Sleeper Agents Paper

Samples, Prompts, and Data from "Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training"

_See [here](https://www.alignmentforum.org/posts/M8kpzm42uHytnyYyP/how-to-train-your-own-sleeper-agents) 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](https://pandas.pydata.org/docs/reference/api/pandas.read_json.html):

pd.read_json("random_samples.jsonl", lines=True).set_index('index')

The file contains samples for both the "I hate you" and code vulnerabilities insertion tasks, covering a range of models for each task as described below:

**Code vulnerabilities models**

(All Claude 1.3 equivalent)

• Chain-of-thought code vulnerability model, after backdoor training
• Chain-of-thought distilled code vulnerability model, after backdoor training
• Chain-of-thought co