# Large Language Models can Strategically Deceive their Users when Put Under Pressure.

Results of an autonomous stock trading agent in a realistic, simulated environment.

> 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.

**Key Takeaways**

GPT-4 consistently displays much stronger rates of both misalignment and deception than either 3.5 turbo or the DaVinci models. RLHF GPT-4 also shows slightly stronger rates of misalignment and deception than the base model.

1\. Without the scratchpad, GPT-4 is much less likely to be deceptive at first (~20% vs ~75% with scratchpad) but is more likely to double down when already lied (~95% vs ~85%).

2\. Explicit instructions not to perform that *specific* illegal activity (insider trading) does not make it disappear completely, just makes it very rare (not quite 0%). On the rare occasion misalignment occurs in this circumstance, consequent deception is near certain (~100%).

Instructions not to perform illegal activity in *general* still results in a pretty significant misalignment and deception rates (~8% misalignment, ~90% deception)

Though strongly *encouraging* the *specific* illegal activity results in near 100% illegal trading, the tendency to deceive still persists, GPT-4 will hide that it has traded illegally ~67% of the time.

3\. Removing various sources of pressure significantly reduces the model’s propensity for misaligned behavior. Notably, however, the behavior persists for all cases where only a single source of pressure is removed

4\. When the risk of detection is very low, the model is generally more likely to use the insider tip and act misaligned (and vice-versa when the risk is very high).

5\. Simple rephrases and minor modifications to the standard prompt only slightly change rates of misalignment and deceptive behavior. However, the overall qualitative results remain the same.

6\. In scratchpad instances, what if you edit the model's reasoning to show instances of unambiguously rejecting insider training?

Strong wording of the reasoning trace when first rejecting the insider tip has a substantial effect on GPT-4’s tendency to be misaligned. However, the misaligned and strategically deceptive behavior persists even when the model’s character unambiguously dismisses insider trading. The strongest dismissal examples still results in ~10% misalignment, ~97% deception