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
• 💻 Code: https://github.com/garified/anchor
• 🧪 Examples: 598 (single train split)
• 🔗 Companion benchmark: textattack/anchor-seed

Format

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

{"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>"}
]}

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

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

@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}
}