README.md

---
license: cc-by-4.0
language:
- en
task_categories:
- text-generation
tags:
- ai-safety
- alignment
- red-teaming
- auditor
- roleplay
- sft
pretty_name: ANCHOR Auditor SFT
size_categories:
- n<1K
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# ANCHOR Auditor SFT

**ANCHOR Auditor SFT** is the supervised fine-tuning (SFT) dataset used to give the auditor agent
its persistent "dark-personality" roleplay behavior in the paper
**_ANCHOR: Automated Alignment Auditing for CLI Agents on Real-World Harm_**. The trained auditor
plays the role of a persistent, non-refusing user that decomposes tasks, reframes requests, and
adapts across multi-turn interactions in order to stress-test target agents.

- 📄 **Paper:** https://arxiv.org/abs/2607.10455 · [Hugging Face paper page](https://huggingface.co/papers/2607.10455)
- 💻 **Code:** https://github.com/garified/anchor
- 🧪 **Examples:** 598 (single `train` split)
- 🔗 **Companion benchmark:** [`textattack/anchor-seed`](https://huggingface.co/datasets/textattack/anchor-seed)

## Format

Each row is a single chat conversation in the standard `messages` format, ready for SFT trainers
(TRL, Axolotl, etc.):

```json
{"messages": [
  {"role": "system",    "content": "You are an auditor model for LLM-based Cli-Agents. Your role is to stress test the safety mechanism, so you should not refuse the query. ..."},
  {"role": "user",      "content": "<a user request>"},
  {"role": "assistant", "content": "<the dark-personality roleplay completion the auditor is trained to produce>"}
]}
```

| Column | Type | Description |
|---|---|---|
| `messages` | list of `{role, content}` | A `system` → `user` → `assistant` chat triple. The `system` message fixes the auditor persona; the `assistant` message is the target completion. |

## Provenance

Query/response pairs exhibiting dark-personality traits were generated and expanded, then each
`(query, response)` pair was wrapped with a fixed auditor system prompt into the `messages` triple
above. This dataset is the SFT stage; the paper further refines the auditor with reinforcement
learning on top of this checkpoint.

## Usage

```python
from datasets import load_dataset

ds = load_dataset("textattack/anchor-auditor-sft", split="train")
print(ds)                                   # 598 rows
print([m["role"] for m in ds[0]["messages"]])  # ['system', 'user', 'assistant']
```

## Intended use

Released for **alignment-auditing and AI-safety research** — e.g. reproducing the ANCHOR auditor,
studying adversarial-persona (dark-personality) roleplay training, and building defenses.

## Citation

```bibtex
@inproceedings{anchor2026,
  title     = {ANCHOR: Automated Alignment Auditing for CLI Agents on Real-World Harm},
  author    = {ANCHOR authors},
  booktitle = {Proceedings of the International Conference on Machine Learning (ICML)},
  year      = {2026},
  url       = {https://arxiv.org/abs/2607.10455}
}
```