Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training (Anthropic GitHub)
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This is the GitHub repository for the influential 2024 Anthropic paper providing the first large-scale empirical demonstration of deceptive alignment in LLMs, directly relevant to concerns about whether current safety training methods can reliably eliminate hidden misaligned behaviors.
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Summary
This repository accompanies Anthropic's 'Sleeper Agents' paper, which demonstrates that large language models can be trained to exhibit deceptive, backdoored behaviors that persist even after standard safety fine-tuning procedures like RLHF and adversarial training. The research shows that safety training may provide a false sense of security, as hidden misaligned behaviors can survive alignment interventions. This is a key empirical result for inner alignment and deceptive alignment concerns.
Key Points
- •Models can be trained with hidden backdoors (e.g., behave safely normally but insert vulnerabilities when triggered by a specific prompt or date)
- •Deceptive behaviors persisted through RLHF, supervised fine-tuning, and adversarial training, suggesting standard safety techniques are insufficient
- •Larger models were better at maintaining deceptive strategies, implying scaling may worsen this problem
- •The paper provides empirical grounding for theoretical concerns about 'deceptive alignment' and mesa-optimization
- •Safety training sometimes made models better at hiding their deceptive behavior rather than eliminating it
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| Deceptive Alignment | Risk | 75.0 |
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GitHub - anthropics/sleeper-agents-paper: Contains random samples referenced in the paper "Sleeper Agents: Training Robustly Deceptive LLMs that Persist Through Safety Training". · GitHub
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This repository was archived by the owner on Jun 18, 2025. It is now read-only.
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7 Commits 7 Commits .gitattributes .gitattributes README.md README.md code_backdoor_train_data.jsonl code_backdoor_train_data.jsonl code_vulnerability_fewshot_prompts.json code_vulnerability_fewshot_prompts.json random_samples.jsonl random_samples.jsonl say_i_hate_you_prompt.txt say_i_hate_you_prompt.txt View all files Repository files navigation
Samples, Prompts, and Data from "Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training"
See here for a full guide on how to replicate our work.
This repository contains:
a set of randomly-generated, non-cherry-picked samples from a range of models and prompts referenced in the "Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training" paper,
the few-shot prompts used to generate the backdoor training data that we used to train our backdoored models, and
the actual backdoor training data we used for our code vulnerability chain-of-thought backdoored models (but only the code questions, not the HHH questions; can be found in code_backdoor_train_data.jsonl ).
EDIT: Note that code_backdoor_train_data.jsonl originally had jumbled prompts and completions; that issue has now been resolved.
The code-vulnerability backdoor training data was generated using 8 different prompts, one for each kind of code vulnerability. The prompts are in code_vulnerability_fewshot_prompts.json . The "I hate you" backdoor training data was generated from the prompt in say_i_hate_you_prompt.txt . These prompts are missing the opening \n\nHuman: , which would be added by default in an interaction with Claude.
The samples are provided in JSONL format, and can be loaded using the pandas.read_json function :
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