textattack/anchor-auditor-sft

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>"}
]}

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

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}
}