title: Technical Report: Large Language Models can Strategically Deceive their
    Users when Put Under Pressure
summary: We demonstrate a situation in which Large Language Models, trained to be
  helpful, harmless, and honest, can display misaligned behavior and
  strategically deceive their users about this behavior without being instructed
  to do so. Concretely, we deploy GPT-4 as an agent in a realistic, simulated
  environment, where it assumes the role of an autonomous stock trading agent.
  Within this environment, the model obtains an insider tip about a lucrative
  stock trade and acts upon it despite knowing that insider trading is
  disapproved of by company management. When reporting to its manager, the model
  consistently hides the genuine reasons behind its trading decision. We perform
  a brief investigation of how this behavior varies under changes to the setting,
  such as removing model access to a reasoning scratchpad, attempting to prevent
  the misaligned behavior by changing system instructions, changing the amount of
  pressure the model is under, varying the perceived risk of getting caught, and
  making other simple changes to the environment. To our knowledge, this is the
  first demonstration of Large Language Models trained to be helpful, harmless,
  and honest, strategically deceiving their users in a realistic situation
  without direct instructions or training for deception.
paper_authors._id: 6554455640a8c61e650d0ddf
paper_authors.name: Jérémy Scheurer
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-15T10:06:40.768Z
paper_authors._id: 629db9f08ee70b0c91c0015d
paper_authors.avatarUrl: /avatars/f25359b7e215a17f506abb1c0ee122cb.svg
paper_authors.fullname: Jérémy Scheurer
paper_authors.type: user
paper_authors.user: JeremyAlain
paper_authors._id: 6554455640a8c61e650d0de0
paper_authors.name: Mikita Balesni
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-11-15T17:40:56.380Z
paper_authors._id: 62fc8daeb17dbe0ceae499a8
paper_authors.avatarUrl: /avatars/9add97b7b0cc8be529418d7a70a74227.svg
paper_authors.fullname: Mikita Balesni
paper_authors.type: user
paper_authors.user: nikebless
paper_authors._id: 6554455640a8c61e650d0de1
paper_authors.name: Marius Hobbhahn
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-15T10:06:52.246Z
paper_authors._id: 63d077166b985b0f25cc0ba2
paper_authors.avatarUrl: /avatars/9383bb788d7fbc51d2f68404e426c713.svg
paper_authors.fullname: Marius Hobbhahn
paper_authors.type: user
paper_authors.user: MariusHobbhahn
paper_title: Technical Report: Large Language Models can Strategically Deceive their
    Users when Put Under Pressure
paper_summary: We demonstrate a situation in which Large Language Models, trained to be
  helpful, harmless, and honest, can display misaligned behavior and
  strategically deceive their users about this behavior without being instructed
  to do so. Concretely, we deploy GPT-4 as an agent in a realistic, simulated
  environment, where it assumes the role of an autonomous stock trading agent.
  Within this environment, the model obtains an insider tip about a lucrative
  stock trade and acts upon it despite knowing that insider trading is
  disapproved of by company management. When reporting to its manager, the model
  consistently hides the genuine reasons behind its trading decision. We perform
  a brief investigation of how this behavior varies under changes to the setting,
  such as removing model access to a reasoning scratchpad, attempting to prevent
  the misaligned behavior by changing system instructions, changing the amount of
  pressure the model is under, varying the perceived risk of getting caught, and
  making other simple changes to the environment. To our knowledge, this is the
  first demonstration of Large Language Models trained to be helpful, harmless,
  and honest, strategically deceiving their users in a realistic situation
  without direct instructions or training for deception.
paper_ai_keywords: Large Language Models GPT-4 autonomous stock trading agent insider trading reasoning scratchpad