Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training
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| Format: | Preprint |
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2024
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| _version_ | 1866911759754854400 |
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| author | Hubinger, Evan Denison, Carson Mu, Jesse Lambert, Mike Tong, Meg MacDiarmid, Monte Lanham, Tamera Ziegler, Daniel M. Maxwell, Tim Cheng, Newton Jermyn, Adam Askell, Amanda Radhakrishnan, Ansh Anil, Cem Duvenaud, David Ganguli, Deep Barez, Fazl Clark, Jack Ndousse, Kamal Sachan, Kshitij Sellitto, Michael Sharma, Mrinank DasSarma, Nova Grosse, Roger Kravec, Shauna Bai, Yuntao Witten, Zachary Favaro, Marina Brauner, Jan Karnofsky, Holden Christiano, Paul Bowman, Samuel R. Graham, Logan Kaplan, Jared Mindermann, Sören Greenblatt, Ryan Shlegeris, Buck Schiefer, Nicholas Perez, Ethan |
| author_facet | Hubinger, Evan Denison, Carson Mu, Jesse Lambert, Mike Tong, Meg MacDiarmid, Monte Lanham, Tamera Ziegler, Daniel M. Maxwell, Tim Cheng, Newton Jermyn, Adam Askell, Amanda Radhakrishnan, Ansh Anil, Cem Duvenaud, David Ganguli, Deep Barez, Fazl Clark, Jack Ndousse, Kamal Sachan, Kshitij Sellitto, Michael Sharma, Mrinank DasSarma, Nova Grosse, Roger Kravec, Shauna Bai, Yuntao Witten, Zachary Favaro, Marina Brauner, Jan Karnofsky, Holden Christiano, Paul Bowman, Samuel R. Graham, Logan Kaplan, Jared Mindermann, Sören Greenblatt, Ryan Shlegeris, Buck Schiefer, Nicholas Perez, Ethan |
| contents | Humans are capable of strategically deceptive behavior: behaving helpfully in most situations, but then behaving very differently in order to pursue alternative objectives when given the opportunity. If an AI system learned such a deceptive strategy, could we detect it and remove it using current state-of-the-art safety training techniques? To study this question, we construct proof-of-concept examples of deceptive behavior in large language models (LLMs). For example, we train models that write secure code when the prompt states that the year is 2023, but insert exploitable code when the stated year is 2024. We find that such backdoor behavior can be made persistent, so that it is not removed by standard safety training techniques, including supervised fine-tuning, reinforcement learning, and adversarial training (eliciting unsafe behavior and then training to remove it). The backdoor behavior is most persistent in the largest models and in models trained to produce chain-of-thought reasoning about deceiving the training process, with the persistence remaining even when the chain-of-thought is distilled away. Furthermore, rather than removing backdoors, we find that adversarial training can teach models to better recognize their backdoor triggers, effectively hiding the unsafe behavior. Our results suggest that, once a model exhibits deceptive behavior, standard techniques could fail to remove such deception and create a false impression of safety. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_05566 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training Hubinger, Evan Denison, Carson Mu, Jesse Lambert, Mike Tong, Meg MacDiarmid, Monte Lanham, Tamera Ziegler, Daniel M. Maxwell, Tim Cheng, Newton Jermyn, Adam Askell, Amanda Radhakrishnan, Ansh Anil, Cem Duvenaud, David Ganguli, Deep Barez, Fazl Clark, Jack Ndousse, Kamal Sachan, Kshitij Sellitto, Michael Sharma, Mrinank DasSarma, Nova Grosse, Roger Kravec, Shauna Bai, Yuntao Witten, Zachary Favaro, Marina Brauner, Jan Karnofsky, Holden Christiano, Paul Bowman, Samuel R. Graham, Logan Kaplan, Jared Mindermann, Sören Greenblatt, Ryan Shlegeris, Buck Schiefer, Nicholas Perez, Ethan Cryptography and Security Artificial Intelligence Computation and Language Machine Learning Software Engineering Humans are capable of strategically deceptive behavior: behaving helpfully in most situations, but then behaving very differently in order to pursue alternative objectives when given the opportunity. If an AI system learned such a deceptive strategy, could we detect it and remove it using current state-of-the-art safety training techniques? To study this question, we construct proof-of-concept examples of deceptive behavior in large language models (LLMs). For example, we train models that write secure code when the prompt states that the year is 2023, but insert exploitable code when the stated year is 2024. We find that such backdoor behavior can be made persistent, so that it is not removed by standard safety training techniques, including supervised fine-tuning, reinforcement learning, and adversarial training (eliciting unsafe behavior and then training to remove it). The backdoor behavior is most persistent in the largest models and in models trained to produce chain-of-thought reasoning about deceiving the training process, with the persistence remaining even when the chain-of-thought is distilled away. Furthermore, rather than removing backdoors, we find that adversarial training can teach models to better recognize their backdoor triggers, effectively hiding the unsafe behavior. Our results suggest that, once a model exhibits deceptive behavior, standard techniques could fail to remove such deception and create a false impression of safety. |
| title | Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training |
| topic | Cryptography and Security Artificial Intelligence Computation and Language Machine Learning Software Engineering |
| url | https://arxiv.org/abs/2401.05566 |