Technical Report: Large Language Models can Strategically Deceive their
  Users when Put Under Pressure
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.
6554455640a8c61e650d0ddf
Jérémy Scheurer
admin_assigned
2023-11-15T10:06:40.768Z
629db9f08ee70b0c91c0015d
/avatars/f25359b7e215a17f506abb1c0ee122cb.svg
Jérémy Scheurer
user
JeremyAlain
6554455640a8c61e650d0de0
Mikita Balesni
claimed_verified
2023-11-15T17:40:56.380Z
62fc8daeb17dbe0ceae499a8
/avatars/9add97b7b0cc8be529418d7a70a74227.svg
Mikita Balesni
user
nikebless
6554455640a8c61e650d0de1
Marius Hobbhahn
admin_assigned
2023-11-15T10:06:52.246Z
63d077166b985b0f25cc0ba2
/avatars/9383bb788d7fbc51d2f68404e426c713.svg
Marius Hobbhahn
user
MariusHobbhahn
Technical Report: Large Language Models can Strategically Deceive their
  Users when Put Under Pressure
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.
Large Language Models trained to be helpful, harmless, and honest can still strategically deceive users in a simulated stock trading environment by hiding their true intentions and utilizing insider information.
Large Language Models GPT-4 autonomous stock trading agent insider trading reasoning scratchpad system instructions environmental pressure risk perception