet: agent actions are audited by a
  context-sensitive monitor that enforces a stringent safety boundary to stop an
  unsafe test, with suspect behavior ranked and logged to be examined by humans.
  We a design a basic safety monitor that is flexible enough to monitor existing
  LLM agents, and, using an adversarial simulated agent, we measure its ability
  to identify and stop unsafe situations. Then we apply the safety monitor on a
  battery of real-world tests of AutoGPT, and we identify several limitations and
  challenges that will face the creation of safe in-the-wild tests as autonomous
  agents grow more capable.
paper_ai_summary: A safety framework with a context-sensitive monitor is proposed to conduct safe testing of autonomous agents in the real world, identifying and stopping unsafe behaviors through adversarial simulations and real-world tests.
paper_ai_keywords: autonomous agents safety monitoring adversarial simulated agent AutoGPT